Cosmetic Gloss Measurement Ver2

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In Vivo Quantitative Evaluation of Gloss 藥妝真 . 晶砂真異典藏 -- 芳香療法、手工皂與化妝品 DIY 的研究世界

Introduction ,[object Object],[object Object]

Color and Gloss ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Degrees of Gloss ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Advantages to Gloss Measurement ,[object Object],[object Object],[object Object],[object Object],[object Object]

Conclusion ,[object Object],[object Object],[object Object]

References ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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Shadow detection and removal from single images of natural scenes is the main problem. Hence, Shadow detection and removal remained a challenging task. Significant research carried out on different shadow detection techniques. Over the last decades several approaches were introduced to deal with shadow detection and removal. Shadows are visual phenomena which happen when an area in the scene is occluded from the primary light source e.g. sun . Shadows are everywhere around us and we are rarely Confused by their presence. This article provides an overview of various methods used for shadow detection and removal using some main components like texture analysis, color information, Gaussian mixture model GMM and deterministic non model based approach. Texture Color This paper is aimed to provide a survey on various algorithms and methods of shadow detection and removal with their comparative study. Avinash Kumar Singh | Ankit Pandit ""Shadow Detection and Removal Techniques: A Perspective View"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd25201.pdf Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/25201/shadow-detection-and-removal-techniques-a-perspective-view/avinash-kumar-singh

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The availability of imaging sensors operating in multiple spectral bands has led to the requirement of image fusion algorithms that would combine the image from these sensors in an efficient way to give an image that is more perceptible to human eye. Multispectral Image fusion is the process of combining images optically acquired in more than one spectral band. In this paper, we present a pixel-level image fusion that combines four images from four different spectral bands namely near infrared(0.76-0.90um), mid infrared(1.55-1.75um),thermal- infrared(10.4-12.5um) and mid infrared(2.08-2.35um) to give a composite colour image. The work coalesces a fusion technique that involves linear transformation based on Cholesky decomposition of the covariance matrix of source data that converts multispectral source images which are in grayscale into colour image. This work is composed of different segments that includes estimation of covariance matrix of images, cholesky decomposition and transformation ones. Finally, the fused colour image is compared with the fused image obtained by PCA transformation.

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ABSTRACT The availability of imaging sensors operating in multiple spectral bands has led to the requirement of image fusion algorithms that would combine the image from these sensors in an efficient way to give an image that is more perceptible to human eye. Multispectral Image fusion is the process of combining images optically acquired in more than one spectral band. In this paper, we present a pixel-level image fusion that combines four images from four different spectral bands namely near infrared(0.76-0.90um), mid infrared(1.55-1.75um),thermal- infrared(10.4-12.5um) and mid infrared(2.08-2.35um) to give a composite colour image. The work coalesces a fusion technique that involves linear transformation based on Cholesky decomposition of the covariance matrix of source data that converts multispectral source images which are in grayscale into colour image. This work is composed of different segments that includes estimation of covariance matrix of images, cholesky decomposition and transformation ones. Finally, the fused colour image is compared with the fused image obtained by PCA transformation.

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Sonar images produced due to the coherent nature of scattering phenomenon inherit a multiplicative component called speckle and contain almost homogeneous as well as textured regions with relatively rare edges. Speckle removal is a pre-processing step required in applications like the detection and classification of objects in the sonar image. In this paper computationally efficient Fractional Integral Mask algorithms to remove the speckle noise from sonar images is proposed. Riemann- Liouville definition of fractional calculus is used to create Fractional integral masks in eight directions. The use of a mask incorporated with the significant coefficients from the eight directional masks and a single convolution operation required in such case helps in obtaining the computational efficiency. The sonar image heterogeneous patch classification is based on a new proposed naive homogeneity index which depends on the texture strength of the patches and despeckling filters can be adjusted to these patches. The application of the mask convolution only to the selected patches again reduce the computational complexity. The non-homomorphic approach used in the proposed method avoids the undesired bias occurring in the traditional homomorphic approach. Experiments show that the mask size required directly depends on the fractional order. Mask size can be reduced for lower fractional orders thus ensuring the computation complexity reduction for lower orders. Experimental results substantiate the effectiveness of the despeckling method. The different non reference image performance evaluation criterion are used to evaluate the proposed method.

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Hand gesture classification is popularly used in wide applications like Human-Machine Interface, Virtual Reality, Sign Language Recognition, Animations etc. The classification accuracy of static gestures depends on the technique used to extract the features as well as the classifier used in the system. To achieve the invariance to illumination against complex background, experimentation has been carried out to generate a feature vector based on skin color detection by fusing the Fourier descriptors of the image with its geometrical features. Such feature vectors are then used in Neural Network environment implementing Back Propagation algorithm to classify the hand gestures. The set of images for the hand gestures used in the proposed research work are collected from the standard databases viz. Sebastien Marcel Database, Cambridge Hand Gesture Data set and NUS Hand Posture dataset. An average classification accuracy of 95.25% has been observed which is on par with that reported in the literature by the earlier researchers.

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For Determination of Particle Size various method are used. here I mentioned the Laser Light scattering for determining the Particle Size. Mainly two type of laser scattering are used, 1. Static laser light scattering. 2. Dynamic laser light scattering. detail about there principle we see in the slide . . . . If in any point you didn't understood ,you can contact with me. hope it useful to you. Thank You.

$Particle Size Analysis by Laser Diffraction Method.$ $Particle Size Analysis by Laser Diffraction Method.$

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Abstract In this research, holographic optical element applied for beautification recorded in holographic surface relief photoresist. Various symmetric periodic structure with high diffraction efficiency in photosensitive material depends on various recording geometry parameters are explained. We used holographic dual beam multiple interference method for this pattern creation. A long range periodicity confirmed with the help of optical microscope, laser light diffraction and scanning electron microscope. Combination of symmetric structured holographic optical elements, DPSS laser source and bi-directional RPM controller results the proto type HOLO-DRZZLER product. Our auspicious aim is to commercialize and our product is mainly meant for outdoor beautification application. Index Terms: Holography, photoresist, holographic decoration, laser and holographic optical element

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