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On the Sensitivity of the Hough Transform for Object Recognition

A common method for finding an object's pose is the generalized Hough transform, which accumulates evidence for possible coordinate transformations in a parameter space and takes large clusters of similar transformations as evidence of a correct solution. We analyze this approach by deriving theoretical bounds on the set of transformations consistent with each data-model feature pairing, and by deriving bounds on the likelihood of false peaks in the parameter space, as a function of noise, occlusion, and tessellation effects. We argue that blithely applying such methods to complex recognition tasks is a risky proposition, as the probability of false positives can be very high.

Identiferoai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/6039
Date01 May 1988
CreatorsGrimson, W. Eric L., Huttenlocher, David
Source SetsM.I.T. Theses and Dissertation
Languageen_US
Detected LanguageEnglish
Format40 p., 5359682 bytes, 2031093 bytes, application/postscript, application/pdf
RelationAIM-1044

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