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Predicting performance of object recognition

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2 Author(s)
Boshra, M. ; AuthenTec Inc., Melbourne, FL, USA ; Bhanu, B.

We present a method for predicting fundamental performance of object recognition. We assume that both scene data and model objects are represented by 2D point features and a data/model match is evaluated using a vote-based criterion. The proposed method considers data distortion factors such as uncertainty, occlusion, and clutter, in addition to model similarity. This is unlike previous approaches, which consider only a subset of these factors. Performance is predicted in two stages. In the first stage, the similarity between every pair of model objects is captured by comparing their structures as a function of the relative transformation between them. In the second stage, the similarity information is used along with statistical models of the data-distortion factors to determine an upper bound on the probability of recognition error. This bound is directly used to determine a lower bound on the probability of correct recognition. The validity of the method is experimentally demonstrated using real synthetic aperture radar (SAR) data

Published in:

Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:22 ,  Issue: 9 )

Date of Publication:

Sep 2000

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