A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive Determination of Filter Scales for Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Visual Method for Assessing the Relative Performance of Edge-Detection Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Method for Objective Edge Detection Evaluation and Detector Parameter Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Characterization of empirical discrepancy evaluation measures
Pattern Recognition Letters
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic generation of consensus ground truth for the comparison of edge detection techniques
Image and Vision Computing
Unimodal thresholding for edge detection
Pattern Recognition
On candidates selection for hysteresis thresholds in edge detection
Pattern Recognition
Evaluation of global thresholding techniques in non-contextual edge detection
Pattern Recognition Letters
Thresholding in edge detection: a statistical approach
IEEE Transactions on Image Processing
Edge detection in ultrasound imagery using the instantaneous coefficient of variation
IEEE Transactions on Image Processing
A morphological gradient approach to color edge detection
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
A gravitational approach to edge detection based on triangular norms
Pattern Recognition
Edge detection in the feature space
Image and Vision Computing
Pattern Recognition Letters
A novel method to look for the hysteresis thresholds for the Canny edge detector
Pattern Recognition
Automatic edge detection using vector distance and partial normalization
WSEAS Transactions on Computers
Generating fuzzy edge images from gradient magnitudes
Computer Vision and Image Understanding
A new gravitational image edge detection method using edge explorer agents
Natural Computing: an international journal
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Hysteresis is an important technique for edge detection, but the unsupervised determination of its parameters is not an easy problem. In this paper, we propose a method for unsupervised determination of hysteresis thresholds using the advantages and disadvantages of two thresholding methods. The basic idea of our method is to look for the best hysteresis thresholds in a set of candidates. First, the method finds a subset and a overset of the unknown edge points set. Then, it determines the best edge map with the measure χ2. Compared with a general method to determine the parameters of an edge detector, our method performs well and is less computationally complex. The basic idea of our method can be generalized to other pattern recognition problems.