A Local Algorithm for Real-Time Junction Detection in Contour Images
CAIP '01 Proceedings of the 9th International Conference on Computer Analysis of Images and Patterns
Performance evaluation of corner detectors using consistency and accuracy measures
Computer Vision and Image Understanding
Local invariant feature detectors: a survey
Foundations and Trends® in Computer Graphics and Vision
Performance evaluation of corner detectors using consistency and accuracy measures
Computer Vision and Image Understanding
Image corner detection using hough transform
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II
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We present a recasting of corner detection to a problem in statistical pattern recognition which we then address with a simple feedforward neural network. The resulting classifier is a robust, threshold-free corner detector which labels with (approximate) Bayesian posterior probabilities; this is in contrast to conventional feature detectors which produce binary labels contingent on a heuristically set threshold. We have generated the training data for our classifier using a grey-level model of the corner feature which permits sampling of the pattern space at arbitrary density as well as providing a validation set to assess the classifier generalisation. Results are presented for real images and the robustness illustrated over a well-known state-of-the-art conventional corner detector.