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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.
Date01 May 1988
CreatorsGrimson, W. Eric L., Huttenlocher, David
Source SetsM.I.T. Theses and Dissertation
Detected LanguageEnglish
Format40 p., 5359682 bytes, 2031093 bytes, application/postscript, application/pdf

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