1. Abstract- Detecting regions of change in multiple images of the same scene taken at different times
is of widespread interest due to a large number of applications in diverse disciplines, including
remote sensing, surveillance, medical diagnosis and treatment, civil infrastructure, and underwater
sensing. In this paper we propose a method of change detection on different types of images. This
method combines the concepts of Change Vector Analysis (CVA), K-Means Algorithm, Otsu
Thresholding and Mathematical Morphology.CVA is the initial step which helps to detect the
intensity level change in multiple images of the same scene taken at different times. The K-means
algorithm is an iterative technique that partitions an image into K clusters. Otsu Algorithm is an
unsupervised method of automatic threshold selection for image segmentation. Mathematical
morphology is very attractive for image segmentation because it efficiently deals with geometrical
descriptions such as size, area, shape, or connectivity that can be considered as segmentation-
oriented features.
Index Terms- Change Detection, K-Means Algorithm, Otsu Thresholding, Mathematical Morphology.
INTRODUCTION- Change detection has gained significance over years due to its extensive
applicability. The available algorithms for this purpose have been successfully used for region
recognition in remotely sensed images.

2. PREVIOUS WORKS-
Various Change Detection Algorithms have been cited in the literature for improving Change
Detection methods.
Change Detection Using Standard Deviation Calculation
This approach is based on the calculation of Standard Deviation for each pixel in the concerned
images. On the basis of standard deviation calculation of pixels, the discontinuity among the pixel is
recorded. Then the image is segmented into a binary image with a fixed threshold where black pixels
signify homogeneous region and white pixels denote discontinuity. By comparing the discontinuous
regions the changes are detected in the prescribed images. However, if the scenes under
consideration have sufficient amount of varied illumination then this algorithm fails to detect the
changed regions properly [1].
Change Detection Using Thresholding
Thresholding is a fundamental technique applied in many image processing applications including
Change Detection. It develops an approach for efficient and quantitative evaluation of thresholding
algorithms for change detection in a surveillance environment. The technique basically attempts for
finding a threshold value, which enables the classification of pixels into different categories. A major
weakness of this segmentation mode is that: it generates only two classes. Therefore, this method
fails to deal with multichannel images [2].
Change Detection Using Adaptive Threshold Technique
Change detection based segmentation algorithms, threshold the frame difference to form change
detection mask. Then the change detection masks are further processed to generate final object
masks. It deals with achieving efficient image segmentation by adaptive threshold technique. This
algorithm can give satisfying Change Detection results with low computation. The processing speed
is high, but it is often not robust. The Change Detection results are suffered from the uncovered
background situations; still object situations, light changing, shadow, and noise [3].
Change Detection Using Morphological Filters
It is an unsupervised technique for change detection (CD) in very high geometrical resolution images,
which is based on the use of morphological filters. This technique integrates the nonlinear and
adaptive properties of the morphological filters with Change Vector Analysis (CVA) procedure.
Alternating sequential filters are the most effective and permit the preservation of the geometrical
information of the structures in the scene while filtering the homogeneous areas. The weakness of
this approach is that the efficiency depends on the Filter size. Also the result is found more
satisfactory when dealing with Very High Resolution Images [4].
STEPS OF PROPOSED METHOD

3. In this paper, we have discussed a technique of change detection in different types of images. It is
assumed that an adequate pre-processing phase has been applied to the multi-temporal images in
order to make them as more comparable as possible. Proposed method consists of four distinct steps
and they are discussed below.
Step 1: Change Vector Analysis
Let us consider two gray scale images f1(x,y) and f2(x,y) acquired over the same area at different
times t1 and t2 respectively. Here, each image fk with k = 1, 2, has a size of I × J pixels.
We analyse the Difference Image
D(x,y) =| f1(x,y) - f2(x,y)|
Step 2: Image Segmentation using K-Means Algorithm
Image segmentation is the process of assigning a label to every pixel in an image such that pixels
with the same label share certain visual characteristics. Each of the pixels in a region are similar with
respect to some characteristic or computed property.
In this step we segment the Difference Image generated in the previous step using K-Means
Clustering Algorithm. This algorithm aims at minimizing an objective function, in this case a squared
error function. The objective function
,
Here ||xi
(j)
– cj||2
is a chosen distance measure between a data point xi
(j)
and the cluster centre
cj , is an indicator of the distance of the n data points from their respective cluster centres [5].
Step 3: Calculation of Threshold Limit
Thresholding is very important task in Change Detection Algorithms. The accuracy of an algorithm is
dependent on the choice of threshold parameters.
The criteria of selection of a parameter for a given image are that the resultant should satisfy the
following:
(i) It should contain most of the prominent changed regions
(ii) It should not contain too much unwanted noise
(iii) It should be meaningful and visibly pleasing.
In the proposed algorithm we calculated the Threshold limit using Otsu Thresholding.
In Otsu Thresholding we iterate through all the possible threshold values and calculating a measure
of spread for the pixel levels Xi each side of the threshold, i.e. the pixels that either fall in foreground
or background using histogram Hi of the image D(x,y).
σ2
b = ∑ (Xi - µb)2
* Hi

4. where, 0 i b
σ2
f = ∑ (Xi - µf)2
* Hi
where, f i 255
Within Class Variance is calculated as
σ2
W = wb σ2
b + wf σ2
f
The value having lowest sum of weighted variances is the Threshold limit T [6].
Step 4: Thresholding the Segmented Image
In the segmented image generated by applying K-Means Algorithm, those pixels having Intensity
value less than Threshold limit found by applying Otsu Thresholding are Zeroed. The pixels having
Intensity value greater than Threshold limit are kept as it is.
K(x,y) = 0 if S(x,y) T
= S(x,y) else
Step 5: Morphological Operation
This step is applicable only for Medical Images. Here Morphological Erosion Operator is applied on
the image K(x,y) [7][8].
The basic effect of the operator is to erode away the boundaries of regions of foreground pixels. Thus
areas of foreground pixels shrink in size, and holes within those areas become larger.
If K(x,y) denotes an input image and Mn denotes a window or flat structuring element of size n , the
erosion by the flat structuring element Mn is given by
G(x,y) = εΝ(K(x,y))
= Μ in{ K(x+xo,y+yo),(xo,yo) Є MN }
Step 6: Counting the number of neighbourhood Pixels
In the image generated above, for pixels having intensity value greater than Zero, the number of
neighbourhood pixels having intensity value greater than Zero is counted. If the count value is
greater than 4, then we set the pixel intensity to 255, else 0.
O(x,y) = 0 if count 4
= 255 else

5. Figure 1: Methodology
EXPERIMENTAL RESULTS:
The input images are considered to be .pgm images. All the image files that we have tested are
natural images. The number of segments obtained are tabulated in Table I and compared to known
Change Detection technique.
It has been observed that when Change Vector Analysis (CVA) is combined with K-means algorithm,
Otsu Thresholding and Mathematical Morphology, it produces reliable results.
(a) (b) (c) (d)
(e) (f) (g) (h)

6. (a) (b) (c) (d)
COMPRATIVE STUDY OF DIFFERENT CHANGE DETECTION TECHNIQUES
For Image 1:
Figure 2 (a) Cell Image without Patch (b) Cell Image with patch (c) Image after CVA
(d) Segmented Image (e) Image after thresholding Segmented Image (f) Image after
Morphological Erosion Operation (g) Final Change Image (h) Ground Truth
(e) (f) (g)
Figure 3 (a) Hall Image (b) Hall Image with Man (c) Image after CVA (d) Segmented Image (e)
Image after thresholding Segmented Image (f) Final Change Image (g) Ground Truth

7. (a) (b) (c)
Table 02
Comparison of Change detection Techniques for Medical Image 1
(d) (e) (f)
Figure 3 (a) Cell Image without Patch (b) Cell Image with Patch (c)
Reference Image (d) CD using Standard Deviation Calculation (e) CD
using Segmentation and Adaptive Thresholding (f) CD using
Proposed Algorithm
Method Total
Pixels
Correct
Detection
False
Alarm
Missed
Alarm
Total Error
Pixels Percentage
CD using SD
Calculation 16900 pixels 16167 pixels 497 pixels 236 pixels 733 pixels 4.337 %
CD using
Segmentation and
Adaptive
Thresholding
16900 pixels 15925 pixels 108 pixels 867 pixels 975 pixels 5.769 %
CD using Proposed
Algorithm 16900 pixels 16118 pixels 189 pixels 593 pixels 782 pixels 4.627 %

8. For Image 2:
(a) (b) (c)
Method Total
Pixels
Correct
Detection
False
Alarm
Missed
Alarm
Total Error
Pixels Percentage
CD using SD
Calculation 25600 pixels 25290 pixels 195 pixels 115 pixels 310 pixels 1.210 %
CD using
Segmentation and
Adaptive
Thresholding
25600 pixels 25172 pixels 186 pixels 242 pixels 428 pixels 1.671 %
(d) (e) (f)
FIG.22 (a) Cell Image without Patch (b) Cell Image with Patch (c)
Reference Image (d) CD using SD (e) CD using Segmentation and
Adaptive Thresholding (f) CD using Proposed Algorithm

9. CD using Proposed
Algorithm 25600 pixels 25121 pixels 13 pixels 466 pixels 479 pixels 1.871 %
Table 03
Comparison of Change detection Techniques for Medical Image 2
For Image 3:
(a) (b) (c)
(d) (e) (f)

10. Method Total
Pixels
Correct
Detection
False
Alarm
Missed
Alarm
Total Error
Pixels Percentage
CD using SD
Calculation 262144 pixels 245734 pixels 14711 pixels 1699 pixels 16410 pixels 6.259 %
CD using
Segmentation and
Adaptive
Thresholding
262144 pixels 256258 pixels 3701 pixels 2185 pixels 5886 pixels 2.245 %
CD using Proposed
Algorithm 262144 pixels 258431 pixels 27 pixels 3686 pixels 3713 pixels 1.416 %
Table 04
Comparison of Change detection Techniques for Image 3
FIG.23 (a) Hall Image without Man (b) Hall Image with Man (c)
Reference Image (d) CD using SD (e) CD using Segmentation and
Adaptive Thresholding (f) CD using Proposed Algorithm

11. CONCLUSION:
We have proposed a new approach for unsupervised Change Detection technique for gray level image that can
successfully detect the change in the images. Comparison of the experimental results with that of other Change
Detection methods, show that the technique gives satisfactory results when applied on well known natural and
medical images.
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