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An Analysis of the Effect of Gaussian Error in Object Recognition

Author(s)
Sarachik, Karen Beth
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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.
Date issued
1994-02-01
URI
http://hdl.handle.net/1721.1/7057
Other identifiers
AITR-1469
Series/Report no.
AITR-1469

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  • AI Technical Reports (1964 - 2004)

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