Scale-Based Detection of Corners of Planar Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Theory of Multiscale, Curvature-Based Shape Representation for Planar Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Image Corner Detection Through Curvature Scale Space
IEEE Transactions on Pattern Analysis and Machine Intelligence
Boundary-based corner detection using eigenvalues of covariance matrices
Pattern Recognition Letters
Pattern Recognition Letters
Wavelet-based corner detection technique using optimal scale
Pattern Recognition Letters
Wavelet-based corner detection using eigenvectors of covariance matrices
Pattern Recognition Letters
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
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
Multiscale contour corner detection based on local natural scale and wavelet transform
Image and Vision Computing
Technical Section: Corner detection by sliding rectangles along planar curves
Computers and Graphics
Morphological corner detector using paired triangular structuring elements
Pattern Recognition
Corner detection and curve segmentation by multiresolution chain-code linking
Pattern Recognition
Pattern Recognition Letters
Multiscale corner detection by using wavelet transform
IEEE Transactions on Image Processing
Corner detection based on gradient correlation matrices of planar curves
Pattern Recognition
Anisotropic diffusion for effective shape corner point detection
Pattern Recognition Letters
Multiscale Corner Detection in Planar Shapes
Journal of Mathematical Imaging and Vision
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In this paper, a new corner detector is proposed based on evolution difference of scale pace, which can well reflect the change of the domination feature between the evolved curves. In Gaussian scale space we use Difference of Gaussian (DoG) to represent these scale evolution differences of planar curves and the response function of the corners is defined as the norm of DoG characterizing the scale evolution differences. The proposed DoG detector not only employs both the low scale and the high one for detecting the candidate corners but also assures the lowest computational complexity among the existing boundary-based detectors. Finally, based on ACU and Error Index criteria the comprehensive performance evaluation of the proposed detector is performed and the results demonstrate that the present detector allows very strong response for corner position and possesses a better detection and localization performance and robustness against noise.