Outline
• Perceptual organization, grouping, and
segmentation
– Introduction
– Region growing
– Split-and-merge
File: week13-f.ppt
Introduction
• Segmentation
– Roughly speaking, segmentation is to partition
the images into meaningful parts that are
relatively homogenous in certain sense
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Introduction – cont.
• Why is segmentation important
– Classification algorithms in general assume that
the features are extracted only from the
objects/regions that we are interested in
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Introduction – cont.
• Why is segmentation difficult
– The first difficulty is a representation issue
• There are many different kinds of objects, textures
• Is there a representation that will apply to all the
images
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Introduction - continued
• How can we characterize all these images perceptually?
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Introduction – cont.
• Why is segmentation difficult
– The first difficulty is a representation issue
• There are many different kinds of objects, textures
• Is there a representation that will apply to all the
images
– The second difficulty is to identify first what
types of objects are present in the given input
image
– The third difficulty is to localize the boundaries
between regions
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Introduction – cont.
• But we can do the task effortless
– How have we done so?
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Introduction – cont.
• Perceptual organization
– Try to understand the principles behind
perception by observing and building models for
perceptual phenomena
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Introduction – cont.
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Introduction – cont.
• Gestalt grouping principles
– Proximity
• Objects that are close to each other tend to be grouped
together
– Similarity
• Objects that are more similar to one another tend to be
grouped together
– Closure
• Objects that form closed units tend to be grouped
together
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Introduction – cont.
• Gestalt grouping principles – continued
–
–
–
–
Good continuation
Common fate
Figure and ground
Subjective contour
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Introduction – cont.
• Problems with Gestalt principles
– They are NOT computational models
– In addition, those factors interfere each other in a
given image
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Introduction – cont.
• Computational models/implementations
– There are generally two kinds of computational
models/implementations for segmentation
• Based on homogeneity measure to group pixels with
similar attributes together
– Region growing/split-and-merge
• Based on discontinuity of attributes to detect
boundaries/contours of regions
– Active contours
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Region Growing
• Region growing
– Is a set of algorithms to group pixels with similar
attributes together
– The basic idea is to grow from a seed pixel
• At a labeled pixel, check its neighbors
– If the attributes of its neighbor is similar to the attributes of
the labeled pixel, label the neighbor
• Repeat until there is no pixel that can be labeled
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Region Growing – cont.
• A simple case
– The attribute of a pixel is its pixel value
– The similarity is given by the difference between
the two pixel values
• If the difference is smaller than a threshold, we say
they are similar
• Otherwise they are not
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Region Growing – cont.
• Recursive implementation
– Given a seed point, call the following recursive function
void RegionGrowing(IMAGE animage, LABEL labelmap, int x, int
y, int label)
if (labelmap[x][y] != 0) return;
labelmap[x][y] = label;
for each neighbor nx, ny of pixel x,y
if labelmap[nx][ny]==0
if diff(animage[x][y]-animage[nx][ny]) < threhold,
RegionGrowing(animage, labelmap, nx, ny, label)
end if
end if
end for
return
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Region Growing – cont.
• Efficient implementation
– Based on scan-line algorithm in graphics
– Each time we label a line instead of a pixel
– This procedure is much more efficient than the
recursive version
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Split and Merge
• There is also a different implementation to
partition an input image into homogenous
regions
– Start with the entire image as one region
– Then split a region into sub-regions if the
variance is larger than a threshold and merge
neighboring regions if they are similar
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Split and Merge – cont.
1. Start with the entire image as a single region
2. Pick a region R. If H(R) is false, then split the
region into four sub-regions
3. Consider any two or more neighboring subregions, R1,R2,...,Rn, in the image. If H(R1 U R2
U ... U Rn ) is true, merge the n regions into a
single region.
4. Repeat these steps until no further splits or
merges take place
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Split and Merge – cont.
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Split and Merge – cont.
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Split and Merge – cont.
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