cs 223-b l11 segmentation

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Lecture 11Lecture 11

Segmentation and GroupingSegmentation and Grouping

Gary BradskiGary Bradski

Sebastian ThrunSebastian 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.htm

*


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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


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Intro: Segmentation and Grouping

Motivation:

not for recognition

for compression

Relationship ofsequence/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 togetherbecause of some local

affinity measure

Bottom up/Top Dowon

need not be mutually

exclusive


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Biological:

Segmentation in Humans


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Biological:For humans at least, Gestalt psychology identifies several properties that result

In grouping/segmentation:


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Consequence:

Groupings by Invisible Completions

* Images from Steve Lehars Gestalt papers: http://cns-alumni.bu.edu/pub/slehar/Lehar.html

Stressing the invisible groupings:


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Consequence:




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Consequence:




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Why do these tokens belong together?

Here, the 3D nature of grouping is apparent:

Corners and creases in 3D, length is interpreted differently:

In

Out

The (in) line at the far

end of corridor mustbe longer than the (out)

near line if they measure

to be the same size


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And the famous invisible dog eating

under a tree:


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Background Subtraction


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Background Subtraction

1. Learn model of the background

By statistics ( , ); mixture of Gaussians; Adaptive filter, etc

1. Take absolute difference with current frame Pixels greater than a threshold are candidate foreground

1. Use morphological open operation to clean up point

noise.2. Traverse the image and use flood fill to measure size of

candidate regions. Assign as foreground those regions bigger than a set value.

Zero out regions that are too small.

1. Track 3 temporal modes:(1) Quick regional changes are foreground (people, moving cars);

(2) Changes that stopped a medium time ago are candidatebackground (chairs that got moved etc);

(3) Long term statistically stable regions are background.


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Background Subtraction ExampleBackground Subtraction Example


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Background Subtraction PrinciplesAt ICCV 1999, MS Research presented a study, Wallflower: Principles and Practice

of Background Maintenance, by Kentaro Toyama, John Krumm, Barry Brumitt, BrianMeyers. This paper compared many different background subtraction techniques

and came up with some principles:

P1:

P2:

P3:

P4:

P5:

cs 223-b l11 segmentation

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