Image and Vision Computing
A computational approach for corner and vertex detection
International Journal of Computer Vision
Extracting topographic terrain features from elevation maps
CVGIP: Image Understanding
SUSAN—A New Approach to Low Level Image Processing
International Journal of Computer Vision
Junctions: Detection, Classification, and Reconstruction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Image Corner Detection Through Curvature Scale Space
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Field Categorization and Edge/Corner Detection from Gradient Covariance
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Handling of impreciseness in gray level corner detection using fuzzy set theoretic approach
Applied Soft Computing
Robust image corner detection based on scale evolution difference of planar curves
Pattern Recognition Letters
Performance evaluation of corner detectors using consistency and accuracy measures
Computer Vision and Image Understanding
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Most conventional derivative-based corner detectors have shortcomings such as missing junctions, poor localization, sensitivity to noise and high computational cost. This paper presents a new, simple and effective low level processing method to detect corners. In this corner detection algorithm, two oriented cross operators called crosses as oriented pair (COP) are used, which provide useful information to extract low-level features due to its characteristics, preference for edge with different direction and simple direction determination. Fast, accurate and noise-robust corner detection is accomplished with the COP.