iit bombay project

8/10/2019 IIT Bombay Project

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Indian Academy of Sciences, Bangalore

Summer Research Fellowship Programme 2010

ame of SRF! SEJAL CHAUHAN

Regis"ra"ion o#! ENGS628

Ins"i"u"ion where wor$ing! IIT, BOMBAY

%a"e of &oining "he pro&ec"! 10thMAY,2010

%a"e of comple"ion of "he pro&ec"! 10thJULY,2010

ame of "he 'uide! Prof.SUBHASIS CHAUDHAI

Pro&ec" (i"le! !IDEO COMPOSTING UNDE !AYING

ILLUMINATION

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A"#$o%&'()'*'$t

I am extremely grateful to Prof. Subhasis Chaudhuri, Department of Electrical Engineering, Indian Institute

of Technology, Bombay, for being the prime source of guidance and inspiration in completion of the

project. I extend my heartfelt gratitude to Mr. R. Shanmuganathan for guiding me and helping me

overcome all the problems during the course of the project. He has been a perennial source of motivationand help behind my work. I thank Mr. Vishal Kumar for consistently channelizing my knowledge and

helping me overcome the initial hurdles in the project. I am thankful to Mr. Ranjith A. Ram with whose

cooperation I was able to do the desired project. I am thankful to Mr. Deven Patel and Mrs. Renu M. R. for

providing the necessary support and help in the lab. I thank Mr. Ronak Shah for his inspiration and help in

completion of the work. I am thankful to my colleagues and friends for motivating me for research and

providing constant support in difficult times.It has indeed been a great privilege for me to work in their

project group. I am indebted to Indian Institute of Technology, Bombay especially Vision and Image

Processing Lab for providing me with the necessary and imperative infrastructure and system support

towards the completion of my work. I am beholden to Indian Academy of Sciences, Indian National

Science Academy & the National Academy of Sciences, India for providing me with the opportunity and

financial support to undertake this project work.

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C'rt+f+"t'

This is to certify that the project titled, Video Compositing Under Varying Illumination, was

undertaken by Sejal Chauan2nd year B.Tech, Department of Electronics and Communication Engineering,

National Institute of Technology, Warangal as a part of Summer Research Fellowship Programme offered

by Indian Academy of Sciences, Indian National Science Academy & The National Academy of Sciences.

The work was completed under the guidance of Prof. Subhasis Chauduri, Department of Electrical

Engineering, IIT,Bombay during the time span of 8 weeks between 10 May 2010- 10 July 2010 and a

report on the same was submitted to Prof. Subhasis Chauduri, Department of Electrical Engineering, Indian

Institute of Technology, Bombay.

Prof. Subhasis Chauduri

Department of Electrical Engineering

Indian Institute of Technology, Bombay

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A-tr"t

Digital Computational Photography aims at replicating the real world scenes faithfully. But there exist

some short comings which have to be improved upon regarding the clarity of the video. One such being the

varying illumination causing the camera to self adjust the intensity of the overall image and hence, causing

distortions in clarity of desired image of the extracted object. This usually happens because of the automatic

gain control (AGC) of the camera. Tracking the moving object and self illuminating it helps us resolve the

problem. The algorithm being used here to extract the exact object is Chan-Vese active contour with

Stauffer Grimsons algorithm which helps in background subtraction using mixture of Gaussians. Chan-

Vese active contour without edges uses geometrical models and variational level set methods to find the

fitting energy functional which is then minimised and the parameters updated to extract the moving object.

The contours evolve depending on the kind of input image given to them and a mask which helps it decideits starting position. The input image has an influence of the light falling on the object which even distorts

the foreground obtained. So for bettering the images, we use k-means clustering method to obtain the best

possible masks which segment the original images and are used in the active contour model of Chan-Vese.

The masks can then be used to re-illuminate the object in the video.

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Co$t'$t

!+('o Co*/o+t+$) U$('r !r+$) I&&*+$t+o$

Acknowledgements

Certificate

Abstract

1 Introduction....61.1 Algorithm Used... 7

1.2 Required Knowledge.......7

2 Background.... 8

2.1 Existing Work .....8

2.2 AGC Estimator .......9

2.3 Chan-Vese model... 10

3 Present Work.. 11

3.1 K-means cluster method .. .12

3.2 Convex Hull....... 134. Experimentation and Results..... ..13

4.1 Application of various algorithms used. 13

4.2 Sample Problem ... 18

5 Conclusions and future work.22

6 Bibliography..... 23

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!+('o Co*/ot+$) U$('r !r+$)

I&&*+$t+o$1 I$tro("t+o$

Digital image processing is any form of signal processing for which the input is an image, such as a

photograph or video frame; the output of image processing may be either an image or, a set of

characteristics or parameters related to the image. Most image-processing techniques involve treating the

image as a two-dimensional signal and applying standard signal-processing techniques to it. There are

many applications to this field such as Computer vision, Face detection, Remote sensing, Morphological

Image Processing. The various fields where this is applied are Biological data visualization,Chemical

imaging,Crime mapping, Data visualization,Educational visualization,Geovisualisation, Information

visualization, Mathematical visualization,Medical imaging,Product visualization, Volume visualization.

In this project, we deal with background subtraction which is a process to detect a movement or significant

differences inside of the video frame, when compared to a reference, and to remove all the non-significant

components (background). The basic requirement from background subtraction algorithm is the ability to

detect motion elements accurately and to adjust to any background illumination.

Background subtraction is applied in many areas, such as surveillance system (to effectively segment the

only moving object), pose or gesture recognition. For instance while inspecting an ATM robbery. Theproblem that we are dealing with is the clarity of the object, in this case a robber. It is very important to

view his/her image with the best luminance which the present digital camera fails to capture due to limited

dynamic ranging.

In the past, some work has been done in the field of motion tracking in videos where the foreground is

separated from the background and any moving object is tracked. The main aim is to segment and extract

the object in such a way that all its features are visible and minimal noise is present. Many algorithms

involving spatial homogeneity, Kalman et al. [5] filter application, Watershed algorithm, Bayesian

technique have been used. The main reason for the failure of background subtraction methods in certain

cases is the lack of exploitation of spatial correlation in image intensity, whereas camera AGC affects the

intensity at a global level.

The next step would be to make sure the object is obtained properly via active contour evolution around the

object. These contours take up the video frame and the previous mask obtained as the input. The goal is to

evolve the contour in such a way that it stops on the boundaries of the foreground region. There are various

ways to define the evolution equation; for example, the contour might move with a velocity that depends on

the local curvature at a given point or the image gradient at that point. After extraction of the object, we use

the mask to brighten up the present object in the video which is only possible if the object is extracted

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perfectly. The artificial illumination is done by the Bhat et al.[10] algorithm.

The report is organized in such a manner that section 1 deals with the introduction of the problem and

various concepts involved. Section 2 deals with the previous existing work and section 3 revolves around

my work. Section 4 gives us the observations and results for different cases followed by conclusions and

references.

1.1 ALGORITHM USED

The Stauffer-Grimsons et al.[1] mulitimodal algorithm which uses the adaptive mixture of Gaussian

variables along with poisson solver et al.[9] and Kalman filter[5] to processes the image in gradient field.

The masks obtained from this algorithm have a lot of noise owing to automatic gain control of the digital

camera. So a robust algorithm which estimates the AGC while tracking is build so that this can be

eliminated and the masks hence obtained are freed from noise.

We then take these improved masks and with the help of active contours evolving around the object and

using level set method extract a finer outline. A function )(i, j , t) (the level-set function) is defined, where

(i, j) are coordinates in the image plane and t is an artificial time. At any given time, the level set function

simultaneously defines an edge contour and a segmentation of the image. The edge contour is taken to be

the zero level set {(i, j) such that)(i, j , t)= 0}, and the segmentation is given by the two regions {)> =0}

and {)< 0}. The level set function will be evolved according to some partial differential equation, and

hopefully will reach a steady state lim t->?)that gives a useful segmentation of the image.

Whenever we take a video which has various texture in the background, this algorithm fails while using

Chan Vese et al.[3] active contours as they even take the input image into consideration. So there is a need

to improve the input image such that the final mask obtained is the exact silhouette of the object. The k-

means clustering method is used.

This method of cluster analysis which aims to partition nobservations into kclusters in which eachobservation belongs to the cluster with the nearest mean. Euclidean distance is used as a metric and

variance is used as a measure of cluster scatter. A weighted distance measure utilizing pixel coordinates,

RGB pixel color and/or intensity, and image texture is commonly used.

1.2 REQUIRED KNOWLEDGE

Stauffer Grimson Model

The algorithm discusses modeling each pixel as a mixture of Gaussians and using an on-line approximation

to update the model. Ridder et al. [5] modeled each pixel with a Kalman Filter which made their system

more robust to lighting changes in the scene. For a pixel (x0,y0) at a time t, where I is the image, its history

is given by:

(1)

The probability of observing the current pixel value is given as follows,

(2)

where K is the number of distributions, i,tis an estimate of the weight (what portion of the data is

accounted for by this Gaussian) of the ith Gaussian in the mixture at time t, i,tis the mean value of the ith

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Gaussian in the mixture at time t, *i,tis the covariance matrix of the ith Gaussian in the mixture at time t,

and where is a Gaussian probability density function.

(3)

If none of the K distributions match the current pixel value, the least probable distribution is replacedwith a distribution with the current value as its mean value, an initially high variance, and low prior weight.

The prior weights of the K distributions at time t, k, t, are adjusted as follows

(4)

The and parameters for unmatched distributions remain the same. The parameters of the distribution

which matches the new observation are updated as follows

First, the Gaussians are ordered by the value of / . This value increases both as a distribution gains

more evidence and as the variance decreases. Then the first B distributions are chosen as the

background model, where

Pre processing of GradientsThis way the saturation is reduced in under- or over-exposed images. This is done by carefully modifying

their corresponding gradient fields. The following is the illumination change function.

++ ffv = (9)

This operationg is dependent on the values of and -which are the parameters involved, whereas fiis the image.

.,/...,//+2#0/.,/ ./

yxfyxfyxg iiii =

In the above equation, i is "he mean gradien" magni"ude of "he i"h image# 12#0,2#0 where "he image

3righ"ens up when "he 4alue is nega"i4e and is e5posed "o low illumina"ion when - is posi"i4e#

(his is "he 3asic e6ua"ion re6uired "o change "he illumina"ion of an o3&ec" in a 4ideo which is done in "he

o"her pro&ec" using "he mas$s o3"ained from "his e5perimen"#

2. BACKGROUND

8

(6)

(5)

(7)

(10)

(8)


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2.1 EXISTING WORK

Stauffer and Grimson [1] modelled each pixel as a mixture of Gaussians and an online EM Algorithm was

proposed by P. KaewTraKulPong et al. [8] to update the model. Even though 3 to 5 Gaussian distributions

are able to model a multi-modal background with difficult situations like shaking branches of tree, clutter

and so forth, there is a fact that this kind of pixel-based background modelling is sensitive to noise andillumination change. A lot of people presented a number of important problems when using background

subtraction algorithms, they proposed a solution using pixel, region and frame level processing, their

algorithm is capable of dealing with quick illumination changes, but their technique is based on a complex

gradient based algorithm.Foreground was analyzed as moving objects, shadow and ghost by combiningthe motion information. Whereas these works are effective to solve all the problems they mentioned, the

computation cost is relatively expensive for real-time video surveillance systems because of the

computation of optical flow.

The following formulae have been used in the past so far to extract the foreground from the background.

[11]

The variation in illumination is done in the gradient field which is non-conservative after processing. To

retrieve the images back from the processed gradients, possion equation solver et al.[9] has been used.

2.2 AGC ESTIMATOR

9

(11)

(17)

(15)

(18)

(16)

(12)

(13)

(14)


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When the effect of camera AGC is significant over a short span of time then Stauffer Grimson method fails

to update the background pixels correctly. AGC is positive when the object is darker than the background

and the whole scene is exposed to more light. While when the object is brighter than its surroundings then

AGC is negative. The AGC and exposure time affect the video frame at a global level so the temporal

intensity variation of the non-motion pixels can be expected to vary in an abrupt fashion but it is possible to

parameterize the intensity variation. To decrease the computational overhead we consider a few uniformly

spaced pixels for the purpose. A scatter plot is made which samples points in the best background imageand the current frame. All the points that lie beyond a certain threshold bandwidth at y=x line are

eliminated(outliers).

Transfer function graph

Background frame

C

U

R

R

E

N

T

F

R

A

M

E

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Fig 1In figure 1 we can see there is a lot of deviation from the band threshold which means AGC has a major role in

changing the image. So the parameters are updated accordingly. The best fit curves for red, green, blue colors are also

depicted.

Outliers= number of points lying outside the thin band of the scatter plot

Total number of points

If the outliers exceed a certain threshold then AGC is present and a best fit curve is obtained. Then the

background pixels mean and variance are updated so as to compensate for the AGC. The transfer function

fb,tfives us a good estimate of the intensity transfer characteristics between the best background estimate

and the current frame. So pre-processing of the mean of Gaussians is done before matching it with the

intensity of the current pixel intensity.

./.1/ 1,,1,7

1, += tktbtktk f

Where -lies between 0.5 and 0.7

The variance undergoes a weighted update

1,,

7

,

7

1, .1/.1/ += tktktbtk f

When the video is too noisy then the parameters are updated as follows

..//,ma5 2

1,

7

,1,

2

1,

27

1, = tktbtktktk f

2.3 CHAN VESE MODEL

Depending on implementation, active contours have been classified as geometric or parametric active

contours. Parametric contours, irrespective of representation, are known to suffer from the problem of

irregular bunching and spacing out of curve points during the curve evolution. In a spline-based

implementation of active contours, this leads to occasional formation of loops locally, and subsequently the

curve blows up due to instabilities. Initialization is problem which is overcome in this experiment as the

masks that are obtained from tracker algorithm is given to the active contour algorithm.

Chan-Vese[3] algorithm uses variational calculus methods to evolve the level set function. Variational

methods work by seeking a level set function that minimizes some functional. In this context, by functional

we mean a mapping that takes a level set function as input, and returns a real number.

Euclidean curve shortening equation

Energy of snakes is given by

WhereEint represents the internal energy of the curve, position vector v(s) = (x(s),y(s)), and -are

parameters that are set by the user with subscripts being the derivatives.

consider the contour with the position vector w = v1, v2,., vn, vi= (xi,yi).

11

(24)

(23)

(19

(20)

(21

(22


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The fitting energy where the minimizing level set function will define the segmentation, is given by

Where H is a Heaviside function, ,8,91,92and p are parameters selected by the user to fit a particular

class of images.

The Euler-Lagrange equations and the gradient-descent method are used to derive the following evolution

equation for the level set function that will minimize the fitting energy

The equation to reinitializing the energy for the snakes to keep evolving is given by

Where :t(0)=

at t=0

Flow Chart

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(25)

(26)

(27)

(28)


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3PRESENT WORK

The experiment aims at achieving robustness and being able to apply to any scene. The masks presently

obtained from various algorithms give us an idea only about the position of the object and somewhat the

shape. We have tried to approach the exact extraction of the silhouette of the moving object by theapplication of k means cluster segmentation to the appropriate active contour model such that the best mask

is approached. We have even included all those situations in which a particular algorithm would work. In

case of a video wherein the background is plain, the algorithm almost achieves the perfect mask. But in

case of a more natural background, there are extra artifacts included in the masks which distorts them. So

we even try to overcome it by morphological operations before they are inputted to the final active

contours.

3.1 K-MEANS CLUSTER SEGMENTATION

This is used to segment the original image in such a manner that two of the clusters combine to complete

the object which needs to be extracted. The processed image can then be properly used as an input to the

active contour model without the final mask being distorted. Clustering is a way to separate groups of

objects. K-means clustering treats each object as having a location in space. It finds partitions such that

objects within each cluster are as close to each other as possible, and as far from objects in other clusters as

possible. The rgb images are first converted to l*a*b and the approach is to choose a small sample region

for each color and to calculate each sample region's average color in 'a*b*' space. We use these color

markers to classify each pixel.

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3.2 CONVEX HULL

In mathematics, and computational geometry, a Delaunay triangulation for a set P of points in the plane is

a triangulation DT(P) such that no point in P is inside the circumcircle of any triangle in DT(P). Delaunay

triangulations maximize the minimum angle of all the angles of the triangles in the triangulation; they tend

to avoid skinny triangles. Convex hull gives us the non-ambiguous and efficient representation of the

required convex shape constructed.

(25)

Though this was a method which will help us find the exact outline of the object in a frame but it only gave

us the points which fell on the outline and it did not help much to achieve the exact mask.

4 EXPERIMENTS AND RESULTS

4.1 APPLICATION OF VARIOUS ALGORITHMS USED

4.1.1 ACTIVE CONTOUR

Fig 1.1 fig 1.2

In the fig 1.1, an image is take whose object needs to be extracted. In fig 1.2 the man walking is grasped by the

contour along with his shadow.

Fig 1.3 fig 1.4 fig 1.5 fig 1.6

Fig 1.3 is the object obtained after 180 iterations with =0.8, fig 1.4 is the object extracted after 180iterations when =0.2. Fig 1.5 is the image of the object extracted after 1800 iterations with=0.8, fig 1. is after 1800 iterations with =0.2 in the !han "ese mode# of acti$e contours.

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We observe the higher the value of alpha, the lower its chances of the object boundary being distorted.

Also we notice that the number of iterations also actively contribute in extracting the object. This is due to

the energy function being minimized and evolved a large number of times such that they distort the output.

Then the surrounding background pixels also start involving in the robust algorithm.

Fig 1.7 fig 1.8

Fig 1.7 is the mask given to the Chan Vese model of active contour and fig 1.8 is the output obtained.

If we notice carefully, the contour has grabbed only those parts of the mask which are not very illuminated

in the actual image.

Fig 1.9 fig 1.10 fig 1.11

Fig 1.12 fig 1.13

Figure 1.9 is the mask obtained from Stauffer Grimson[1] , fig1.10,fig 1.11,fig 1.12,fig 1.13 are the outputs of active

contours run at different iterations.

Here the original mask is better in those areas which are well illuminated. But the contour is covering up all

the holes in the darker region of the actual region. This shows us that active contour will fail if the input

image has different illuminations on the object. So the k cluster method should be adopted to segment the

objects image and combine those in which the object becomes complete regardless the illumination

without including the background.

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4.1.2 CONVEX HULL

Fig 2.1 fig 2.2

Fig 2.3 fig 2.4

Fig 2.5 fig 2.6

Figure 2 shows us the implementation of convex hull. Fig 2.1 shows us the original input image, fig2.2 gives us the

mask from the tracker code, fig 2.3 is the morphologically improved image mask, fig 2.4 is the foreground extracted

when fig 2.1 and fig 2.3 are fed to the active contours, fig 2.5 is the close up version of the object, fig 2.6 is the

convex hull obtained of the object.Note: fig 2.6 can not be used to retrieve the best possible mask.

4.1.3 K-means Cluster Segmentation

With fig 2.1 being the input, we apply k-means cluster segmentation and obtain the following results.

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Fig 3.1 fig 3.2

Fig 3.3 fig 3.4 fig 3.5Fig 3.1 gives us the first segmented image cluster where the object is lowly exposed, fig 3.2 gives the second

segmented image cluster where the object is highly exposed, fig 3.3 gives us the third image cluster which comprises

of the background, fig 3.4 is the combination of cluster 1 and 2 which is given as the input to active contours, fig 3.5

is the almost perfect mask obtained from the image.Note: The drawback here is missing hair in the final mask

obtained.

4.1.4 ILLUMINATION VARIATION IN A GIVEN IMAGE

Fig 4.1 fig 4.2

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Fig 4.3 fig 4.4 fig 4.5In these figures, illumination variation has been applied. Fig 4.1 is the input image1, fig 4.2 is the image obtained by

processing image1 and setting the -value as negative for brighter exposure; fig4.3, fig 4.4,fig 4.5 are images

obtained while the values of - were posi"i4e#

.1.3 $$+$) of th' Stff'r Gr+*o$ "o(' %+th th' AGC 't+*tor

Fig ;

In fig ;, "he "op lef" corner has a grid which is di4ided in"o four par"s where "he "op lef" grid shows "he curren" frame

3eing processed, "op righ" shows "he image o3"ained af"er "rac$er algori"hm was applied, 3o""om righ" shows "he

ou"pu" af"er "he A'< es"ima"or has rec"ified "he mas$ 3y reducing "he noise dras"ically and 3o""om lef" shows "he 3es"3ac$ground image= whereas "he righ" image shows "he "ransfer func"ion graph of "he curren" frame#

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which is due "o "he num3er of ou"liers e5ceeding "he 3and "hreshold in "he "ransfer func"ion and "he A'< more "han i"

can 3e compensa"ed#

A//&+"t+o$ of 5*'$ C&t'r S')*'$tt+o$

Fig ;#2#@ fig ;#2#

Fig ;#2#

C

Fig ;#2#D fig ;#2#10Fig 4.2.6 is the frame evaluated; fig 4.2.7 is the k-means cluster segmented image where two of the clusters have

been combined to make sure the dark and the light parts of the object are included; fig 4.2.8 is the mask obtainedfrom the tracker code with AGC estimator; fig 4.2.9 is the final mask obtained by using the original frame and

previous mask in active contour model; fig 4.2.10 is the final mask obtained by using the k-means clustered image

and tracker code mask

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Co*/r+o$ -'t%''$ -"#tr"#+$) $( tr"#'r "o(' *#

Fig 4.2.11 fig 4.2.12In fig 4.2.11 is the mask obtained by feeding the previous better masks in the active contour along with the current

frame which is segmented by k-means cluster. Fig 4.2.12 is the mask obtained by the tracker code with AGC

estimator.

Backtracking

Fig 4.2.13 fig 4.2.14Fig 4.2.13 is the original frame and fig 4.2.14 is the output obtained by applying backtracking which turns out to be

more accurate. The last mask is first given as the input to active contour along with their corresponding frame and the

mask obtained is iteratively fed to the active contour model with the previous frame. This makes sure that the noise

level remains in control.

B"#tr"#+$) $( 5*'$ C&t'r+$)

Fig 4.2.15 fig 4.2.16 fig 4.2.17Fig ;#2#1? is "he segmen"ed image, fig ;#2#1@ is "he mas$ o3"ained 4ia 3ac$"rac$ing and fig ;#2#1 is "he image mas$

o3"ained when "he algori"hm is carried ou" se6uen"ially from "he 3eginning#Note: We can see the improvement in the

backtracking algorithm.

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Shortcoming of the algorithm

Fig 4.2.18 fig 4.2.19Fig ;#2#1 is "he mas$ o3"ained from "he algori"hm "ill where "he noise is "olera3le, fig ;#2#1D is "he mas$ o3"ained

"owards "he end when A'< is "oo high#

Ee will no"ice "ha" when a lo" of pi5els e5ceed "he 3andwid"h of "he "ransfer func"ion graph, "he algori"hm

is no" a3le "o compensa"e for "he gain and a dis"or"ed mas$ is o3"ained which e4en segmen"a"ion is no" a3le"o o4ercome#

Fig ;#2#20 fig ;#2#21

Fig 4.2.20 is the mask obtained when the segmented clusters are not chosen and any two random ones combines togive the input image to active contour model, fig 4.2.21 is the mask obtained after selecting the clusters.

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CONCLUSIONS AND FUTURE WORK

We have approached towards developing a novel technique for extracting the best possible mask from the

frames of a video. The k cluster method used has given better results than the previous methods existing.

But the shortcoming is that it requires user input for selecting any two clusters completing the object. The

k-means cluster segmentation takes O(n) time and can be used in real time programs. Further work can bedone on this field so as to make it more robust and less dependent on the user inputs. We notice that the

noise is intolerable when the AGC is very high, failing the segmentation with contour method in such cases.

We also noticed that convex hull would not be able to help us in regaining the masks as they only give

some points lying on the outline of the object making it more vulnerable to distortions. Another

shortcoming of the k-means cluster segmentation is that if the color of the object matches with some of the

background components then the active contour will also grasp other artifacts distorting the final mask

obtained. We also infer that there is a need for morphological operations in case the background is more

natural with a lot of other objects.

We have shown that we are able to produce significant improvements in the masks by applying active

contours to the cluster segmented images. Moreover, the computational complexity is taken only by the

active contour algorithm. In future, we can build up an algorithm which will cluster the image in such a

way that the shadow along with other static objects can be detected and eliminated from the final masks.

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