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IEEE 2014 MATLAB PROJECTS ACADEMIC YEAR 2014-2015 FOR M.Tech/ B.E/B.Tech
VENTM14001 Regularized Simultaneous Forward–Backward Greedy Algorithm for Sparse Unmixing of
Hyperspectral Data
Abstract—Sparse unmixing assumes that each observed signature of a hyper spectral image is a linear
combination of only a few spectra (end members) in an available spectral library. It then estimates the
fractional abundances of these end members in the scene. The sparse un mixing problem still remains a
great difficulty due to the usually high correlation of the spectral library. Under such circumstances, this
paper presents a novel algorithm termed as the regularized simultaneous forward–backward greedy
algorithm (RSFoBa) for sparse un mixing of hyper spectral data. The RSFoBa has low computational
complexity of getting an approximate solution for the l0 problem directly and can exploit the joint
sparsity among all the pixels in the hyper spectral data. In addition, the combination of the forward
greedy step and the backward greedy step makes the RSFoBa more stable and less likely to be trapped
into the local optimum than the conventional greedy algorithms. Furthermore, when updating the
solution in each iteration, a regularizer that enforces the spatial-contextual coherence within the hyper
spectral image is considered to make the algorithm more effective. We also show that the sublibrary
obtained by the RSFoBa can serve as input for any other sparse unmixing algorithms to make them more
accurate and time efficient. Experimental results on both synthetic and real data demonstrate the
effectiveness of the proposed algorithm.
Published in: Geo science and Remote Sensing, IEEE Transactions on (Volume:52 , Issue: 9 )
Date of Publication: Sept. 2014
Index Terms—Dictionary pruning, greedy algorithm (GA), hyperspectral unmixing, multiple-measurement
vector (MMV), sparse unmixing.
VENTM14002 Mixed Noise Removal by Weighted Encoding with Sparse Nonlocal Regularization
Abstract: Mixed noise removal from natural images is a challenging task since the noise distribution
usually does not have a parametric model and has a heavy tail. One typical kind of mixed noise is
additive white Gaussian noise (AWGN) coupled with impulse noise (IN).
Many mixed noise removal methods are detection based methods. They first detect the locations of IN
pixels and then remove the mixed noise. However, such methods tend to generate many artifacts when
the mixed noise is strong. In this paper, we propose a simple yet effective method,
namely weighted encoding with sparsenonlocal regularization (WESNR), for mixed noise removal. In
WESNR, there is not an explicit step of impulse pixel detection; instead, soft impulse pixel detection
via weighted encoding is used to deal with IN and AWGN simultaneously. Meanwhile, the image sparsity
prior and nonlocal self-similarity prior are integrated into a regularization term and introduced into the
variational encoding framework. Experimental results show that the proposed WESNR method achieves
leading mixed noise removal performance in terms of both quantitative measures and visual quality.
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Published in:Image Processing, IEEE Transactions on (Volume:23 , Issue: 6 )
Date of Publication: June 2014
Index Terms—Mixed noise removal, weighted encoding, nonlocal, sparse representation.
VENTM14003 Subspace Matching Pursuit for Sparse Unmixing of Hyperspectral Data
Abstract : Sparse unmixing assumes that each mixed pixel in the hyperspectral image can be expressed
as a linear combination of only a few spectra (end members) in a spectral library, known a priori. It then
aims at estimating the fractional abundances of these endmembers in the scene. Unfortunately,
because of the usually high correlation of the spectral library, the sparse unmixing problem still remains
a great challenge. Moreover, most related work focuses on the l1 convex relaxation methods, and little
attention has been paid to the use of simultaneous sparse representation via greedy algorithms (GAs)
(SGA) for sparse unmixing. SGA has advantages such as that it can get an approximate solution for the
l0 problem directly without smoothing the penalty term in a low computational complexity as well as
exploit the spatial information of the hyperspectral data. Thus, it is necessary to explore the potential of
using such algorithms for sparse unmixing. Inspired by the existing SGA methods, this paper presents a
novel GA termed subspace matching pursuit (SMP) forsparse unmixing of hyperspectral data. SMP
makes use of the low-degree mixed pixels in thehyperspectral image to iteratively find a subspace to
reconstruct the hyperspectral data. It is proved that, under certain conditions, SMP can recover the
optimal endmembers from the spectral library. Moreover, SMP can serve as a dictionary pruning
algorithm. Thus, it can boost other sparseunmixing algorithms, making them more accurate and time
efficient. Experimental results on both synthetic and real data demonstrate the efficacy of the proposed
algorithm.
Published in: Geoscience and Remote Sensing, IEEE Transactions on (Volume:52 , Issue: 6 )
Date of Publication: June 2014
Index Terms—Dictionary pruning, greedy algorithm (GA), hyperspectral unmixing, multiple-measurement
vector (MMV), simultaneous sparse representation, sparse unmixing, subspace matching
pursuit (SMP).
VENTM14004 Sparse Unmixing of Hyperspectral Data Using Spectral a Priori Information
Abstract: Given a spectral library, sparse unmixing aims at finding the optimal subset of endmembers
from it to model each pixel in the hyperspectral scene. However, sparse unmixing still remains a
challenging task due to the usually high mutual coherence of the spectral library. In this paper, we
exploit the spectral a priori information in the hyperspectral image to alleviate this difficulty. It assumes
that some materials in the spectral library are known to exist in the scene. Such information can be
obtained via field investigation or hyperspectral data analysis. Then, we propose a novel model to
incorporate the spectral a priori information into sparse unmixing. Based on the alternating direction
method of multipliers, we present a new algorithm, which is
termed sparse unmixing using spectral apriori information (SUnSPI), to solve the model. Experimental
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results on both synthetic and real datademonstrate that the spectral a priori information is beneficial
to sparse unmixing and that SUnSPI can exploit this information effectively to improve the abundance
estimation.
Published in: Geoscience and Remote Sensing, IEEE Transactions on (Volume:53 , Issue: 2 )
Date of Publication: Feb. 2015
Index Terms—Hyperspectral unmixing, sparse unmixing, alternating direction method of multipliers
(ADMM), spectral a priori information.
VENTM14005 Gradient Histogram Estimation and Preservation for Texture Enhanced Image Denoising
Abstract: Natural image statistics plays an important role in image denoising, and various
natural image priors, including gradient-based, sparse representation-based, and nonlocal self-similarity-
based ones, have been widely studied and exploited for noise removal. In spite of the great
success of many denoising algorithms, they tend to smooth the fine scale image textures when
removing noise, degrading the image visual quality. To address this problem, in this paper, we propose
a texture enhanced image denoising method by enforcing the gradient histogram of the
denoised image to be close to a reference gradient histogram of the original image. Given the
reference gradient histogram, a novel gradient histogram preservation (GHP) algorithm is developed
to enhance the texture structures while removing noise. Two region-based variants of GHP are proposed
for the denoising of images consisting of regions with different textures. An algorithm is also developed
to effectively estimate the reference gradient histogram from the noisy observation of the
unknownimage. Our experimental results demonstrate that the proposed GHP algorithm can well
preserve thetexture appearance in the denoised images, making them look more natural.
Published in: Image Processing, IEEE Transactions on (Volume:23 , Issue: 6 )
Date of Publication: June 2014
Index Terms—Image denoising, histogram specification, nonlocal similarity, sparse representation.
VENTM14006 Image Set based Collaborative Representation for Face Recognition
Abstract: With the rapid development of digital imaging and communication technologies, image set-based
face recognition (ISFR) is becoming increasingly important. One key issue of ISFR is how to
effectively and efficiently represent the query face image set using the gallery face image sets. Theset-to-
set distance-based methods ignore the relationship between gallery sets, whereas representing the
query set images individually over the gallery sets ignores the correlation between query set images. In
this paper, we propose a novel image set-based collaborative representationand classification method
for ISFR. By modeling the query set as a convex or regularized hull, we represent this hull collaboratively
over all the gallery sets. With the resolved representationcoefficients, the distance between the
query set and each gallery set can then be calculated for classification. The proposed model naturally
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and effectively extends the image-based collaborative presentation to an image set based one, and our
extensive experiments on benchmark ISFR databases show the superiority of the proposed method to
state-of-the-art ISFR methods under different set sizes in terms of both recognition rate and efficiency.
Published in: Information Forensics and Security, IEEE Transactions on (Volume:9 , Issue: 7 )
Date of Publication: July 2014
Index Terms—image set, collaborative representation, set to sets distance, face recognition
VENTM14007 Fast Compressive Tracking
Abstract : It is a challenging task to develop effective and efficient appearance models for robust
object tracking due to factors such as pose variation, illumination change, occlusion, and motion blur.
Existing online tracking algorithms often update models with samples from observations in recent
frames. Despite much success has been demonstrated, numerous issues remain to be addressed. First,
while these adaptive appearance models are data-dependent, there does not exist sufficient amount of
data for online algorithms to learn at the outset. Second, online tracking algorithms often encounter the
drift problems. As a result of self-taught learning, misaligned samples are likely to be added and degrade
the appearance models. In this paper, we propose a simple yet effective and efficient tracking algorithm
with an appearance model based on features extracted from a multiscale image feature space with
data-independent basis. The proposed appearance model employs non-adaptive random projections
that preserve the structure of the image feature space of objects. A very sparse measurement matrix is
constructed to efficiently extract the features for the appearance model. We compress sample images
of the foreground target and the background using the same sparse measurement matrix. The tracking
task is formulated as a binary classification via a naive Bayes classifier with online update in the
compressed domain. A coarse-to-fine search strategy is adopted to further reduce the computational
complexity in the detection procedure. The proposed compressive tracking algorithm runs in real-time
and performs favorably against state-of-the-art methods on challenging sequences in terms of
efficiency, accuracy and robustness.
Published in: Pattern Analysis and Machine Intelligence, IEEE Transactions on (Volume:PP , Issue: 99 )
April 2014
Index Terms—Visual Tracking, Random Projection, Compressive Sensing, Compressed sensing, Feature
extraction, Image coding, Object tracking, Robustness, Sparse matrices, Target tracking
VENTM14008 Speech Intelligibility Prediction Based on Mutual Information
Abstract: This paper deals with the problem of predicting the average intelligibility of noisy and
potentially processed speech signals, as observed by a group of normal hearing listeners. We propose a
model which performs this prediction based on the hypothesis that intelligibility is monotonically
related to the mutual information between critical-band amplitude envelopes of the clean signal and the
corresponding noisy/processed signal. The resulting intelligibility predictor turns out to be a simple
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function of the mean-square error (mse) that arises when estimating a clean critical-band amplitude
using a minimum mean-square error (mmse) estimator based on the noisy/processed amplitude. The
proposed model predicts that speech intelligibility cannot be improved by any processing of noisy
critical-band amplitudes. Furthermore, the proposed intelligibility predictor performs well ( ρ > 0.95) in
predicting the intelligibility of speech signals contaminated by additive noise and potentially non-linearly
processed using time-frequency weighting.
Published in: Audio, Speech, and Language Processing, IEEE/ACM Transactions on (Volume:22 ,Issue: 2 )
Date of Publication: Feb. 2014
Index Terms— Instrumental measures, noise reduction, objective distortion measures, speech
enhancement, speech intelligibility prediction.
VENTM14009 Super-Resolution Compressed Sensing: An Iterative Reweighted Algorithm for Joint
Parameter Learning and Sparse Signal Recovery
Abstract : In many practical applications such as direction-of- arrival (DOA) estimation and line spectral
estimation, the sparsifying dictionary is usually characterized by a set of unknown parameters in a
continuous domain. To apply the conventional compressed sensing to such applications, the
continuous parameter space has to be discretized to a finite set of grid points. Discretization, however,
incurs errors and leads to deteriorated recovery performance. To address this issue, we propose
an iterative reweighted method which jointly estimates the unknown parameters and thesparse signals.
Specifically, the proposed algorithm is developed by iteratively decreasing a surrogate function
majorizing a given objective function, which results in a gradual and interweavediterative process to
refine the unknown parameters and the sparse signal. Numerical results show that
the algorithm provides superior performance in resolving closely-spaced frequency components.
Published in: Signal Processing Letters, IEEE (Volume:21 , Issue: 6 )
Date of Publication: June 2014
Index Terms—Compressed sensing, super-resolution, parameter learning, sparse signal recovery
VENTM14010 Variants of non-negative least-mean-square algorithm and convergence analysis
Abstract: Due to the inherent physical characteristics of systems under investigation, non-negativity is
one of the most interesting constraints that can usually be imposed on the parameters to estimate.
TheNon-Negative Least-Mean-Square algorithm (NNLMS) was proposed to adaptively find solutions of a
typical Wiener filtering problem but with the side constraint that the resulting weights need to be non-negative.
It has been shown to have good convergence properties. Nevertheless, certain practical
applications may benefit from the use of modified versions of this algorithm. In this paper, we derive
three variants of NNLMS. Each variant aims at improving the NNLMS performance regarding one of the
following aspects: sensitivity of input power, unbalance of convergence rates for different weights and
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computational cost. We study the stochastic behavior of the adaptive weights for these three
new algorithms for non-stationary environments. This study leads to analytical models to predict the
first and second order moment behaviors of the weights for Gaussian inputs. Simulation results are
presented to illustrate the performance of the new algorithms and the accuracy of the derived models.
Published in: Signal Processing, IEEE Transactions on (Volume:62 , Issue: 15 )
Date of Publication: Aug.1, 2014
Keywords: Adaptive signal processing, convergence analysis, exponential algorithm, least-mean-square
algorithms, non-negativity constraints, normalized algorithm, sign-sign algorithm.
VENTM14011 Training-Free Non-Intrusive Load Monitoring of Electric Vehicle Charging with Low
Sampling Rate
Abstract—Non-intrusive load monitoring (NILM) is an important topic in smart-grid and smart-home.
Many energy disaggregation algorithms have been proposed to detect various individual appliances
from one aggregated signal observation. However, few works studied the energy disaggregation of
plugin electric vehicle (EV) charging in the residential environment since EVs charging at home has
emerged only recently. Recent studies showed that EV charging has a large impact on smartgrid
especially in summer. Therefore, EV charging monitoring has become a more important and urgent
missing piece in energy disaggregation. In this paper, we present a novel method to disaggregate EV
charging signals from aggregated real power signals. The proposed method can effectively mitigate
interference coming from air-conditioner (AC), enabling accurate EV charging detection and energy
estimation under the presence of AC power signals. Besides, the proposed algorithm requires no
training, demands a light computational load, delivers high estimation accuracy, and works well for data
recorded at the low sampling rate 1/60 Hz. When the algorithm is tested on real-world data recorded
from 11 houses over about a whole year (total 125 months worth of data), the averaged error in
estimating energy consumption of EV charging is 15.7 kwh/month (while the true averaged energy
consumption of EV charging is 208.5 kwh/month), and the averaged normalized mean square error in
disaggregating EV charging load signals is 0.19.
Keywords—Non-intrusive load monitoring (NILM); Electric Vehicle (EV); Smart Grid; Energy
Disaggregation
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IEEE 2013 MATLAB PROJECTS ACADEMIC YEAR 2014-2015 FOR M.Tech/ B.E/B.Tech
VENTM13001 -Dimensional Wavelet Packet Spectrum for Texture Analysis
Abstract :This brief derives a 2-D spectrum estimator from some recent results on the statistical
properties ofwavelet packet coefficients of random processes. It provides an analysis of the bias of this
estimator with respect to the wavelet order. This brief also discusses the performance of this wavelet-based
estimator, in comparison with the conventional 2-D Fourier-based spectrum estimator
on textureanalysis and content-based image retrieval. It highlights the effectiveness of the wavelet-basedspectrum
estimation.
Published in: Image Processing, IEEE Transactions on (Volume:22 , Issue: 6 )
Date of Publication: June 2013
Keywords – 2-D Wavelet packet transforms; Random fields; Spectral analysis, Spectrum estimation,
Similarity measurements.
VENTM13002 Supervised and Unsupervised Speech Enhancement Using Nonnegative Matrix
Factorization
Abstract—Reducing the interference noise in a monaural noisy speech signal has been a challenging task
for many years. Compared to traditional unsupervised speech enhancement methods, e.g., Wiener
filtering, supervised approaches, such as algorithms based on hidden Markov models (HMM), lead to
higher-quality enhanced speech signals. However, the main practical difficulty of these approaches is
that for each noise type a model is required to be trained a priori. In this paper, we investigate a new
class of supervised speech denoising algorithms using nonnegative matrix factorization (NMF). We
propose a novel speech enhancement method that is based on a Bayesian formulation of NMF (BNMF).
To circumvent the mismatch problem between the training and testing stages, we propose two
solutions. First, we use an HMM in combination with BNMF (BNMF-HMM) to derive a minimum mean
square error (MMSE) estimator for the speech signal with no information about the underlying noise
type. Second, we suggest a scheme to learn the required noise BNMF model online, which is then used
to develop an unsupervised speech enhancement system. Extensive experiments are carried out to
investigate the performance of the proposed methods under different conditions. Moreover, we
compare the performance of the developed algorithms with state-of-the-art speech enhancement
schemes using various objective measures. Our simulations show that the proposed BNMF-based
methods outperform the competing algorithms substantially.
Published in: Audio, Speech, and Language Processing, IEEE Transactions on (Volume:21 , Issue: 10 )
Date of Publication: Oct. 2013
Index Terms—Nonnegative matrix factorization (NMF), speech enhancement, PLCA, HMM, Bayesian
Inference
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VENTM13003 Image Segmentation Using a Sparse Coding Model of Cortical Area V1
Abstract: Algorithms that encode images using a sparse set of basis functions have previously been
shown to explain aspects of the physiology of a primary visual cortex (V1), and have been used for
applications, such as image compression, restoration, and classification. Here, a sparse coding algorithm,
that has previously been used to account for the response properties of orientation tuned cells in
primary visual cortex, is applied to the task of perceptually salient boundary detection. The proposed
algorithm is currently limited to using only intensity information at a single scale. However, it is shown
to out-perform the current state-of-the-art image segmentation method (Pb) when this method is also
restricted to using the same information.
Published in: Image Processing, IEEE Transactions on (Volume:22 , Issue: 4 )
Index Terms— Image Segmentation; Edge detection; Neural Networks; Predictive Coding; Sparse
Coding; Primary Visual Cortex
VENTM13004 How to SAIF-ly Boost Denoising Performance
Abstract: Spatial domain image filters (e.g., bilateral filter, non-local means, locally adaptive regression
kernel) have achieved great success in de noising. Their overall performance, however, has not generally
surpassed the leading transform domain-based filters (such as BM3-D). One important reason is that
spatial domain filters lack efficiency to adaptively fine tune their de noising strength; something that is
relatively easy to do in transform domain method with shrinkage operators. In the pixel domain, the
smoothing strength is usually controlled globally by, for example, tuning a regularization parameter. In
this paper, we propose spatially adaptive iterative filtering (SAIF) a new strategy to control the de
noising strength locally for any spatial domain method. This approach is capable of filtering local image
content iteratively using the given base filter, and the type of iteration and the iteration number are
automatically optimized with respect to estimated risk (i.e., mean-squared error). In exploiting the
estimated local signal-to-noise-ratio, we also present a new risk estimator that is different from the
often-employed SURE method, and exceeds its performance in many cases. Experiments illustrate that
our strategy can significantly relax the base algorithm's sensitivity to its tuning (smoothing) parameters,
and effectively boost the performance of several existing de noising filters to generate state-of-the-art
results under both simulated and practical conditions.
Published in: Image Processing, IEEE Transactions on (Volume:22 , Issue: 4 )
Index Terms— Image de noising, spatial domain filter, risk estimator, SURE, pixel aggregation
VENTM13005 Nonlocally Centralized Sparse Representation for Image Restoration
Abstract: Sparse representation models code an image patch as a linear combination of a few atoms
chosen out from an over-complete dictionary, and they have shown promising results in
various image restoration applications. However, due to the degradation of the observed image (e.g.,
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noisy, blurred, and/or down-sampled), the sparse representations by conventional models may not be
accurate enough for a faithful reconstruction of the original image. To improve the performance
of sparse representation-based image restoration, in this paper the concept of sparse coding noise is
introduced, and the goal of image restoration turns to how to suppress the sparse coding noise. To this
end, we exploit the image nonlocal self-similarity to obtain good estimates of the sparse coding
coefficients of the original image, and then centralize the sparse coding coefficients of the
observed image to those estimates. The so-called non locally centralized sparse representation (NCSR)
model is as simple as the standard sparse representation model, while our extensive experiments on
various types of image restoration problems, including de noising, de blurring and super-resolution,
validate the generality and state-of-the-art performance of the proposed NCSR algorithm.
Published in: Image Processing, IEEE Transactions on (Volume:22 , Issue: 4 )
Date of Publication: April 2013
Index Terms— Image restoration, nonlocal similarity, sparse representation.
VENTM13006 Sparse Representation Based Image Interpolation With Nonlocal Autoregressive
Modeling
Abstract: Sparse representation is proven to be a promising approach to image super-resolution, where
the low-resolution (LR) image is usually modeled as the down-sampled version of its high-resolution (HR)
counterpart after blurring. When the blurring kernel is the Dirac delta function, i.e., the LR image is
directly down-sampled from its HR counterpart without blurring, the super-resolution problem becomes
an image interpolation problem. In such cases, however, the conventional sparse representation
models (SRM) become less effective, because the data fidelity term fails to constrain the image local
structures. In natural images, fortunately, many nonlocal similar patches to a given patch could provide
nonlocal constraint to the local structure. In this paper, we incorporate the image nonlocal self-similarity
into SRM for image interpolation. More specifically, a nonlocal autoregressive model (NARM) is
proposed and taken as the data fidelity term in SRM. We show that the NARM-induced sampling matrix
is less coherent with the representation dictionary, and consequently makes SRM more effective for
image interpolation. Our extensive experimental results demonstrate that the proposed NARM-based
image interpolation method can effectively reconstruct the edge structures and suppress the
jaggy/ringing artifacts, achieving the best image interpolation results so far in terms of PSNR as well as
perceptual quality metrics such as SSIM and FSIM.
Published in: Image Processing, IEEE Transactions on (Volume:22 , Issue: 4 )
Date of Publication: April 2013
Index Terms—Image interpolation, nonlocal autoregressive model, sparse representation, super-resolution.
VENTM13007 Acceleration of the Shiftable Algorithm for Bilateral Filtering and Nonlocal
Means
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Abstract: A direct implementation of the bilateral filter requires O(σs2) operations per pixel, where
σs is the(effective) width of the spatial kernel. A fast implementation of the bilateral filter that
required O(1) operations per pixel with respect to σs was recently proposed. This was done by using
trigonometric functions for the range kernel of the bilateral filter, and by exploiting their so-called shift
ability property. In particular, a fast implementation of the Gaussian bilateral filter was realized by
approximating the Gaussian range kernel using raised cosines. Later, it was demonstrated that this idea
could be extended to a larger class of filters, including the popular non-local means filter. As already
observed, a flip side of this approach was that the run time depended on the width σr of the range
kernel. For an image with dynamic range [0,T], the run time scaled as O(T2/σr
2) with σr. This made it
difficult to implement narrow range kernels, particularly for images with large dynamic range. In this
paper, we discuss this problem, and propose some simple steps to accelerate the implementation, in
general, and for small σr in particular. We provide some experimental results to
demonstrate the acceleration that is achieved using these modifications.
Published in: Image Processing, IEEE Transactions on (Volume:22 , Issue: 4 )
Date of Publication: April 2013
Index Terms—Bilateral filter, non-local means, shiftability, constant-time algorithm, Gaussian kernel,
truncation, running maximum, max filter, recursive filter, O(1) complexity.
VENTM13008 Incremental Learning of 3D-DCT Compact Representations for Robust Visual Tracking
Abstract: Visual tracking usually requires an object appearance model that is robust to changing
illumination, pose, and other factors encountered in video. Many recent trackers utilize appearance
samples in previous frames to form the bases upon which the object appearance model is built. This
approach has the following limitations: 1) The bases are data driven, so they can be easily corrupted,
and 2) it is difficult to robustly update the bases in challenging situations. In this paper, we construct an
appearance model using the 3D discrete cosine transform (3D-DCT). The 3D-DCT is based on a
set of cosine basis functions which are determined by the dimensions of the 3D signal and thus
independent of the input video data. In addition, the 3D-DCT can generate a compact energy spectrum
whose high-frequency coefficients are sparse if the appearance samples are similar. By discarding these
high-frequency coefficients, we simultaneously obtain a compact 3D-DCT-based
object representation and a signal reconstruction-based similarity measure (reflecting the information
loss from signal reconstruction). To efficiently update the object representation, we propose
an incremental 3D-DCTalgorithm which decomposes the 3D-DCT into successive operations of the 2D
discrete cosine transform (2D-DCT) and 1D discrete cosine transform (1D-DCT) on the input video data.
As a result, the incremental 3D-DCT algorithm only needs to compute the 2D-DCT for newly added
frames as well as the 1D-DCT along the third dimension, which significantly reduces the computational
complexity. Based on this incremental 3D-DCT algorithm, we design a discriminative criterion to
evaluate the likelihood of a test sample belonging to the foreground object. We then embed the
discriminative criterion into a particle filtering framework for object state inference over time.
Experimental results demonstrate the effectiveness and robustness of the proposed tracker.
Published in: Pattern Analysis and Machine Intelligence, IEEE Transactions on (Volume:35 , Issue: 4 )
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Date of Publication: April 2013
Index Terms—Visual tracking, appearance model, compact representation, discrete cosine transform
(DCT), incremental learning, template matching.
VENTM13009 Visual Saliency Based on Scale-Space Analysis in the Frequency Domain
Abstract: We address the issue of visual saliency from three perspectives. First, we
consider saliency detection as a frequency domain analysis problem. Second, we achieve this by
employing the concept of non saliency. Third, we simultaneously consider the detection of salient
regions of different size. The paper proposes a new bottom-up paradigm for detecting visual saliency,
characterized by a scale-space analysis of the amplitude spectrum of natural images. We show
that the convolution of the image amplitude spectrum with a low-pass Gaussian kernel of an
appropriate scale is equivalent to an image saliency detector. The saliency map is obtained by
reconstructing the 2D signal using the original phase and the amplitude spectrum, filtered at
a scale selected by minimizing saliency map entropy. A Hypercomplex Fourier Transform
performs the analysis in the frequency domain. Using available databases, we demonstrate
experimentally that the proposed model can predict human fixation data. We also introduce a new
image database and use it to show that the saliency detector can highlight both small and large salient
regions, as well as inhibit repeated distractors in cluttered images. In addition, we show that it is able to
predict salient regions on which people focus their attention.
Published in: Pattern Analysis and Machine Intelligence, IEEE Transactions on (Volume:35 , Issue: 4 )
Date of Publication: April 2013
Index Terms—Visual attention, saliency, Hypercomplex Fourier Transform, eye-tracking, scale space
analysis.
VENTM13010 Demosaicking of Noisy Bayer-Sampled Color Images With Least-Squares Luma-Chroma
Demultiplexing and Noise Level Estimation
Abstract: This paper adapts the least-squares luma-chroma de multiplexing (LSLCD) de
mosaicking method to noisy Bayer color filter array (CFA) images. A model is presented for the noise in
white-balanced gamma-corrected CFA images. A method to estimate the noise level in each of the red,
green, and blue color channels is then developed. Based on the estimated noise parameters, one of a
finite set of configurations adapted to a particular level of noise is selected to de mosaic the noisy data.
The noise-adaptive de mosaicking scheme is called LSLCD with noise estimation (LSLCD-NE).
Experimental results demonstrate state-of-the-art performance over a wide
range of noise levels, with low computational complexity. Many results with several
algorithms, noise levels, and images are presented on our companion web site along with software to
allow reproduction of our results.
Published in: Image Processing, IEEE Transactions on (Volume:22 , Issue: 1 )
Date of Publication: Jan. 2013
Index Terms—color filter array, Bayer sampling, demosaicking, noise estimation, noise reduction, noise
model
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VENTM13011 Fuzzy C-Means Clustering With Local Information and Kernel Metric for Image
Segmentation
Abstract: In this paper, we present an improved fuzzy C-means (FCM)
algorithm for image segmentation by introducing a tradeoff weighted fuzzy factor and a kernel metric.
The tradeoff weighted fuzzy factor depends on the space distance of all neighboring pixels and their
gray-level difference simultaneously. By using this factor, the new algorithm can accurately estimate the
damping extent of neighboring pixels. In order to further enhance its robustness to noise and outliers,
we introduce a kernel distance measure to its objective function. The new algorithm adaptively
determines the kernel parameter by using a fast bandwidth selection rule based on the distance
variance of all data points in the collection. Furthermore, the tradeoff
weighted fuzzy factor and the kernel distance measure are both parameter free. Experimental results on
synthetic and real images show that the new algorithm is effective andefficient, and is relatively
independent of this type of noise.
Published in: Image Processing, IEEE Transactions on (Volume:22 , Issue: 2 )
Date of Publication: Feb. 2013
Index Terms—Fuzzy clustering, gray-level constraint, image segmentation, kernel metric, spatial
constraint.
VENTM13012 Re initialization-Free Level Set Evolution via Reaction Diffusion
Abstract: This paper presents a novel reaction-diffusion (RD) method for implicit active contours that is
completely free of the costly re initialization procedure in level set evolution (LSE). A diffusion term is
introduced into LSE, resulting in an RD-LSE equation, from which a piecewise constant solution can be
derived. In order to obtain a stable numerical solution from the RD-based LSE, we propose a two-step
splitting method to iteratively solve the RD-LSE equation, where we first iterate the LSE equation, then
solve the diffusion equation. The second step regularizes the level set function obtained in the first step
to ensure stability, and thus the complex and costly re initialization procedure is completely eliminated
from LSE. By successfully applying diffusion to LSE, the RD-LSE model is stable by means of the simple
finite difference method, which is very easy to implement. The proposed RD method can be generalized
to solve the LSE for both variational level set method and partial differential equation-based
level set method. The RD-LSE method shows very good performance on boundary anti leakage. The
extensive and promising experimental results on synthetic and real images validate the effectiveness of
the proposed RD-LSE approach.
Published in: Image Processing, IEEE Transactions on (Volume:22 , Issue: 1
Date of Publication: Jan. 2013
Index Terms—Active contours, image segmentation, level set, partial differential equation (PDE),
reaction-diffusion, variational method.
VENTM13013 Online Object Tracking With Sparse Prototypes
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Abstract: Online object tracking is a challenging problem as it entails learning an effective model to
account for appearance change caused by intrinsic and extrinsic factors. In this paper, we propose a
novel online object tracking algorithm with sparse prototypes, which exploits both classic principal
component analysis (PCA) algorithms with recent sparse representation schemes for learning effective
appearance models. We introduce l1 regularization into the PCA reconstruction, and develop a novel
algorithm to represent an object by sparse prototypes that account explicitly for data and noise.
For tracking, objects are represented by the sparse prototypes learned online with update. In order to
reduce tracking drift, we present a method that takes occlusion and motion blur into account rather
than simply includes image observations for model update. Both qualitative and quantitative
evaluations on challenging image sequences demonstrate that the proposed tracking algorithm
performs favorably against several state-of-the-art methods.
Published in: Image Processing, IEEE Transactions on (Volume:22 , Issue: 1 )
Date of Publication: Jan. 2013
Index Terms—Appearance model, _1 minimization, object tracking, principal component analysis (PCA),
sparse prototypes
VENTM13014 Reversible Data Hiding in Encrypted Images by Reserving Room Before Encryption
Abstract: Recently, more and more attention is paid
to reversible data hiding (RDH) in encrypted images, since it maintains the excellent property that the
original cover can be losslessly recovered after embedded data is extracted while protecting
the image content's confidentiality. All previous methods embed data by reversibly vacating room from
the encrypted images, which may be subject to some errors on data extraction
and/or image restoration. In this paper, we propose a novel method by reserving room before
encryption with a traditional RDH algorithm, and thus it is easy for the data hider to reversibly embed
data in the encrypted image. The proposed method can achieve real reversibility, that is, data extraction
and image recovery are free of any error. Experiments show that this novel method can embed more
than 10 times as large payloads for the same image quality as the previous methods, such as for
PSNR=40 dB.
Published in: Information Forensics and Security, IEEE Transactions on (Volume:8 , Issue: 3 )
Date of Publication: March 2013
Index Terms— Reversible data hiding, image encryption, privacy protection, histogram shift.
VENTM13015 Fast and Accurate Matrix Completion via Truncated Nuclear Norm Regularization
Abstract: Recovering a large matrix from a small subset of its entries is a challenging problem arising in
many real applications, such as image inpainting and recommender systems. Many existing approaches
formulate this problem as a general low-rank matrix approximation problem. Since the rank operator is
non convex and discontinuous, most of the recent theoretical studies use the nuclear norm as a convex
relaxation. One major limitation of the existing approaches based on nuclear norm minimization is that
all the singular values are simultaneously minimized, and thus the rank may not be well approximated in
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practice. In this paper, we propose to achieve a better approximation to the rank
of matrix by truncatednuclear norm, which is given by the nuclear norm subtracted by the sum of the
largest few singular values. In addition, we develop a novel matrix completion algorithm by minimizing
the Truncated Nuclear Norm. We further develop three efficient iterative procedures, TNNR-ADMM,
TNNR-APGL, and TNNR-ADMMAP, to solve the optimization problem. TNNR-ADMM utilizes the
alternating direction method of multipliers (ADMM), while TNNR-AGPL applies the accelerated proximal
gradient line search method (APGL) for the final optimization. For TNNR-ADMMAP, we make use of an
adaptive penalty according to a novel update rule for ADMM to achieve a faster convergence rate. Our
empirical study shows encouraging results of the proposed algorithms in comparison to the state-of-the-art
matrix completion algorithms on both synthetic and real visual datasets.
Published in: Pattern Analysis and Machine Intelligence, IEEE Transactions on (Volume:35 , Issue: 9 )
Date of Publication: Sept. 2013
Index Terms—Matrix completion, nuclear norm minimization, alternating direction method of
multipliers, accelerated proximal gradient Method
VENTM13016 A Saliency Detection Model Using Low-Level Features Based on Wavelet Transform
Abstract: Researchers have been taking advantage of visual attention in various image processing
applications such as image retargeting, video coding, etc. Recently, many saliency detection algorithms
have been proposed by extracting features in spatial or transform domains. In this paper, a
novel saliency detection model is introduced by utilizing low-level features obtained from
the wavelet transform domain. Firstly, wavelet transform is employed to create the multi-scale
feature maps which can represent different features from edge to texture. Then, we propose a
computational model for the saliency map from these features. The proposed model aims to modulate
local contrast at a location with its global saliency computed based on the likelihood of the features, and
the proposed model considers local center-surround differences and global contrast in the
final saliency map. Experimental evaluation depicts the promising results from the proposed model by
outperforming the relevant state of the artsaliency detection models.
Published in: Multimedia, IEEE Transactions on (Volume:15 , Issue: 1 )
Date of Publication: Jan. 2013
Index Terms—Feature map, saliency detection, saliency map, visual attention, wavelet transform.
VENTM13017 Robust Point Matching Revisited: A Concave Optimization Approach
Abstract- The well-known robust point matching (RPM) method uses deterministic annealing for
optimization, and it has two problems. First, it cannot guarantee the global optimality of the solution
and tends to align the centers of two point sets. Second, deformation needs to be reg- ularized to avoid
the generation of undesirable results. To address these problems, in this paper we show that the energy
function of RPM can be reduced to a concave function with very few non-rigid terms after eliminating
the transformation variables and applying linear transformation; we then propose to use concave
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optimization technique to minimize the resulting energy function. The proposed method scales well
with problem size, achieves the globally optimal solution, and does not need regularization for simple
transformations such as similarity transform. Experiments on synthetic and real data validate the
advantages of our method in comparison with state-of-the-art methods.
VENTM13018 Phase Noise in MIMO Systems: Bayesian Cram´er-Rao Bounds and Soft-Input
Estimation
Abstract: This paper addresses the problem of estimating time varying phase noise caused by
imperfect oscillators in multiple-input multiple-output (MIMO) systems. The estimation
problem is parameterized in detail and based on an equivalent signal model its dimensionality
is reduced to minimize the . New exact and
closed-form expressions for the Bayesian Cramér-Rao lower bounds (BCRLBs) and soft-input
maximum a posteriori (MAP) estimators for online, i.e., filtering, and offline, i.e., smoothing,
estimation of phase noise over the length of a frame are derived. Simulations demonstrate that
the proposed MAP estimators' mean-square error (MSE) performances are very close to the
derived BCRLBs at moderate-to-high signal-to-noise ratios. To reduce the overhead and
complexity associated with tracking the phase noise processes over the length of a frame, a
novel soft-input extended Kalman filter (EKF) and extended Kalman smoother (EKS) that use
soft statistics of the transmitted symbols given the current observations are proposed.
Numerical results indicate that by employing the proposed phase tracking approach, the bit-error
rate performance of a MIMO system affected by phase noise can be significantly
improved. In addition, simulation results indicate that the proposed phase noise estimation
scheme allows for application of higher order modulations and larger numbers of antennas in
MIMO systems that employ imperfect oscillators.
Published in: Signal Processing, IEEE Transactions on (Volume:61 , Issue: 10 )
Issue Date : May15, 2013
Index Terms—Multi-input multi-output (MIMO), Wiener phase noise, Bayesian Cram´er Rao lower
bound (BCRLB), maximum-a-posteriori (MAP), soft-decision extended Kalman filter (EKF), and extended
Kalman smoother (EKS).
VENTM13019 Multi scale Gossip for Efficient Decentralized Averaging in Wireless Packet
Networks
Abstract: This paper describes and analyzes a hierarchical algorithm called Multi
scale Gossip for solving the distributed average consensus problem in wireless sensor networks.
The algorithm proceeds by recursively partitioning a given network. Initially, nodes at the finest
scale gossip to compute local averages. Then, using multi-hop communication and geographic
routing to communicate between nodes that are not directly connected, these
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local averages are progressively fused up the hierarchy until the global average is computed.
We show that the proposed hierarchical scheme with k=Θ(loglogn) levels of hierarchy is
competitive with state-of-the-art randomized gossip algorithms in terms of message
complexity, achieving ε-accuracy with high probability after O(n loglogn log[1/(ε)] ) single-hop
messages. Key to our analysis is the way in which the network is recursively partitioned. We
find that the above scaling law is achieved when sub networks at scale j contain O(n(2/3)j) nodes;
then the message complexity at any individual scale is O(n log[1/ε]). Another important
consequence of the hierarchical construction is that the longest distance over which messages
are exchanged is O(n1/3) hops (at the highest scale), and most messages (at lower scales) travel
shorter distances. In networks that use link-level acknowledgements, this results in less
congestion and resource usage by reducing message retransmissions. Simulations illustrate that
the proposed scheme is more efficient than state-of-the-art randomized gossip algorithms
based on averaging along paths.
Published in: Signal Processing, IEEE Transactions on (Volume:61 , Issue: 9 )
Date of Publication: May1, 2013
VENTM13020 Compressed Sensing of EEG for Wireless Tele monitoring with Low Energy
Consumption and Inexpensive Hardware
Abstract: Tele monitoring of electroencephalogram (EEG) through wireless body-area networks
is an evolving direction in personalized medicine. Among various constraints in designing such a
system, three important constraints are energy consumption, data compression, and device
cost. Conventional data compression methodologies, although effective in data compression,
consumes significant energy and cannot reduce device cost. Compressed sensing (CS), as an
emerging data compression methodology, is promising in catering to these constraints.
However, EEG is non sparse in the time domain and also non sparse in transformed domains
(such as the wavelet domain). Therefore, it is extremely difficult for current CS algorithms to
recover EEG with the quality that satisfies the requirements of clinical
diagnosis and engineering applications. Recently, block sparse Bayesian learning (BSBL) was
proposed as a new method to the CS problem. This study introduces the technique to the tele
monitoring of EEG. Experimental results show that its recovery quality is better than state-of-the-
art CS algorithms, and sufficient for practical use. These results suggest that BSBL is very
promising for tele monitoring of EEG and other non sparse physiological signals.
Published in: Biomedical Engineering, IEEE Transactions on (Volume:60 , Issue: 1 )
Date of Publication: Jan. 2013
Index Terms—Telemonitoring, Healthcare, Wireless Body- Area Network (WBAN), Compressed
Sensing (CS), Block Sparse Bayesian Learning (BSBL), electroencephalogram (EEG)
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VENTM13021 Compressed Sensing for Energy-Efficient Wireless Tele monitoring of Non-
Invasive Fetal ECG via Block Sparse Bayesian Learning
Abstract: Fetal ECG (FECG) tele monitoring is an important branch in telemedicine. The
design of a tele monitoring system via a wireless body area network with
low energy consumption for ambulatory use is highly desirable. As an emerging
technique, compressed sensing (CS) shows great promise in compressing/reconstructing data
with low energy consumption. However, due to some specific characteristics of raw FECG
recordings such as non sparsity and strong noise contamination, current CS algorithms
generally fail in this application. This paper proposes to use the block sparse Bayesian
learning framework to compress/reconstruct non sparse raw FECG recordings. Experimental
results show that the framework can reconstruct the raw recordings with high quality.
Especially, the reconstruction does not destroy the interdependence relation among the
multichannel recordings. This ensures that the independent component analysis
decomposition of the reconstructed recordings has high fidelity. Furthermore, the framework
allows the use of a sparse binary sensing matrix with much fewer nonzero entries
to compress recordings. Particularly, each column of the matrix can contain only two nonzero
entries. This shows that the framework, compared to other algorithms such as current CS
algorithms and wavelet algorithms, can greatly reduce code execution in CPU in the data
compression stage.
Published in: Biomedical Engineering, IEEE Transactions on (Volume:60 , Issue: 2 )
Date of Publication: Feb. 2013
Index Terms—Fetal ECG (FECG), Tele monitoring, Telemedicine, Healthcare, Block Sparse
Bayesian Learning (BSBL), Compressed Sensing (CS), Independent Component Analysis (ICA)
IEEE 2012 MATLAB PROJECTS ACADEMIC YEAR 2014-2015 FOR M.Tech/ B.E/B.Tech
VENTM12001 Image Forgery Localization via Fine-Grained Analysis of CFA Artifacts
Abstract: In this paper, a forensic tool able to discriminate between original and forged regions in
an image captured by a digital camera is presented. We make the assumption that the image is acquired
using a Color Filter Array, and that tampering removes the artifacts due to the de mosaicking algorithm.
The proposed method is based on a new feature measuring the presence of de mosaicking artifacts at a
local level, and on a new statistical model allowing to derive the tampering probability of each 2 ×
2image block without requiring to know a priori the position of the forged region. Experimental results
on different cameras equipped with different de mosaicking algorithms demonstrate both the
validity of the theoretical model and the effectiveness of our scheme.
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Published in: Information Forensics and Security, IEEE Transactions on (Volume:7 , Issue: 5 )
Date of Publication: Oct. 2012
Index Terms—Image forensics, CFA artifacts, digital camera demosaicing, tampering probability map,
forgery localization.
VENTM12002 Bottom-Up Saliency Detection Model Based on Human Visual Sensitivity and Amplitude
Spectrum
Abstract: With the wide applications of saliency information in visual signal processing, many saliency
detection methods have been proposed. However, some key characteristics of the human visual system
(HVS) are still neglected in building these saliency detection models. In this paper,we propose a new
saliencydetection model based on the human visual sensitivity and the amplitude spectrum of
quaternion Fourier transform (QFT). We use the amplitude spectrum of QFT to represent the color,
intensity, and orientation distributions for image patches. The saliency value for each image patch is
calculated by not only the differences between the QFT amplitude spectrum of this patch and other
patches in the whole image, but also the visual impacts for these differences determined by the human
visual sensitivity. The experiment results show that the proposed saliency detection model outperforms
the state-of-the-art detection models. In addition, we apply our proposed model in the application of
image retargeting and achieve better performance over the conventional algorithms.
Published in: Multimedia, IEEE Transactions on (Volume:14 , Issue: 1 )
Date of Publication: Feb. 2012
Index Terms—Amplitude spectrum, Fourier transform, human visual sensitivity, saliency detection,
visual attention.
VENTM12003 Monogenic Binary Coding: An Efficient Local Feature Extraction Approach to Face
Recognition
Abstract: Local-feature-based face recognition (FR) methods, such as Gabor features encoded
by local binary pattern, could achieve state-of-the-art FR results in large-scale face databases such as
FERET and FRGC. However, the time and space complexity of Gabor transformation are too high for
many practical FR applications. In this paper, we propose a new
and efficient local feature extraction scheme, namely monogenic binary coding (MBC),
for face representation and recognition. Monogenic signal representation decomposes an original signal
into three complementary components: amplitude, orientation, and phase. We encode
the monogenic variation in each local region and monogenic feature in each pixel, and then calculate
the statistical features (e.g., histogram) of the extracted local features.
The local statistical features extracted from the complementary monogenic components (i.e.,
amplitude, orientation, and phase) are then fused for effective FR. It is shown that the proposed MBC
scheme has significantly lower time and space complexity than the Gabor-transformation-based local
feature methods. The extensive FR experiments on four large-scale databases demonstrated the
effectiveness of MBC, whose performance is competitive with and even better than state-of-the-artlocal-
feature-based FR methods.
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Published in: Information Forensics and Security, IEEE Transactions on (Volume:7 , Issue: 6 )
Biometrics Compendium, IEEE
Date of Publication: Dec. 2012
Index Terms—Face recognition, Gabor filtering, LBP, monogenic binary coding, monogenic signal
analysis.
VENTM12004 A Joint Time-Invariant Filtering Approach to the Linear Gaussian Relay Problem
Abstract :In this paper, the linear Gaussian relay problem is considered. Under the linear time-invariant
(LTI) model the rate maximization problem in the linear Gaussian relay channel is formulated in
the frequency domain based on the Toeplitz distribution theorem. Under the further assumption of
realizable input spectra, the rate maximization problem is converted to the problem of joint source
and relay filter design with two power constraints, one at the source and the other at the relay, and a
practical solution to this problem is proposed based on the (adaptive) projected (sub)gradient method.
Numerical results show that the proposed method yields a considerable gain over the instantaneous
amplify-and-forward (AF) scheme in inter-symbol interference (ISI) channels. Also, the optimality of the
AF scheme within the class of one-tap relay filters is established in flat-fading channels.
Published in: Signal Processing, IEEE Transactions on (Volume:60 , Issue: 8 )
Date of Publication: Aug. 2012
Index Terms—Filter design, linear Gaussian relay, linear time invariant model, projected subgradient
method, Toeplitz distribution theorem.
VENTM12005 Monotonic Regression: A New Way for Correlating Subjective and Objective Ratings in
Image Quality Research
Abstract: To assess the performance of image quality metrics (IQMs), some regressions, such as logistic
regression and polynomial regression, are used to correlate objective ratings with subjective scores.
However, some defects in optimality are shown in these regressions. In this correspondence,
monotonic regression (MR) is found to be an effective correlation method in the performance
assessment of IQMs. Both theoretical analysis and experimental results have proven that MR performs
better than any other regression. We believe that MR could be an effective tool for performance
assessment in the IQM research.
Published in: Image Processing, IEEE Transactions on (Volume:21 , Issue: 4 )
Date of Publication: April 2012
Index Terms—Image quality assessment, image quality metric (IQM), metric performance, monotonic
regression (MR).
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VENTM12006 An efficient leaf recognition algorithm for plant classification using support vector
machine
Abstract: Recognition of plants has become an active area of research as most of the plant species are at
the risk of extinction. This paper uses an efficient machine learning approach for
the classification purpose. This proposed approach consists of three phases such as preprocessing,
feature extraction and classification. The preprocessing phase involves a typical image processing steps
such as transforming to gray scale and boundary enhancement. The feature extraction phase derives the
common DMF from five fundamental features. The main contribution of this approach is
the SupportVector Machine (SVM) classification for efficient leaf recognition. 12 leaf features which are
extracted and orthogonalized into 5 principal variables are given as input vector to the SVM. Classifier
tested with flavia dataset and a real dataset and compared with k-NN approach, the proposed approach
produces very high accuracy and takes very less execution time.
Published in: Pattern Recognition, Informatics and Medical Engineering (PRIME), 2012 International
Conference on
Date of Conference: 21-23 March 2012
Keywords- Digital Morphological Features (DMFs); Leaf Recognition; Support Vector Machine
VENTM12007 Image Signature: Highlighting Sparse Salient Regions
Abstract—We introduce a simple image descriptor referred to as the image signature. We show, within
the theoretical framework of sparse signal mixing, that this quantity spatially approximates the
foreground of an image. We experimentally investigate whether this approximate foreground overlaps
with visually conspicuous image locations by developing a saliency algorithm based on the image
signature. This saliency algorithm predicts human fixation points best among competitors on the Bruce
and Tsotsos [1] benchmark data set and does so in much shorter running time. In a related experiment,
we demonstrate with a change blindness data set that the distance between images induced by the
image signature is closer to human perceptual distance than can be achieved using other saliency
algorithms, pixel-wise, or GIST [2] descriptor methods.
Published in: Pattern Analysis and Machine Intelligence, IEEE Transactions on (Volume:34 , Issue: 1 )
Date of Publication: Jan. 2012
Index Terms—Saliency, visual attention, change blindness, sign function, sparse signal analysis.
VENTM12008 An Efficient Algorithm for Level Set Method Preserving Distance Function
Abstract: The level set method is a popular technique for tracking moving interfaces in several
disciplines, including computer vision and fluid dynamics. However, despite its high flexibility, the
original level set method is limited by two important numerical issues. First, the level set method does
not implicitly preserve the level set function as a distance function, which is necessary to estimate
accurately geometric features, s.a. the curvature or the contour normal. Second,
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the level set algorithm is slow because the time step is limited by the standard Courant-Friedrichs-Lewy
(CFL) condition, which is also essential to the numerical stability of the iterative scheme. Recent
advances with graph cut methods and continuous convex relaxation methods provide powerful
alternatives to the level set method for image processing problems because they are fast, accurate, and
guaranteed to find the global minimizer independently to the initialization. These recent techniques use
binary functions to represent the contour rather than distance functions, which are usually considered
for the level set method. However, the binary function cannot provide the distance information, which
can be essential for some applications, s.a. the surface reconstruction problem from scattered points
and the cortex segmentation problem in medical imaging. In this paper, we propose a
fast algorithm to preserve distance functions inlevel set methods. Our algorithm is inspired by
recent efficient l1 optimization techniques, which will provide an efficient and easy to
implement algorithm. It is interesting to note that our algorithm is not limited by the CFL condition and
it naturally preserves the level set function as a distance function during the evolution, which avoids the
classical re-distancing problem in level set methods. We apply the proposed algorithm to carry out
image segmentation, where our methods prove to be 5-6 times faster than
standard distance preserving level set - techniques. We also present two applications where
preserving a distance function is essential. Nonetheless, our method stays generic and can be applied to
any level set methods that require the distance information.
Published in: Image Processing, IEEE Transactions on (Volume:21 , Issue: 12 )
Date of Publication: Dec. 2012
Index Terms: Image segmentation, level set, numerical scheme, signed distance function,splitting,
surface reconstruction
VENTM12009 Structure Extraction from Texture via Relative Total Variation
Abstract: It is ubiquitous that meaningful structures are formed by or appear over textured surfaces.
Extracting them under the complication of texture patterns, which could be regular, near-regular, or
irregular, is very challenging, but of great practical importance. We propose new inherent variation and
relative total variation measures, which capture the essential difference of these two types of visual
forms, and develop an efficient optimization system to extract main structures. The new variation
measures are validated on millions of sample patches. Our approach finds a number of new applications
to manipulate, render, and reuse the immense number of “structure with texture” images and drawings
that were traditionally difficult to be edited properly.
Keywords: texture, structure, smoothing, total variation, relative total variation, inherent variation,
prior, regularized optimization
2012
VENTM12010 Quick Detection of Brain Tumors and Edemas: A Bounding Box Method Using Symmetry
Abstract: A significant medical informatics task is indexing patient databases according to size, location,
and other characteristics of brain tumors and edemas, possibly based on magnetic resonance (MR)
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imagery. This requires segmenting tumors and edemas within images from different MR modalities. To
date, automated brain tumor or edema segmentation from MR modalities remains a challenging as well
as computationally intensive task. In this paper, we propose a novel automated, fast, and approximate
segmentation technique. The input is a patient study consisting of a set of MR slices. The output is a
corresponding set of the slices that circumscribe the tumors with axis-parallel bounding boxes. The
proposed approach is based on an unsupervised change detection method that searches for the most
dissimilar region (axis-parallel bounding boxes) between the left and the right halves of a brain in an
axial view MR slice. This change detection process uses a novel score function based on Bhattacharya
coefficient computed with gray level intensity histograms. We prove that this score function admits a
very fast (linear in image height and width) search to locate the bounding box. The average dice
coefficients for localizing brain tumors and edemas, over ten patient studies, are 0.57 and 0.52
respectively, which significantly exceeds the scores for two other competitive region-based bounding
box techniques.
Index Terms– MR image Segmentation, Bhattacharya coefficient, Brain Tumor, Edema.
VENTM12011 Efficient Misalignment-Robust Representation for Real-Time Face Recognition
Abstract: Sparse representation techniques for robust face recognition have been widely studied in the
past several years. Recently face recognition with simultaneous misalignment, occlusion and other
variations has achieved interesting results via robust alignment by sparse representation (RASR). In
RASR, the best alignment of a testing sample is sought subject by subject in the database. However, such
an exhaustive search strategy can make the time complexity of RASR prohibitive in large-scale face
databases. In this paper, we propose a novel scheme, namely misalignment robust representation
(MRR), by representing the misaligned testing sample in the transformed face space spanned by all
subjects. The MRR seeks the best alignment via a two-step optimization with a coarse-to-fine search
strategy, which needs only two deformation-recovery operations. Extensive experiments on
representative face databases show that MRR has almost the same accuracy as RASR in various face
recognition and verification tasks but it runs tens to hundreds of times faster than RASR. The running
time of MRR is less than 1 second in the large-scale Multi-PIE face database, demonstrating its great
potential for real-time face recognition.
VENTM12012 Multi-User Diversity vs. Accurate Channel State Information in MIMO Downlink
Channels
Abstract: In a multiple transmit antenna, single antenna per receiver downlink channel with limited
channel state feedback, we consider the following question: given a constraint on the total system-wide
feedback load, is it preferable to get low-rate/coarse channel feedback from a large number of receivers
or high-rate/high-quality feedback from a smaller number of receivers? Acquiring feedback from many
receivers allows multi-user diversity to be exploited, while high-rate feedback allows for very precise
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selection of beamforming directions. We show that there is a strong preference for obtaining high-quality
feedback, and that obtaining near-perfect channel information from as many receivers as
possible provides a significantly larger sum rate than collecting a few feedback bits from a large number
of users. In terms of system design, this corresponds to a preference for acquiring high-quality feedback
from a few users on each time-frequency resource block, as opposed to coarse feedback from many
users on each block.
Published in: Wireless Communications, IEEE Transactions on (Volume:11 , Issue: 9 )
Date of Publication: September 2012
Index Terms-MIMO downlink channels, MU-MIMO communication, multi-user diversity
VENTM12013 Joint Estimation of Channel and Oscillator Phase Noise in MIMO Systems
Abstract: Oscillator phase noise limits the performance of high speed communication systems since
it results in time varying channels and rotation of the signal constellation from symbol to
symbol. In this paper, jointestimation of channel gains and Wiener phase noise in multi-input multi-output
(MIMO) systems is analyzed. The signal model for the estimation problem is
outlined in detail and new expressions for the Cramér-Rao lower bounds (CRLBs) for the multi-parameter
estimation problem are derived. A data-aided least-squares (LS) estimator for jointly
obtaining the channel gains and phase noise parameters is derived. Next, a decision-directed
weighted least-squares (WLS) estimator is proposed, where pilots and estimated data symbols are
employed to track the time-varying phase noise parameters over a frame. In order to reduce the
overhead and delay associated with the estimation process, a new decision-directed extended Kalman
filter (EKF) is proposed for tracking the MIMO phase noise throughout a frame. Numerical results show
that the proposed LS, WLS, and EKF estimators' performances are close to the CRLB. Finally, simulation
results demonstrate that by employing the proposed channel and time-varying
phase noise estimators the bit-error rate performance of a MIMO system can be significantly
improved.
Published in: Signal Processing, IEEE Transactions on (Volume:60 , Issue: 9 )
Date of Publication: Sept. 2012
Index Terms—Channel estimation, Cramér-Rao lower bound (CRLB), extended Kalman filter (EKF), multi-input
multi-output (MIMO), weighted least squares (WLS), Wiener phase noise.
IEEE 2011 MATLAB PROJECTS ACADEMIC YEAR 2014-2015 FOR M.Tech/ B.E/B.Tech
VENTM11001 An Augmented Lagrangian Method for Total Variation Video Restoration
Abstract: This paper presents a fast algorithm for restoring video sequences. The proposed algorithm, as
opposed to existing methods, does not consider video restoration as a sequence of image restoration
problems. Rather, it treats a video sequence as a space-time volume and poses a space-time total
variation regularization to enhance the smoothness of the solution. The optimization problem is solved
by transforming the original unconstrained minimization problem to an equivalent constrained
minimization problem. An augmented Lagrangian method is used to handle the constraints, and an
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alternating direction method is used to iteratively find solutions to the subproblems. The proposed
algorithm has a wide range of applications, including video deblurring and denoising, video disparity
refinement, and hot-air turbulence effect reduction.
Published in: Image Processing, IEEE Transactions on (Volume:20 , Issue: 11 )
Date of Publication: Nov. 2011
Index Terms: Alternating direction method (ADM), augmented Lagrangian, hot-air turbulence,
total variation (TV), video deblurring, video disparity, video restoration
VENTM11002 On Optimal Power Control for Delay-Constrained Communication Over Fading Channels
Abstract: In this paper, the problem of optimal power control for delay-constrained communication
over fading channels is studied. The objective is to find a power control law that optimizes the link layer
performance, specifically, minimizes delay bound violation probability (or equivalently, the packet drop
probability), subject to constraints on average power, arrival rate and delay bound. The transmission
buffer size is assumed to be finite; hence, when the buffer is full, there will be packet drop. The fading
channel under study has a continuous state, e.g., Rayleigh fading. Since directly solving the power
control problem (which optimizes the link layer performance) is particularly challenging, the problem is
decomposed into three sub problems and the three sub problems are solved iteratively; the resulting
scheme is called joint queue length aware (JQLA) power control, which produces a local optimal solution
to the three sub problems. It is proved that the solution that simultaneously solves the three sub
problems is also an optimal solution to the optimal power control problem. Simulation results show that
the JQLA scheme achieves superior performance over the time domain water filling and the truncated
channel inversion power control.
Published in: Information Theory, IEEE Transactions on (Volume:57 , Issue: 6 )
Date of Publication: June 2011
Index Terms—Delay-constrained communication, power control, queuing analysis, delay bound
violation probability, packet drop probability.
VENTM11003 A Level Set Method for Image Segmentation in the Presence of Intensity
Inhomogeneities With Application to MRI
Abstract: Intensity in homogeneity often occurs in real-world images, which presents a considerable
challenge in image segmentation. The most widely used image segmentation algorithms are region-based
and typically rely on the homogeneity of the image intensities in the regions of interest, which
often fail to provide accurate segmentation results due to the intensity inhomogeneity. This paper
proposes a novel region-based method for image segmentation, which is able to deal
with intensity inhomogeneities in the segmentation. First, based on the model
of images with intensity inhomogeneities, we derive a local intensity clustering property of
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the image intensities, and define a local clustering criterion function for the image intensities in a
neighborhood of each point. This local clustering criterion function is then integrated with respect to the
neighborhood center to give a global criterion of image segmentation. In alevel set formulation, this
criterion defines an energy in terms of the level set functions that represent a partition of
the image domain and a bias field that accounts for the intensity inhomogeneity of the image.
Therefore, by minimizing this energy, our method is able to simultaneously segment the image and
estimate the bias field, and the estimated bias field can be used for intensity inhomogeneity correction
(or bias correction). Our method has been validated on synthetic images and real images of various
modalities, with desirable performance in the presence of intensity inhomogeneities. Experiments show
that our method is more robust to initialization, faster and more accurate than the well-known
piecewise smooth model. As an application, our method has been used for segmentation and bias
correction of magnetic resonance (MR) images with promising results.
Published in: Image Processing, IEEE Transactions on (Volume:20 , Issue: 7 )
Date of Publication: July 2011
Index Terms: Bias correction, MRI, image segmentation, intensity inhomogeneity, level set
VENTM11004 Hybrid DE algorithm with adaptive crossover operator for solving real-world numerical
optimization problems
Abstract—In this paper, the results for the CEC 2011 Competition on testing evolutionary algorithms on
real world optimization problems using a hybrid differential evolution algorithm are presented. The
proposal uses a local search routine to improve convergence and an adaptive crossover operator.
According to the obtained results, this algorithm shows to be able to find competitive solutions with
reported results.
Index Terms—Differential Evolution algorithm, parameter selection, CEC competition.
Published in: Evolutionary Computation (CEC), 2011 IEEE Congress on June 2011
VENTM11005 An Improved Algorithm for Blind Reverberation Time Estimation
Abstract—An improved algorithm for the estimation of the reverberation time (RT) from reverberant
speech signals is presented. This blind estimation of the RT is based on a simple statistical model for the
sound decay such that the RT can be estimated by means of a maximum-likelihood (ML) estimator. The
proposed algorithm has a significantly lower computational complexity than previous ML-based
algorithms for RT estimation. This is achieved by a downsampling operation and a simple pre-selection
of possible sound decays. The new algorithm is more suitable to track time-varying RTs than related
approaches. In addition, it can also estimate the RT in the presence of (moderate) background noise.
The proposed algorithm can be employed to measure the RT of rooms from sound recordings without
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using a dedicated measurement setup. Another possible application is its use within speech
dereverberation systems for hands-free devices or digital hearing aids.
Index Terms—reverberation time, blind estimation, low complexity, speech dereverberation
IEEE 2010 MATLAB PROJECTS ACADEMIC YEAR 2014-2015 FOR M.Tech/ B.E/B.Tech
VENTM10001 Distance Regularized Level Set Evolution and Its Application to Image Segmentation
Abstract—Level set methods have been widely used in image processing and computer vision. In
conventional level set formulations, the level set function typically develops irregularities during its
evolution, which may cause numerical errors and eventually destroy the stability of the evolution.
Therefore, a numerical remedy, called reinitialization, is typically applied to periodically replace the
degraded level set function with a signed distance function. However, the practice of reinitialization not
only raises serious problems as when and how it should be performed, but also affects numerical
accuracy in an undesirable way. This paper proposes a new variational level set formulation in which the
regularity of the level set function is intrinsically maintained during the level set evolution. The level set
evolution is derived as the gradient flow that minimizes an energy functional with a distance
regularization term and an external energy that drives the motion of the zero level set toward desired
locations. The distance regularization term is defined with a potential function such that the derived
level set evolution has a unique forward-and-backward (FAB) diffusion effect, which is able to maintain a
desired shape of the level set function, particularly a signed distance profile near the zero level set. This
yields a new type of level set evolution called distance regularized level set evolution (DRLSE). The
distance regularization effect eliminates the need for reinitialization and thereby avoids its induced
numerical errors. In contrast to complicated implementations of conventional level set formulations, a
simpler and more efficient finite difference scheme can be used to implement the DRLSE formulation.
DRLSE also allows the use of more general and efficient initialization of the level set function. In its
numerical implementation, relatively large time steps can be used in the finite difference scheme to
reduce the number of iterations, while ensuring sufficient numerical accuracy. To demonstrate the
effectiveness of the DRLSE formulation, we apply it to an edge-based active contour model for image
segmentation, and provide a simple narrowband implementation to greatly reduce computational cost.
Published in: IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 19, NO. 12, DECEMBER 2010
Index Terms—Forward and backward diffusion, image segmentation, level set method, narrowband,
reinitialization.
VENTM10002 Demonstration of Real-Time Spectrum Sensing for Cognitive Radio
Abstract: The requirement for real-time processing indeed poses challenges on
implementing spectrum sensingalgorithms. Trade-off between the complexity and the effectiveness
of spectrum sensing algorithms should be taken into consideration. In this paper, a fast Fourier
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transform based spectrum sensingalgorithm, whose decision variable is independent of noise level, is
introduced. A small form factor software defined radio development platform is employed to implement
a spectrum sensing receiver with the proposed algorithm. To our best knowledge, it is the
first time that real-time spectrum sensingon hardware platform with controllable primary user devices is
demonstrated.
Published in: Communications Letters, IEEE (Volume:14 , Issue: 10 )
Date of Publication: October 2010
Index Terms: Cognitive radio, demonstration, real-time, spectrum sensing
VENTM10003 Retinal Vessel Extraction by Matched Filter with First-Order Derivative of Gaussian
Abstract: Accurate extraction of retinal blood vessels is an important task in computer aided diagnosis
of retinopathy. The Matched Filter (MF) is a simple yet effective method for vessel extraction. However,
a MF will respond not only to vessels but also to non-vessel edges. This will lead to frequent false vessel
detection. In this paper we propose a novel extension of the MF approach, namely the MF-FDOG, to
detect retinal blood vessels. The proposed MF-FDOG is composed of the original MF, which is a zero-mean
Gaussian function, and the first-order derivative of Gaussian (FDOG). The vessels are detected by
thresholding the retinal image’s response to the MF, while the threshold is adjusted by the image’s
response to the FDOG. The proposed MF-FDOG method is very simple; however, it reduces significantly
the false detections produced by the original MF and detects many fine vessels that are missed by the
MF. It achieves competitive vessel detection results as compared with those state-of-the-art schemes
but with much lower complexity. In addition, it performs well at extracting vessels from pathological
retinal images.
Keywords: retinal image segmentation; vessel detection; matched filter; line detection
2010
VENTM10004 Accurate Computation of the MGF of the Lognormal Distribution and its Application to
Sum of Lognormals
Abstract: Sums of lognormal random variables (RVs) are of wide interest in wireless
communications and other areas of science and engineering. Since the distribution of
lognormal sums is not log-normal and does not have a closed-form analytical expression, many
approximations and bounds have been developed. This paper develops two computational
methods for the moment generating function (MGF) or the characteristic function (CHF) of a
single lognormal RV. The first method uses classical complex integration techniques based on
steepest-descent integration. The saddle point of the integrand is explicitly expressed by the
Lambert function. The steepest-descent (optimal) contour and two closely-related closed-form
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contours are derived. A simple integration rule (e.g., the midpoint rule) along any of these
contours computes the MGF/CHF with high accuracy. The second approach uses a variation on
the trapezoidal rule due to Ooura and Mori. Importantly, the cumulative distribution function
of lognormalsums is derived as an alternating series and convergence acceleration via the
Epsilon algorithm is used to reduce, in some cases, the computational load by a factor of 106!
Overall, accuracy levels of 13 to 15 significant digits are readily achievable.
Published in: Communications, IEEE Transactions on (Volume:58 , Issue: 5 )
Date of Publication: May 2010
Index Terms—Sum of lognormals, moment-generating function, characteristic function.
IEEE <2009 MATLAB PROJECTS ACADEMIC YEAR 2014-2015 FOR M.Tech/ B.E/B.Tech
VENTMXX001 Canny Edge Detection Enhancement by Scale Multiplication
The technique of scale multiplication is analyzed in the framework of Canny edge detection. A scale
multiplication function is defined as the product of the responses of the detection filter at two scales.
Edge maps are constructed as the local maxima by thresholding the scale multiplication results. The
detection and localization criteria of the scale multiplication are derived. At a small loss in the detection
criterion, the localization criterion can be much improved by scale multiplication. The product of the two
criteria for scale multiplication is greater than that for a single scale, which leads to
better edgedetection performance. Experimental results are presented.
Published in: Pattern Analysis and Machine Intelligence, IEEE Transactions on (Volume:27 , Issue: 9 )
Date of Publication: Sept. 2005
VENTMXX002 Performance analysis of channel estimation and adaptive equalization in slow fading
channel
ABSTRACT: In our project, we first build up a wireless communication simulator including Gray coding,
modulation, different channel models (AWGN, flat fading and frequency selective fading channels),
channel estimation, adaptive equalization, and demodulation. Next, we test the effect of different
channel models to the data and image in receiver with constellation and BER (bit error rate) plots under
QPSK modulation. For Image data source, we also compare the received image quality to original image
in different channels. At last, we give detail results and analyses of the performance improvement with
channel estimation and adaptive equalization in slow Rayleigh fading channel. For frequency selective
fading channel, we use linear equalization with both LMS (least mean squares) and RLS (Recursive Least
Squares) algorithms to compare the different improvements. We will see that in AWGN channel, the
image is degraded by random noise; in flat fading channel, the image is degraded by random noise and
block noise; in frequency selective fading channel, the image is degraded by random noise, block noise,
and ISI.
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Keywords: Slow fading, flat fading, frequency selective fading, channel estimation, LMS, RLS
2007
VENTMXX003 ROBUST OBJECT TRACKING USING JOINT COLOR-TEXTURE HISTOGRAM
Abstract: A novel object tracking algorithm is presented in this paper by using the joint color texture
histogram to represent a target and then applying it to the mean shift framework. Apart from the
conventional color histogram features, the texture features of the object are also extracted by using the
local binary pattern (LBP) technique to represent the object. The major uniform LBP patterns are
exploited to form a mask for joint color-texture feature selection. Compared with the traditional color
histogram based algorithms that use the whole target region for tracking, the proposed algorithm
extracts effectively the edge and corner features in the target region, which characterize better and
represent more robustly the target. The experimental results validate that the proposed method
improves greatly the tracking accuracy and efficiency with fewer mean shift iterations than standard
mean shift tracking. It can robustly track the target under complex scenes, such as similar target and
background appearance, on which the traditional color based schemes may fail to track.
Keywords: Object tracking; mean shift; local binary pattern; color histogram.
2000
VENTMXX004 Efficient Encoding of Low-Density Parity-Check Codes
Abstract—Low-density parity-check (LDPC) codes can be considered serious competitors to turbo codes
in terms of performance and complexity and they are based on a similar philosophy: constrained
random code ensembles and iterative decoding algorithms. In this paper,we consider the encoding
problem for LDPCcodes. More generally, we consider the encoding problem for codes specified by
sparse parity-check matrices. We show how to exploit the sparseness of the parity-check matrix to
obtain efficient encoders. For the (3 6)-regular LDPC code, for example, the complexity of encoding is
essentially quadratic in the block length. However, we showthat the associated coefficient can be made
quite small, so that encoding codes even of length 100 000 is still quite practical. More importantly, we
will show that “optimized” codes actually admit linear time encoding.
Published in: Information Theory, IEEE Transactions on (Volume:47 , Issue: 2 )
Date of Publication: Feb 2001
Index Terms—Binary erasure channel, decoding, encoding, parity check, random graphs, sparse
matrices, turbo codes.
VENTMXX005 ML Estimation of Time and Frequency Offset in OFDM Systems
Abstract: We present the joint maximum likelihood (ML) symbol-time and carrier-frequency
offset estimator in orthogonal frequency-division multiplexing (OFDM) systems. Redundant
information contained within the cyclic prefix enables this estimation without additional pilots.
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Simulations show that the frequencyestimator may be used in a tracking mode and the time estimator
in an acquisition mode
Published in: Signal Processing, IEEE Transactions on (Volume:45 , Issue: 7 )
Date of Publication: Jul 1997
Index Terms: OFDM systems, acquisition mode, carrier-frequency offset estimator, cyclic prefix,
maximum likelihood, orthogonal frequency-division multiplexing, redundant information, symbol-time
estimator, time offset, tracking mode
VENTMXX006 Performance analysis of channel estimation and adaptive equalization in slow fading
channel
Abstract: In our project, we first build up a wireless communication simulator including Gray coding,
modulation, different channel models (AWGN, flat fading and frequency selective fading channels),
channel estimation, adaptive equalization, and demodulation. Next, we test the effect of different
channel models to the data and image in receiver with constellation and BER (bit error rate) plots under
QPSK modulation. For Image data source, we also compare the received image quality to original image
in different channels. At last, we give detail results and analyses of the performance improvement with
channel estimation and adaptive equalization in slow Rayleigh fading channel. For frequency selective
fading channel, we use linear equalization with both LMS (least mean squares) and RLS (Recursive Least
Squares) algorithms to compare the different improvements. We will see that in AWGN channel, the
image is degraded by random noise; in flat fading channel, the image is degraded by random noise and
block noise; in frequency selective fading channel, the image is degraded by random noise, block noise,
and ISI.
Keywords: Slow fading, flat fading, frequency selective fading, channel estimation, LMS, RLS
2007
VENTMXX007 PARAFAC-Based Blind Estimation Of Possibly Underdetermined Convolutive MIMO
Systems
Abstract: In this paper, we consider the problem of blind identification of a convolutive multiple-input-multiple-
output (MIMO) system with No outputs and Ni inputs. While many methods have been
proposed to blindly identify convolutive MIMO systems with No ges Ni (overdetermined), very scarce
results exist for the case of (underdetermined), all of which refer to systems that either have some
special structure or special and values. In this paper, we show that, as long as , independent of whether
the system is overdetermined or underdetermined, we can always find the appropriate order of
statistics that guarantees identifiability of the system response within trivial ambiguities. We also
propose an algorithm to reach the solution, that consists of parallel factorization (PARAFAC) of a -way
tensor containing th-order statistics of the system outputs, followed by an iterative scheme. For a
certain order of statistics , we provide the description of the class of identifiable MIMO systems. We also
show that this class can be expanded by applying PARAFAC decomposition to a pair of tensors instead of
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