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Lecture 11
Lecture 11
Segmentation and Grouping
Segmentation and Grouping
Gary Bradski
Gary Bradski
Sebastian Thrun
Sebastian Thrun
http://robots.stanford.edu/cs223b/index.html
* Pictures from Mean Shift: A Robust Approach toward Feature Space Analysis, by D. Comaniciu and P. Meer http://www.caip.rutgers.edu/~comanici/MSPAMI/msPamiResults.html
*
2
Outline
•
Segmentation Intro
–
What and why
–
Biological
Segmentation:
•
By learning the background
•
By energy minimization
–
Normalized Cuts
•
By clustering
–
Mean Shift (perhaps the best technique to date)
•
By fitting
–
optional, but projects doing SFM should read.
Reading source: Forsyth Chapters in segmentation, available (at least this term)
http://www.cs.berkeley.edu/~daf/new-seg.pdf
3
Intro: Segmentation and Grouping
•
Motivation:
–
not for recognition
–
for compression
•
Relationship of
sequence/set of
tokens
–
Always for a goal or
application
•
Currently, no real
theory
What: Segmentation breaks an image into groups over space and/or time
Why:
Tokens are
–
The things that are grouped
(pixels, points, surface elements,
etc., etc.)
•
top down segmentation
–
tokens grouped because they lie
on the same object
•
bottom up segmentation
–
tokens belong together
because of some local
affinity measure
•
Bottom up/Top Dowon
need not be mutually
exclusive
4
Biological:
Segmentation in Humans
5
Biological:
For humans at least, Gestalt psychology identifies several properties that result
In grouping/segmentation:
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