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- W2544085142 abstract "Humans have the ability to identify shapes that have been rotated, scaled, and even partially occluded; emulating this capability is a classic problem of computer vision. This dissertation examines a particular class of computer algorithms for solving such problems, and studies the mechanisms by which we may evaluate their performance.In particular, this thesis studies the class of object recognition methods based on the Hough transform, develops a new variant of the method that is especially adapted to handling rotated and scaled objects, and formulates evaluation tools for quantifying the performance of these methods.The performance analysis presented is based on viewing the Hough class of methods in the context of signal detection theory. This approach deals in particular with two measures: the probability of correctly detecting an existing signal and the probability of a false alarm. In the Hough transform class of methods, we obtain appropriate measures for these quantities and use them to evaluate and compare the methods that we have studied." @default.
- W2544085142 created "2016-11-04" @default.
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- W2544085142 date "1996-10-03" @default.
- W2544085142 modified "2023-09-27" @default.
- W2544085142 title "A framework for developing and evaluating Hough-like object recognition methods" @default.
- W2544085142 hasPublicationYear "1996" @default.
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