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.
Michael Boshra, Bir Bhanu, "Predicting Performance of Object Recognition", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 22, no. , pp. 956-969, September 2000, doi:10.1109/34.877519