Corner detection and curve representation using cubic B-spline
Computer Vision, Graphics, and Image Processing
SUSAN—A New Approach to Low Level Image Processing
International Journal of Computer Vision
International Journal of Computer Vision
Robust Image Corner Detection Through Curvature Scale Space
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision and Image Understanding
Evaluation of Interest Point Detectors
International Journal of Computer Vision - Special issue on a special section on visual surveillance
Using Simple Decomposition for Smoothing and Feature Point Detection of Noisy Digital Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Ridge's corner detection and correspondence
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Corner Detection Using Support Vector Machines
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
An Axiomatic Approach to Corner Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Novel Linear Time Corner Detection Algorithm
CGIV '05 Proceedings of the International Conference on Computer Graphics, Imaging and Visualization
Performance evaluation of corner detectors using consistency and accuracy measures
Computer Vision and Image Understanding
IEEE Transactions on Image Processing
Scale- and rotation-robust genetic programming-based corner detectors
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
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We developed a method to validate and filter a large set of previously obtained corner points. We derived the necessary relationships between image derivatives and estimates of corner angle, orientation and contrast. Commonly used cornerness measures of the auto-correlation matrix estimates of image derivatives are expressed in terms of these estimated corner properties. A candidate corner is validated if the cornerness score directly obtained from the image is sufficiently close to the cornerness score for an ideal corner with the estimated orientation, angle and contrast. We tested this algorithm on both real and synthetic images and observed that this procedure significantly improves the corner detection rates based on human evaluations. We tested the accuracy of our corner property estimates under various noise conditions. Extracted corner properties can also be used for tasks like feature point matching, object recognition and pose estimation.