A survey of thresholding techniques
Computer Vision, Graphics, and Image Processing
A fast thresholding selection procedure for multimodal and unimodal histograms
Pattern Recognition Letters
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
Background-Subtraction in Thermal Imagery Using Contour Saliency
International Journal of Computer Vision
On minimum variance thresholding
Pattern Recognition Letters
Extrapolative Spatial Models for Detecting Perceptual Boundaries in Colour Images
International Journal of Computer Vision
Hybrid Runtime Management of Space-Time Heterogeneity for Parallel Structured Adaptive Applications
IEEE Transactions on Parallel and Distributed Systems
Automatic generation of consensus ground truth for the comparison of edge detection techniques
Image and Vision Computing
The strongest schema learning GA and its application to multilevel thresholding
Image and Vision Computing
Unimodal thresholding for edge detection
Pattern Recognition
On candidates selection for hysteresis thresholds in edge detection
Pattern Recognition
Solving the process of hysteresis without determining the optimal thresholds
Pattern Recognition
Evaluation of global thresholding techniques in non-contextual edge detection
Pattern Recognition Letters
IEEE Transactions on Image Processing
Segmentation of Images Having Unimodal Distributions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ridler and Calvard's, Kittler and Illingworth's and Otsu's methods for image thresholding
Pattern Recognition Letters
Unimodal thresholding for Laplacian-based Canny-Deriche filter
Pattern Recognition Letters
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The gradient image is used to detect edge points, and the gradient histogram is a typical case of a unimodal histogram. It is well-documented that bi-modal thresholding methods (such as the Otsu method) detect edges poorly. Therefore, specific unimodal thresholding methods are used to detect edge points. However, unimodal thresholding methods (such as the Rosin method) sometimes obtain very noisy results. In this paper, we propose a histogram transformation to improve the performance of some thresholding methods. Using the Berkeley Segmentation Dataset, we present quantitative performance results in an edge detection task to show that our transformation improves the performance of the Otsu and Rosin methods. Our histogram transformation can be used by any histogram thresholding method, but the performance of the method, using the transformed histogram, will depend of the criterion used by this method.