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Uncertainty Propagation in Model-Based Recognition

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dc.creator Jacobs, D.W.
dc.creator Alter, T.D.
dc.date 2004-11-19T17:17:43Z
dc.date 2004-11-19T17:17:43Z
dc.date 1995-02-01
dc.date.accessioned 2013-10-09T02:49:15Z
dc.date.available 2013-10-09T02:49:15Z
dc.date.issued 2013-10-09
dc.identifier AIM-1476
dc.identifier http://hdl.handle.net/1721.1/7337
dc.identifier.uri http://koha.mediu.edu.my:8181/xmlui/handle/1721
dc.description Building robust recognition systems requires a careful understanding of the effects of error in sensed features. Error in these image features results in a region of uncertainty in the possible image location of each additional model feature. We present an accurate, analytic approximation for this uncertainty region when model poses are based on matching three image and model points, for both Gaussian and bounded error in the detection of image points, and for both scaled-orthographic and perspective projection models. This result applies to objects that are fully three- dimensional, where past results considered only two-dimensional objects. Further, we introduce a linear programming algorithm to compute the uncertainty region when poses are based on any number of initial matches. Finally, we use these results to extend, from two-dimensional to three- dimensional objects, robust implementations of alignmentt interpretation- tree search, and ransformation clustering.
dc.format 22 p.
dc.format 603479 bytes
dc.format 923764 bytes
dc.format application/octet-stream
dc.format application/pdf
dc.language en_US
dc.relation AIM-1476
dc.subject Model Based Recognition; 3-D Recognition; Error Models: Alignment; Scaled Orthographic Projection; Linear Programming
dc.title Uncertainty Propagation in Model-Based Recognition


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