Finding parameters for Bayesian association
نویسندگان
چکیده
7 In feature based object recognition an essential step is finding the locations of the 8 local object features in the image by maximizing a feature similarity function. We 9 study the mapping of a feature similarity function to a likelihood function that 10 reflects the actual probabilities related to associating the object point in a certain 11 location in the image. We adopt probabilistic approach for the feature matching, 12 and suggest a model that contains two adjustable parameters: a steepness parameter 13 of the likelihood function and a threshold parameter related to the probability of 14 the feature appearing in the image. We analyze the effect of the parameters to the 15 feature association, and suggest practical rules for setting the parameters according 16 to the available information, to maximize the number of correct feature associations. 17
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