Issue No. 09 - September (2000 vol. 22)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/34.877519
<p><b>Abstract</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.</p>
Bounds on recognition performance, model-based real-world object recognition, modeling data distortion, performance validation, synthetic aperture radar images, theory of performance prediction.
M. Boshra and B. Bhanu, "Predicting Performance of Object Recognition," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 22, no. , pp. 956-969, 2000.