Heap graph, software birthmark, frequent sub graph mining.

IOSR Journal of Computer Engineering (IOSR-JCE)
e-ISSN: 2278-0661,p-ISSN: 2278-8727, Volume 17, Issue 3, Ver. II (May – Jun. 2015), PP 44-49
www.iosrjournals.org
DOI: 10.9790/0661-17324449 www.iosrjournals.org 44 | Page
Motion-Based Foreground Image Segmentation
Vinayak G Birdar
Computer Sciemnce And Engineering Bkit Bhalki 585328
Abstract: A variety of useful applications demonstrate the need for precise motion-based segmentation of
image data. Video compression techniques rely heavily on accurate and efficient representation of spatio-
temporal regions possessing similar motion models. In addition a variety of cinematographic effects are
potentially possible with successful separation of a moving object’s image from an arbitrary background.
Computer vision systems often depend on the ability to distinguish or describe a moving object in an image
sequence.
This paper proposes a methodology, which separates a moving foreground from a stationary background in a
general video sequence. It replaces or removes moving persons or objects in motion pictures. The problem is
motivated primarily by the need for replacing or removing moving persons or objects in motion pictures. The
main complication exists in this problem is the issue of occlusions with the background. Consider a speaker
that moves their hand behind a stationary background object such as a desk or chair. Such occlusions represent
a challenge and are not examined in this approach. In addition to ignoring occlusions, the following analysis
makes other assumptions about a given image sequence. Lighting and color changes should not be present to a
high degree in the video being examined. Furthermore, the moving object should be constrained towards the
interior of the image.
Keywords: Image, image segmentation, motion estimator, noise estimator, recursive tracer, Block -matching
algorithm.
I. Introduction
Due to rapid progress in microelectronics and computer technology together with the creation of
networks operating with various channels capacities, the last decade has seen the emerging of new multimedia
applications such as Internet multimedia, video on demand (VOD), interpersonal communications (Video
Conference, Video phone) & Digital Library. The importance of visual communications has increased
tremendously. In addition however, motion-based segmentation also finds a niche in the realms of the video
compression and computer vision.
In the analysis of objects in images it is essential that we can distinguish between the objects of interest
and “the rest”. The later group is also referred to as background. The techniques that are used to find the objects
of interest are usually referred to as segmentation technique i.e. segmenting the foreground from background.
Motion is powerful cue used by human being and animals to extract object of interest from background of
irreverent detail. In imaging applications motion arises from a relative displacement between the sensing system
and scene being viewed such as in robotic application, autonomous navigation and dynamic scene analysis. The
task of image segmentation is to classify pixels as belonging to foreground or background objects. A common
approach is to use pixel threshold, which make decision based on local pixel information that can be effective
when pixel intensities are clearly above or below threshold. Thresholding produces a segmentation that yields all
pixels that are belongs to the objects or objects of interest in an image.
The system is consisting of noise estimator, motion estimator, and recursive motion tracer. Selection of
a motion estimator model represents the first step in the problem. Regardless of motion estimator, careful
attention must be paid to noise effects when estimating motion. Faulty motion vectors due to image noise can
lead to visually unpleasant effects such as isolated background blocks in the resulting segmented image. Noise-
reduction filters may be used to alleviate this problem. Another method is to examine the resulting mean-squared
error of known zero-motion vector regions. Any error must be due solely to noise and thus this provides
information about the noise in a particular image sequence.
Accurate knowledge of all the motion vectors in a sequence theoretically provides the means to segment
the images into pixels associated with a moving object and pixels associated with a rigid background. The
algorithm for tracing motion vectors throughout the sequences is highly recursive and can be computationally
expensive, depending on the number of non-zero motion vectors present. A video scene to be segmented should
be motion traced both forwards and backwards temporally.

II. Objective Of The Study
The objective of this work is to develop an algorithm that separates moving foreground from a stationary
background in a general video sequence. This problem is motivated primarily by the need for replacing or
removing a moving object in a motion picture. As mentioned above, however, motion-based segmentation also
finds a niche in the realms of video compression and computer vision. Selection of a motion estimator model
represents the first step in the problem. Gradient-based methods such as optical flow have shown high
performance but generally come with increased computational overhead than block-based matching. The
disadvantage of block methods is an expected loss of sharpness at edge regions marking the boundary between
foreground and background.
Regardless of motion estimator, careful attention must be paid to noise effects when estimating motion.
Faulty motion vectors due to image noise can lead to visually unpleasant effects such as isolated background
blocks in the resulting segmented image. Noise-reduction filters may be used to alleviate this problem. Another
method is to examine the resulting mean-squared error of known zero-motion vector regions. Any error must be
due solely to noise and thus this provides information about the noise in a particular image sequence. Accurate
knowledge of all the motion vectors in a sequence theoretically provides the means to segment the images into
pixels associated with a moving object and pixels associated with a rigid background. The algorithm for tracing
motion vectors throughout the sequences is highly recursive and can be computationally expensive, depending on
the number of non-zero motion vectors present.
A video scene to be segmented should be motion traced both forwards and backwards temporally. An
interesting and difficult complication to this problem is the issue of occlusions with the background.
III. Implementation
The creation of segmentation system is accomplished in three stages as shown in fig 1.
Noise estimation
Moving vector detection
Tracing the moving vector
During the first stage the noise parameter are computed using least mean square error algorithm the
motion vector between a pair of successive frames are computed without considering noise. The mean–squared
error of regions that have known zero motion vectors must be solely a result of noise in the image sequence. The
mean and standard deviation of the resulting collection of mean-squared error is computed and some number of
standard deviation above the mean represent a threshold value. Using block-matching method carries the process
of moving vector detection for motion parameter. Third stage function is to distinguish regions that are moving
in any frame. Tracing motion vectors lends itself naturally to a recursive solution.
3.1 Noise Estimator
Before the start of motion estimation, an accurate
estimation of the mean-squared error due to nose
in pixel intensities must be obtained [7]. With this
information, incorrect non-zero motion vectors
may be discarded before they are traced. Based on
the tracing algorithm used, the rejection of these
vectors can have important consequences for the
resulting visual quality. The noise estimation is
computed in a straightforward way. Vectors
between a pair of successive frames are computed
without considering noise. Any mean squared error
of regions along with non-zero motion vectors is a
result of noise in the image sequence. The mean
and standard deviation of the resulting collection of mean-squared errors is computed, and some number of
standard deviations above the mean represents a threshold value.
In the calculation of motion vector, only minimum mean-squared errors that fall below this threshold can be
considered true non-zero motion vectors. This method relies on the assumption that a large number of zero-
motion regions can be found before considering noise. Sometimes the images are noisy; in that case the
collection of data points for threshold estimation is small.

3.2 Motion Estimation
A block-matching motion estimator is used to calculate motion vectors over each pair of images in the sequence
[1, 5]. Since high accuracy in the motion vectors is desired, the estimator performs a full-search over the
window. The minimum mean-squared error criterion gives the best block match. The choice of the block size
will have a great impact on results. A smaller block size will tend to produce more false motion vectors despite
any noise estimation, but will result in finer edge definition in the resulting segmented image. Larger block sizes
have coarser edges but are less plagued by noise effects. Furthermore the number of computations necessary for
motion tracing goes down as the block size increases since there are fewer motion vectors to analyze. For the
images analyzed (144 x 176 pixels) a block size of 8 pixels is used and seems to be a reasonable choice. Calculate
Motion vectors over each pair of frames in sequence. The estimator performs a full search over the window.
Block-Matching Procedure
The basic idea behind block–matching procedure is depicted in the figure-4 where the displacement for a pixel
(n1,n2) in frame k (present frame) is determined by considering an N1XN2 block centered about a pixel (n1,n2)
and searching frame k+1 for the location of best matching block of same size. The process differ in (1) Matching
criteria (2) Search strategy (3) Determination of block size. Uses Maximum Matching pixel count (MPC). Block
size is calculated by doing continuous testing and taking combination of frame sizes with different frame skips.
The matching of the blocks can be quantified according to various criteria including the maximum cross-
correlation, the minimum mean square error (MMES), the minimum mean absolute difference (MAD) and
maximum matching pel count (MPC).
Block Size: The selection of appropriate block is essential for any block based motion estimation. There are
conflicting requirements on the size of the search block. If the blocks are too small, a match may be established
between block containing similar gray level patterns, which are unrelated in the sense of motion. On the other
hand, if the blocks are too large, then actual motion vectors may vary within a block, violating the assumption of
a single motion vector per block. The block size for the proposed design is calculated by performing continuous
testing and taking different combination of frame sizes with different frame skips.
3.3 Motion Tracer
This block represents the heart of the segmentation process [11] as shown in figure-5. Its function is to
distinguish regions that are moving in any frame or have moved at any time throughout the sequence of images.
For instance, in a complicated moving object such as a person, a hand or shoulder may only move slightly
between a single pair of frames and will be stationary in all other frames. Ideally the tracer recognizes this and
segments the region over all the frames, not just the frames in which it moved. Tracing motion vectors lends itself
naturally to a recursive solution. Each block with non-zero motion vectors in each frame represents a “seed” call
to the tracing function.

The figure-6 illustrates a moving block, which translates into a region corresponding to four blocks. The tracing
process begins with a seed call where the seed block will move into as many as four other blocks, and each of
these blocks is recursively called by the tracing function. The purpose of the tracing function is simply to identify
the appropriate moving pixels based on the motion vectors and block regions. Motion tracing has a
straightforward solution only in a one direction temporally. In other words, tracing must be done in both the
forwards and reverse temporal directions for best segmentation results.
Figure-7 shows three varieties of the tracing process. The first case represents the fully general approach. For
any moving block only the pixels corresponding to that moving block are associated with motion, but all four
regions impinged by the block are seeded to the successive tracing call. This is the most accurate approach but
also the most computationally burdensome. The second approach is to seed all four blocks as well, but to treat all
pixels within the four seeded blocks as having moved rather than just the actual moving pixels itself. A final
approach is to mark all moving pixels as in the general case, but to only seed the block corresponding to
maximum overlap. If there are equal overlaps, then multiple blocks are seeded. Although this variation only
approximates the tracing problem, it can be much faster since each trace call usually only seeds one recursive call
rather than four. In the most general case, the tracing algorithm runs slow. For improved speed, motion vectors
are computed not between each frame but between every n frames and tracing is done on this smaller set of
motion vectors. This explains the use of the optional interpolation estimator at the end of the block diagram in
figure-1. This interpolation can improve temporal resolution that may be lost by skipping every n frames. The
current implementation uses n=5 and does not use interpolation to increase temporal resolution

IV. Experimental Results:
The figure-8 shows the input image frame Sequence from news.qcif, various image sequences are
considered for regress testing of the implemented design, the image sequences are classified depending upon the
frame sequence and images are chooses depending upon the level intensities and frame size and frame skips.
Design is being tested for both gray level images as well as color image sequence. The figure-9 shows the
segmentation of input frame sequence with number of frames #6 and frame skips #5.
V. Conclusions
The methodology proposed here performs an efficient segmentation of moving foreground object from a
given image sequence with a still background. From the result obtained it can be observed with the increase in
frame size and keeping constant frame skip, the clarity of the segmentation is increased and results in higher
resolution of segmentation because of the difference between two neighboring frame is quite less. A smaller
threshold value in this case has not been found effective due to excessive loss of true motion information. Mean-
squared error approach.
References
[1]. B. Han and L. Davis, On-Line Density-Based Appearance Modeling for Object Tracking, ICCV 2005.
[2]. Mittal and N. Paragios, Motion-based Background Substraction using Adaptive Kernel Density Estimation,CVPR, 2004
[3]. Ludwig Schwardt “The Least-Mean-Square Algorithm” University of Stellenbosch, 6th May 2004
[4]. P.R. Giaccone and G.A. Jones. “Segmentation of Global Motion using Temporal Probabilistic Classification.” Proceedings of
British Machine Vision Conference, pages 619-628, University of Southampton, 1998.
[5]. Wang and Adelson. "Spatio-Temporal Segmentation of Video Data." Proceedings of the SPIE: Image and Video Processing II,
vol. 2182, San Jose, CA, February 1994
[6]. Wang and Adelson. "Representing Moving Images with Layers." IEEE Transactions on Image Processing, vol. 3, no. 5, September

1994.
[7]. Ioannis Kompatsiaris George Mantzaras and Michael G Strintzis “Spatiotemporal segmentation and tracking of objects in color
image sequences” information processing laboratory Electrical and computer Engineering Department Aristotle University of
Thessaloniki Greece.
[8]. Farneback G. “Motion Based Segmentation of Image Sequence”, Linkoping University, SE-581 83 Linkoping, Sweden, 19996.
LiTH-ISY-EX-1596.
[9]. Jnhui Pan Shipeng Li, Ya-Qin Zhang,”Automatic extraction of moving objects using multiple features and multiple frames”
Tsinghua University Microsoft Research, China.
[10]. Ludwig Schwardt “The Least-Mean-Square Algorithm” University of Stellenbosch, 6th
May 2004
[11]. Jong Bae Kim and Hang Joon Kim “Efficient Region-Based Motion Segmentation or a Video Monitoring system” Artificial
Intelligence (AI) Research Group, Dept of Computer Engineering Kyungpook National University 1370 Sangyuk-Dong, Puk-gu,
Daegu, 702-701, Korea.
[12]. Hwang-Seok Oh and Heung-Kyu Lee, Kajeong-dong, Yusong-gu, Taejon “Block-Matching Algorithm based on an adaptive
reduction of the search area of the motion estimation” Virtual Reality Research and development center, ETRI, 161.
[13]. Y. Ted Wu, “Image Segmentation The first step in 3D imaging” Able software corporation,
[14]. Peter Dornbach “Implementation of Bi-directional ray tracing algorithm” Technical University of Budapest, Dept of Control
Engineering & Information Technology, Hungery.
[15]. Mohamad Irfan Kazim “ Block Matching techniques of Motion Estimation for object tracing”
[16]. R.C.Gonzalez, R.E.Woods “ Digital Image Processing”, second edition, Pearson Education 2002.

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