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Presented at: IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, June 18-23, 2007 IEEE Xplore, 2007

While feature point recognition is a key component of modern approaches to object detection, existing approaches require computationally expensive patch preprocessing to handle perspective distortion. In this paper, we show that formulating the problem in a Naive Bayesian classification framework makes such preprocessing unnecessary and produces an algorithm that is simple, efficient, and robust. Furthermore, it scales well to handle large number of classes. To recognize the patches surrounding keypoints, our classifier uses hundreds of simple binary features and models class posterior probabilities. We make the problem computationally tractable by assuming independence between arbitrary sets of features. Even though this is not strictly true, we demonstrate that our classifier nevertheless performs remarkably well on image datasets containing very significant perspective changes.

Keywords: computer vision ; keypoint recognition ; naive Bayesian ; object detection ; image classification Reference CVLAB-CONF-2008-004View record in Web of Science





Author: Ozuysal, Mustafa; Fua, Pascal; Lepetit, Vincent

Source: https://infoscience.epfl.ch/record/126376?ln=en



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