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Image Segmentation

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The term image segmentation refers to the partition of an image ... Boundaries of each segment should be smooth, not ragged, and should be spatially accurate. ... – PowerPoint PPT presentation

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Title: Image Segmentation


1
Image Segmentation
Dr. Ramprasad Bala Computer and Information
Science UMASS Dartmouth CIS 465 Topics in
Computer Vision
2
Image Segmentation
  • The term image segmentation refers to the
    partition of an image into a set of regions that
    covers it.
  • The goal in many tasks is for the regions to
    represent some meaningful area.
  • Regions may also be defined as group of pixels
    having both a border and a particular shape such
    as a circle, ellipse or polygon.

3
Image Segmentation (IS)
  • Segmentation has two objectives
  • The first is to decompose the image into parts
    for further analysis.
  • The second is to perform a change in
    representation.

4
First Objective
  • In simple cases, the environment might be well
    enough controlled so that the segmentation
    process reliably extracts only the parts that
    needs to be analyzed.
  • In more complex cases, a great deal of domain
    knowledge may be required. Such as extracting a
    complete road network from a grayscale image.

5
Second Objective
  • The pixels of the image of the image must be
    organized into higher-level units that are either
    more meaningful or more efficient for further
    analysis.
  • A crucial issue is the development of a bottom-up
    approach that is domain independent (usually this
    is very difficult).

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Identifying Regions
  • Regions of an IS should be uniform and
    homogeneous with respect to some characteristic -
    gray-level, color or texture.
  • Region interiors should be simple and without
    many holes.
  • Adjacent regions of a segmentation should have
    significantly different values with respect to
    the characteristic on which they are uniform.
  • Boundaries of each segment should be smooth, not
    ragged, and should be spatially accurate.

8
Clustering Methods
  • Clustering in Pattern Recognition is the process
    of partitioning a set of pattern vectors into
    subsets called clusters.
  • The general problem in clustering is to partition
    a set of vectors having similar values.
  • For example, consider pattern vectors of two real
    numbers. Then clustering could consist of finding
    subsets of points that are close to each other
    in Euclidean two-space.

9
Classical Clustering Algorithms
  • In image analysis, the vectors represent pixels
    or sometimes small neighborhoods around pixels.
  • The components of these vectors could be
  • Intensity values
  • RGB values and color properties derived from them
  • Calculated properties
  • Texture measurements.
  • Any feature that can be associated with a pixel
    can be used to group them.

10
Iterative K-Means Clustering
11
Iterative K-Means Clustering
  • This algorithm is guaranteed to terminate, but it
    may not find the global optimum.
  • Step 2 may be modified to partition the set of
    vectors into K random clusters and then compute
    their means.
  • Step 5 can be modified to stop after the
    percentage of vectors that change clusters in a
    given iteration is small.

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Isodata Clustering
  • Isodata clustering is another iterative algorithm
    that uses a split-and-merge technique.
  • Assume that there are K clusters C1, C2, ,Ck
    with means m1, m2,,mk and Sk be the covariance
    matrix of cluster k.

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Isodata clustering algorithm
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Histogram-Based Method
  • Intuitively Histograms represent regions of
    common features gray-scale or color.
  • Finding valleys and segmenting based on peaks can
    be a simple solution.
  • Finding valleys automatically is a non-trivial
    problem. (Histogram mode seeking).
  • May need further processing using domain
    knowledge.

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Ohlanders Recursive Algorithm
  • Ohlander, Price and Reddy (1978) proposed a
    recursive approach to the histogram-based
    clustering.
  • The idea is to perform histogram mode seeking
    first on the whole image and then on each of the
    regions obtained from the resultant clusters,
    until regions are obtained that cannot be
    decomposed any further.

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Region Growing
  • Instead of partitioning the image, a region
    grower begins at one position and attempts to
    grow each region until the pixels being compared
    are too dissimilar to the region to add them.
  • Usually, a statistical test is performed to
    decide if it is the case.

23
Haralicks region growing
  • Assumes a region is a set of connected pixels
    with the same population mean and variance.
  • Let R be a region of N pixels neighboring a pixel
    with gray-level intensity y.
  • Let X and S2 be respectively the mean and the
    variance of the region R.
  • The statistic T is computed as follows

24
  • If T is small then y is added to the region and
    the new mean and variance updated.
  • If T is too high y is not likely to have risen
    from the population of pixels in R.
  • To give a precise meaning to the notion of too
    high a difference, we can use an a-level
    statistical significance test.
  • The fraction a represents the probability that a
    T statistic with N-1 degrees of freedom will
    exceed tN-1(a).
  • If the observed T is larger than tN-1(a) then we
    declare the difference to be significant.

25
If the pixel y and segment really came from the
same population, the probability that the test
provides an incorrect answer is a . a is a user
specified parameter. tN-1(a) is higher for small
degrees of freedom and lower for larger degrees
of freedom.
26
Representing Regions
  • Each algorithm that produces a set of image
    regions has to have a way to store them.
  • Some of the methods are -
  • Overlays on the original image
  • Labeled images
  • Boundary encoding
  • Quad-tree structures
  • Property tables

27
Overlays
  • An overlay is a method of showing the regions
    computed from an image by overlaying some color
    or colors on top of the original image.
  • To show region segmentation, one could convert
    the pixels of the region borders to white and
    display the transformed gray-tone image.
    Sometimes more than one-pixel width borders are
    used.

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Labeled Images
  • Labeled images are good intermediate
    representation for regions that can also be used
    in further processing.
  • The idea is to assign each detected region a
    unique identifier (an integer) and create image
    where all the pixels of a region will have a
    unique identifier as pixel.

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Boundary Coding
  • Regions can also be represented by their
    boundaries in a data structure instead of an
    image.
  • The simplest form is just a linear list of border
    pixels.
  • A variation of the list of points is the Freeman
    chain code, which codes the information from the
    list of points at any desired quantization and
    uses less space than the original point list.

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Quadtrees
  • An image is broken into regions.
  • Each region of interest would be represented by a
    quadtree structure.
  • Each node of a quadtree represents a square
    region in the image and can one of three labels
    full, empty or mixed.
  • If the region is labeled full then every pixel in
    the region is of interest.
  • Only mixed regions have children.

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Property Tables
  • Sometimes we want to represent a region by its
    extracted properties rather than by its pixels.
    This is called a property table.
  • It is a table in a relational database sense that
    has a row for each region in the image and a
    column for each property of interest.
  • Properties can be size, shape, intensity, color
    or texture of the region. Area, ratio of
    minor-to-major axis of the best-fitting ellipse,
    two main colors and more texture measures can all
    be used.

36
Identifying Contours
  • Tracking Existing Region Boundaries
  • Once regions have been segmented, the border
    pixels for each region can be extracted.
  • The border will be a set of pixels that envelopes
    a region.
  • The algorithm is quite straight forward.
  • Single pass would do.

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The Canny Edge Detector
41
The Canny Edge Detector
  • The Canny ED is very useful for finding contours.
  • It takes as an input s which is used to apply a
    Gaussian filter on the image as the first step.
  • Gradient magnitude and direction are found on the
    smoothed image.
  • Gradient direction is used to thin edges by
    suppressing any pixel response that is not higher
    than the two neighboring pixels on either side of
    the direction. This is called non-maximal
    suppression.

42
  • The 2 8-neighbors of a pixel that are to be
    compared are found by rounding off the computed
    gradient to yield one pixel on either side of the
    center pixel.
  • Once the gradient magnitudes are thinned. High
    magnitude contours are tracked.
  • In the final stage continuous contour segments
    are sequentially followed.
  • Contour following is initiated only on edge
    pixels where gradient magnitude meets a high
    threshold however once started a contour may be
    followed through pixels whose gradient magnitude
    are lower than the threshold.

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46
  • Segmentation Part II
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