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SEGMENTATION
Submitted by:
Sadhana Singh
Shri Ram Murti Smarak College of
Engg.
Segmentation and Grouping
• Motivation: not information is
evidence
• Obtain a compact
representation from an
image/motion sequence/set of
tokens
• Should support application
• Broad theory is absent at
present
• Grouping (or clustering)
– collect together tokens that
“belong together”
• Fitting
– associate a model with tokens
– issues
• which model?
• which token goes to which
element?
• how many elements in the
model?
General ideas
• tokens
– whatever we need to group
(pixels, points, surface
elements, etc., etc.)
• top down segmentation
– tokens belong together because
they lie on the same object
• bottom up segmentation
– tokens belong together because
they are locally coherent
• These two are not mutually
exclusive
Why do these tokens belong together?
Basic ideas of grouping in humans
• Figure-ground discrimination
– grouping can be seen in terms
of allocating some elements to
a figure, some to ground
– impoverished theory
• Gestalt properties
– elements in a collection of
elements can have properties
that result from relationships
(Muller-Lyer effect)
• gestaltqualitat
– A series of factors affect
whether elements should be
grouped together
• Gestalt factors
Segmentation as clustering
• Cluster together (pixels, tokens,
etc.) that belong together
• Agglomerative clustering
– attach closest to cluster it is
closest to
– repeat
• Divisive clustering
– split cluster along best
boundary
– repeat
• Point-Cluster distance
– single-link clustering
– complete-link clustering
– group-average clustering
• Dendrograms
– yield a picture of output as
clustering process continues

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segmentation

  • 1. SEGMENTATION Submitted by: Sadhana Singh Shri Ram Murti Smarak College of Engg.
  • 2. Segmentation and Grouping • Motivation: not information is evidence • Obtain a compact representation from an image/motion sequence/set of tokens • Should support application • Broad theory is absent at present • Grouping (or clustering) – collect together tokens that “belong together” • Fitting – associate a model with tokens – issues • which model? • which token goes to which element? • how many elements in the model?
  • 3. General ideas • tokens – whatever we need to group (pixels, points, surface elements, etc., etc.) • top down segmentation – tokens belong together because they lie on the same object • bottom up segmentation – tokens belong together because they are locally coherent • These two are not mutually exclusive
  • 4. Why do these tokens belong together?
  • 5.
  • 6. Basic ideas of grouping in humans • Figure-ground discrimination – grouping can be seen in terms of allocating some elements to a figure, some to ground – impoverished theory • Gestalt properties – elements in a collection of elements can have properties that result from relationships (Muller-Lyer effect) • gestaltqualitat – A series of factors affect whether elements should be grouped together • Gestalt factors
  • 7.
  • 8. Segmentation as clustering • Cluster together (pixels, tokens, etc.) that belong together • Agglomerative clustering – attach closest to cluster it is closest to – repeat • Divisive clustering – split cluster along best boundary – repeat • Point-Cluster distance – single-link clustering – complete-link clustering – group-average clustering • Dendrograms – yield a picture of output as clustering process continues

Editor's Notes

  1. A disturbing possibility is that they all lie on a sphere -- but then, if we didn’t know that the tokens belonged together, where did the sphere come from?
  2. A driving force behind the gestalt movement is the observation that it isn’t enough to think about pictures in terms of separating figure and ground (e.g. foreground and background). This is (partially) because there are too many different possibilities in pictures like this one. Is a square with a hole in it the figure? or a white circle? or what?
  3. The famous Muller-Lyer illusion; the point is that the horizontal bar has properties that come only from its membership in a group (it looks shorter in the lower picture, but is actually the same size) and that these properties can’t be discounted--- you can’t look at this figure and ignore the arrowheads and thereby make the two bars seem to be the same size.