Scale-Based Detection of Corners of Planar Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
SUSAN—A New Approach to Low Level Image Processing
International Journal of Computer Vision
Robust Image Corner Detection Through Curvature Scale Space
IEEE Transactions on Pattern Analysis and Machine Intelligence
Curvature Scale Space Corner Detector with Adaptive Threshold and Dynamic Region of Support
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Canny Edge Detection Enhancement by Scale Multiplication
IEEE Transactions on Pattern Analysis and Machine Intelligence
Edge and Curve Detection for Visual Scene Analysis
IEEE Transactions on Computers
Wavelet transform domain filters: a spatially selective noise filtration technique
IEEE Transactions on Image Processing
Techniques for efficient and effective transformed image identification
Journal of Visual Communication and Image Representation
Fundamenta Informaticae - Swarm Intelligence
Robust Harris-Laplace Detector by Scale Multiplication
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
Corner detection based on gradient correlation matrices of planar curves
Pattern Recognition
On the convergence of planar curves under smoothing
IEEE Transactions on Image Processing
Curvature product corner detection in direct curvature scale space
International Journal of Computational Vision and Robotics
Fundamenta Informaticae - Swarm Intelligence
Hi-index | 0.11 |
The paper proposes a new corner detector based on scale multiplication. The algorithm starts with extracting the contour of the object of interest, and then computes the curvature of this contour with Gaussian derivative filters at various scales. Local extremes of the product of the curvatures at different scales are reported as corners when the value of the product exceeds a threshold. The proposed detector is based on the well-known curvature scale-space, but improves on it in two ways: first, since the finest scale is part of the scale product, there is no need for coarse-to-fine corner tracking. Second, since many scales are involved, false positive/negative detections are unlikely even with a single threshold. Finally, the comparison between the proposed approach and other corner detectors shows that our approach is more competitive with respect to CCN and ACU criteria under similarity and affine transforms. Moreover, a number of experiments also illustrate that the scale product corner detection has more robustness for noise.