Learning Center
Plans & pricing Sign in
Sign Out

Image Segmentation (PowerPoint download)

VIEWS: 154 PAGES: 44

									Image Segmentation

  CIS 601 Fall 2004
  Longin Jan Latecki
        Image Segmentation
• Segmentation divides an image into its
  constituent regions or objects.
• Segmentation of images is a difficult task in
  image processing. Still under research.
• Segmentation allows to extract objects in
• Segmentation is unsupervised learning.
• Model based object extraction, e.g.,
  template matching, is supervised learning.
           What it is useful for
• After a successful segmenting the image, the contours of
   objects can be extracted using edge detection and/or
   border following techniques.
• Shape of objects can be described.
• Based on shape, texture, and color objects can be
• Image segmentation techniques are extensively used in
   similarity searches, e.g.:
     Segmentation Algorithms
• Segmentation algorithms are based on one of
  two basic properties of color, gray values, or
  texture: discontinuity and similarity.
• First category is to partition an image based on
  abrupt changes in intensity, such as edges in an
• Second category are based on partitioning an
  image into regions that are similar according to a
  predefined criteria. Histogram thresholding
  approach falls under this category.
Domain   spaces
  spatial domain (row-column (rc) space)

  histogram spaces

  color space

  texture space

  other complex feature space
      Clustering in Color Space
1. Each image point is mapped to a point in a color
space, e.g.:

Color(i, j) = (R (i, j), G(i, j), B(i, j))

It is many to one mapping.

2. The points in the color space are grouped to clusters.

3. The clusters are then mapped back to regions in the
Original pictures                          segmented pictures

Mnp: 30, percent 0.05, cluster number 4

Mnp : 20, percent 0.05, cluster number 7
       Displaying objects in the
          Segmented Image
• The objects can be distinguished by
  assigning an arbitrary pixel value or
  average pixel value to the pixels belonging
  to the same clusters.
Thus, one needs clustering algorithms
for image segmentation.

Homework 8:

Implement in Matlab and test on some example images
the clustering in the color space.
Use Euclidean distance in RGB color space.
You can use k-means, PAM, or some other clustering
Links to k-means, PAM, data normalization

Test images: rose, plane, car, tiger, landscape
      Segmentation by Thresholding

• Suppose that the gray-level histogram
  corresponds to an image f(x,y) composed of
  dark objects on the light background, in such a
  way that object and background pixels have
  gray levels grouped into two dominant modes.
  One obvious way to extract the objects from the
  background is to select a threshold ‘T’ that
  separates these modes.
• Then any point (x,y) for which f(x,y) < T is called
  an object point, otherwise, the point is called a
  background point.
Gray Scale Image Example

  Image of a Finger Print with light background
Segmented Image

   Image after Segmentation
In Matlab histograms for images can be
constructed using the imhist command.

I = imread('pout.tif');
figure, imshow(I);
figure, imhist(I) %look at the hist to get a threshold, e.g., 110
BW=roicolor(I, 110, 255); % makes a binary image
figure, imshow(BW) % all pixels in (110, 255) will be 1 and white
                          % the rest is 0 which is black

roicolor returns a region of interest selected as those pixels in I that
match the values in the gray level interval.
BW is a binary image with 1's where the values of I match the values
of the interval.
    Thresholding Bimodal Histograms
• Basic Global Thresholding:
  1)Select an initial estimate for T
  2)Segment the image using T. This will produce two
   groups of pixels. G1 consisting of all pixels with gray
   level values >T and G2 consisting of pixels with values
  3)Compute the average gray level values mean1 and
   mean2 for the pixels in regions G1 and G2.
  4)Compute a new threshold value
          T=(1/2)(mean1 +mean2)
  5)Repeat steps 2 through 4 until difference in T in
   successive iterations is smaller than a predefined
   parameter T0.
Gray Scale Image - bimodal

      Image of rice with black background
          Segmented Image

Image histogram of rice   Image after segmentation
Basic Adaptive Thresholding:

Images having uneven illumination makes it difficult
to segment using histogram,
this approach is to divide the original image
into sub images
and use the thresholding process
to each of the sub images.
        Multimodal Histogram
• If there are three or more dominant modes in the
  image histogram, the histogram has to be
  partitioned by multiple thresholds.

• Multilevel thresholding classifies a point (x,y) as
   belonging to one object class
  if T1 < (x,y) <= T2,
  to the other object class
  if f(x,y) > T2
  and to the background
  if f(x,y) <= T1.
     Thresholding multimodal histograms

• A method based on
Discrete Curve Evolution
to find thresholds in the histogram.

• The histogram is treated as a polyline
  and is simplified until a few vertices remain.
• Thresholds are determined by vertices that are local
       Discrete Curve Evolution (DCE)

It yields a sequence: P=P0, ..., Pm

Pi+1 is obtained from Pi by deleting the vertices of Pi
that have minimal relevance measure

K(v, Pi) = |d(u,v)+d(v,w)-d(u,w)|

         v                            v
                          >                           w
                      w         u

Gray Scale Image - Multimodal

          Original Image of lena
Multimodal Histogram

      Histogram of lena
              Segmented Image

Image after segmentation – we get a outline of her face, hat, shadow etc
Color Image - bimodal

  Colour Image having a bimodal histogram

Histograms for the three colour spaces
       Segmented Image

Segmented image, skin color is shown
           Split and Merge
• The goal of Image Segmentation is to find
  regions that represent objects or
  meaningful parts of objects. Major
  problems of image segmentation are result
  of noise in the image.
• An image domain X must be segmented in
  N different regions R(1),…,R(N)
• The segmentation rule is a logical
  predicate of the form P(R)
• Image segmentation with respect to
  predicate P partitions the image X into
  subregions R(i), i=1,…,N such that

  X = i=1,..N U R(i)
  R(i) ∩ R(j) = 0 for I ≠ j
  P(R(i)) = TRUE for i = 1,2,…,N
  P(R(i) U R(j)) = FALSE for i ≠ j
• The segmentation property is a logical
  predicate of the form P(R,x,t)
• x is a feature vector associated with region
• t is a set of parameters (usually
  thresholds). A simple segmentation rule
  has the form:
            P(R) : I(r,c) < T for all (r,c) in R
• In the case of color images the feature
  vector x can be three RGB image
  components (R(r,c),G(r,c),B(r,c))
• A simple segmentation rule may have the
  P(R) : (R(r,c) <T(R)) && (G(r,c)<T(G))&&
           (B(r,c) < T(B))
     Region Growing (Merge)
• A simple approach to image segmentation
  is to start from some pixels (seeds)
  representing distinct image regions and to
  grow them, until they cover the entire
• For region growing we need a rule
  describing a growth mechanism and a rule
  checking the homogeneity of the regions
  after each growth step
           Region Growing
• The growth mechanism – at each stage k
  and for each region Ri(k), i = 1,…,N, we
  check if there are unclassified pixels in the
  8-neighbourhood of each pixel of the
  region border
• Before assigning such a pixel x to a region
  Ri(k),we check if the region homogeneity:
  P(Ri(k) U {x}) = TRUE , is valid
    Region Growing Predicate
The arithmetic mean m and standard deviation std of a
region R having n =|R| pixels:

        1                            1
m( R )   I ( r , c )   std ( R)           R     ( I (r , c)  m( R))2
        n ( r ,c )R                n  1 ( r ,c )

The predicate
P: |m(R1) – m(R2)| < k*min{std(R1), std(R2)},
is used to decide if the merging
of the two regions R1, R2 is allowed, i.e.,
if |m(R1) – m(R2)| < k*min{std(R1), std(R2)},
two regions R1, R2 are merged.
• The opposite approach to region growing is
  region splitting.
• It is a top-down approach and it starts with the
  assumption that the entire image is
• If this is not true, the image is split into four sub
• This splitting procedure is repeated recursively
  until we split the image into homogeneous
• If the original image is square N x N, having
  dimensions that are powers of 2(N = 2n):
• All regions produced but the splitting algorithm
  are squares having dimensions M x M , where
  M is a power of 2 as well.
• Since the procedure is recursive, it produces an
  image representation that can be described by a
  tree whose nodes have four sons each
• Such a tree is called a Quadtree.


R0              R1

R2                                       R1

                 R00   R01   R02   R04
• Splitting techniques disadvantage, they
  create regions that may be adjacent and
  homogeneous, but not merged.
• Split and Merge method is an iterative
  algorithm that includes both splitting and
  merging at each iteration:
             Split / Merge
• If a region R is inhomogeneous
  (P(R)= False) then is split into four sub
• If two adjacent regions Ri,Rj are
  homogeneous (P(Ri U Rj) = TRUE), they
  are merged
• The algorithm stops when no further
  splitting or merging is possible
             Split / Merge
• The split and merge algorithm produces
  more compact regions than the pure
  splitting algorithm
• 3D – Imaging : A basic task in 3-D image
  processing is the segmentation of an image
  which classifies voxels/pixels into objects or
  groups. 3-D image segmentation makes it
  possible to create 3-D rendering for multiple
  objects and perform quantitative analysis for the
  size, density and other parameters of detected
• Several applications in the field of Medicine like
  magnetic resonance imaging (MRI).
Results – Region grow
Results – Region Split
Results – Region Split and

To top