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
How Optimal Depth Cue Integration Depends on the Task
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
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
Object segmentation using ant colony optimization algorithm and fuzzy entropy
Pattern Recognition Letters
Unimodal thresholding for edge detection
Pattern Recognition
On candidates selection for hysteresis thresholds in edge detection
Pattern Recognition
A filter model for feature subset selection based on genetic algorithm
Knowledge-Based Systems
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
A gravitational approach to edge detection based on triangular norms
Pattern Recognition
Pattern Recognition Letters
A novel method to look for the hysteresis thresholds for the Canny edge detector
Pattern Recognition
Unsupervised edge detection and noise detection from a single image
Pattern Recognition
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Hysteresis is an important edge detection technique, but the unsupervised determination of hysteresis thresholds is a difficult problem. Thus, hysteresis has limited practical applicability. Unimodal thresholding techniques are another edge detection method. They are useful, because the histogram of a feature image (usually the feature image is an approximation of the gradient image) is unimodal, and there are many unsupervised methods to solve this problem. But such techniques do not use spatial information to detect edge points, so their performance is worse than that of the hysteresis. In this paper, we show how to formulate the hysteresis process as a unimodal thresholding problem without determining the optimal hysteresis thresholds. Using similar steps of the Canny edge detector to obtain an approximation of the gradient image we compare the performance of our method against that of a method that determines the best parameters of an edge detector and show that our method performs relatively well. Additionally, our method can adjust its sensitivity by using different unimodal thresholding techniques.