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The Combinatorics of Heuristic Search Termination for Object Recognition in Cluttered Environments

Author(s)
Grimson, W. Eric L.
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Abstract
Many recognition systems use constrained search to locate objects in cluttered environments. Earlier analysis showed that the expected search is quadratic in the number of model and data features, if all the data comes from one object, but is exponential when spurious data is included. To overcome this, many methods terminate search once an interpretation that is "good enough" is found. We formally examine the combinatorics of this, showing that correct termination procedures dramatically reduce search. We provide conditions on the object model and the scene clutter such that the expected search is quartic. These results are shown to agree with empirical data for cluttered object recognition.
Date issued
1989-05-01
URI
http://hdl.handle.net/1721.1/6027
Other identifiers
AIM-1111
Series/Report no.
AIM-1111
Keywords
computer vision, object recognition, search, combinatorics

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