Image and Vision Computing
Computer Vision, Graphics, and Image Processing
Grey level corner detection: a generalization and a robust real time implementation
Computer Vision, Graphics, and Image Processing
Pattern Recognition
Scale-Based Detection of Corners of Planar Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detail-preserving median based filters in image processing
Pattern Recognition Letters
The nature of statistical learning theory
The nature of statistical learning theory
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
Analysis of gray level corner detection
Pattern Recognition Letters
Multi-scale curvature product for robust image corner detection in curvature scale space
Pattern Recognition Letters
Technical Section: Corner detection by sliding rectangles along planar curves
Computers and Graphics
Direct Curvature Scale Space: Theory and Corner Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A distributed PSO-SVM hybrid system with feature selection and parameter optimization
Applied Soft Computing
Handling of impreciseness in gray level corner detection using fuzzy set theoretic approach
Applied Soft Computing
Faster and Better: A Machine Learning Approach to Corner Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Information Technology in Biomedicine
IEEE Transactions on Image Processing
Foveated Visual Search for Corners
IEEE Transactions on Image Processing
Developing Nonstationary Noise Estimation for Application in Edge and Corner Detection
IEEE Transactions on Image Processing
Multiscale Corner Detection of Gray Level Images Based on Log-Gabor Wavelet Transform
IEEE Transactions on Circuits and Systems for Video Technology
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This paper presents a fast two-stage corner detector with noise tolerance. In the first stage, a novel candidate pruning approach based on PSO-SVM is proposed to select candidatecorner pixels which have great potential to be corners. In the second stage the Harris corner detector is employed to recognize real corners among the candidate-corner pixels. The parameters and feature selection of SVM classifier is optimized by using particle swarm optimization (PSO). The method takes advantage of the minimum structure risk of SVM and the quickly globally optimizing ability of PSO. Generally speaking, corners are considered as the junction of edges. Thus, edge pixels with a high gradient in more than one direction should be selected as candidate corners. Meanwhile, impulse noise often corrupts digital images while images are transmitted over an unreliable channel or are captured using a camera with faulty sensors. Noise-corrupted pixels usually cause serious false detection problems in most corner detectors. The proposed PSO-SVM candidate pruning approach detects noisy pixels and excludes them from being candidate corners to enhance the noise tolerance of the corner detector. Through the well-selection of candidate corners, the proposed candidate pruning approach can 1) enhance the noise tolerance capability, and 2) reduce the computational effort of the corner detectors.