The combinatorics of object recognition in cluttered environments using constrained search
Artificial Intelligence
On the Verification of Hypothesized Matches in Model-Based Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A study of affine matching with bounded sensor error
International Journal of Computer Vision
An Integrated Model for Evaluating the Amount of Data Required for Reliable Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Polynomial-Time Object Recognition in the Presence of Clutter, Occlusion, and Uncertainty
ECCV '92 Proceedings of the Second European Conference on Computer Vision
Verifying model-based alignments in the presence of uncertainty
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
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Model-Based object recognition is a fundamentnal task of Computer Vision. In this paperwe consider the performance of the popular geometric hashing (GH) algorithmfor model based recognition and, in a probabilistic setting, examine the influence of some design decisions and derive several tradeoffs between two measures of performance: reliability and time complexity. We also propose a variation of the GH algorithm, which alleviates some of its inherent problems and demonstrate its enhanced performance in experiments.