Predicting performance of object recognition

We present a method for predicting fundamental performance of object recogn ition. 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 cr iterion. The proposed method considers data distortion factors such as unce rtainty, occlusion, and clutter, in addition to model similarity. This is u nlike previous approaches, which consider only a subset of these factors. P erformance is predicted in two stages. In the first stage, the similarity b etween every pair of model objects is captured by comparing their structure s 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 boun d on the probability of correct recognition. The validity of the method is experimentally demonstrated using real synthetic aperture radar (SAR) data.