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On the Sensitivity of the Hough Transform for Object Recognition

Author(s)
Grimson, W. Eric L.; Huttenlocher, David
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Abstract
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.
Date issued
1988-05-01
URI
http://hdl.handle.net/1721.1/6039
Other identifiers
AIM-1044
Series/Report no.
AIM-1044
Keywords
Hough transform, object recognition

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