Robust algorithms for model-based object recognition and localization
Name
43412515-MIT.pdf
Description
Full printable version
Size
4.73 MB
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Checksum (MD5)
80cd60b835ce401daffc7be9704622af
Author(s)
Bazzi, Louay Mohamad Jamil, 1974-
Advisor(s)
Sanjoy K. Mitter.
Date Issued
1999
Publisher
Massachusetts Institute of Technology
Abstract
We consider the problem of model-based object recognition and localization in the presence of noise, spurious features, and occlusion. We address the case where the model is allowed to be transformed by elements in a given space of allowable transformations. Known algorithms for the problem either treat noise very accurately in an unacceptable worst case running time, or may have unreliable output when noise is allowed. We introduce the idea of tolerance which measures the robustness of a recognition and localization method when noise is allowed. We present a collection of algorithms for the problem, each achieving a different degree of tolerance. The main result is a localization algorithm that achieves any desired tolerance in a relatively low order worst case asymptotic running time. The time constant of the algorithm depends on the ratio of the noise bound over the given tolerance bound. The solution we provide is general enough to handle different cases of allowable transformations, such as planar affine transformations, and scaled rigid motions in arbitrary dimensions.
Description
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.
Includes bibliographical references (p. 86-87).
Subjects
Electrical Engineering and Computer Science
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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