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An Analysis of the Effect of Gaussian Error in Object Recognition

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dc.contributor.author Sarachik, Karen Beth en_US
dc.date.accessioned 2004-10-20T20:24:12Z
dc.date.available 2004-10-20T20:24:12Z
dc.date.issued 1994-02-01 en_US
dc.identifier.other AITR-1469 en_US
dc.identifier.uri http://hdl.handle.net/1721.1/7057
dc.description.abstract Object recognition is complicated by clutter, occlusion, and sensor error. Since pose hypotheses are based on image feature locations, these effects can lead to false negatives and positives. In a typical recognition algorithm, pose hypotheses are tested against the image, and a score is assigned to each hypothesis. We use a statistical model to determine the score distribution associated with correct and incorrect pose hypotheses, and use binary hypothesis testing techniques to distinguish between them. Using this approach we can compare algorithms and noise models, and automatically choose values for internal system thresholds to minimize the probability of making a mistake. en_US
dc.format.extent 7376380 bytes
dc.format.extent 3521585 bytes
dc.format.mimetype application/postscript
dc.format.mimetype application/pdf
dc.language.iso en_US
dc.relation.ispartofseries AITR-1469 en_US
dc.title An Analysis of the Effect of Gaussian Error in Object Recognition en_US


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