Threshold selection by clustering gray levels of boundary
Pattern Recognition Letters
Unsupervised multiscale segmentation of color images
Pattern Recognition Letters
Gradient estimation using wide support operators
IEEE Transactions on Image Processing
Histogram thresholding using fuzzy and rough measures of association error
IEEE Transactions on Image Processing
Gradient histogram: Thresholding in a region of interest for edge detection
Image and Vision Computing
Joint Bayesian PET reconstruction algorithm using a quadratic hybrid multi-order prior
MIRAGE'07 Proceedings of the 3rd international conference on Computer vision/computer graphics collaboration techniques
Review article: Edge and line oriented contour detection: State of the art
Image and Vision Computing
NDE weld defect detection and feature extraction using segmentation approach
International Journal of Advanced Intelligence Paradigms
An effective segmentation for noise-based image verification using gamma mixture models
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
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We describe a method to automatically find gradient thresholds to separate edge from nonedge pixels. A statistical model that is the weighted sum of two gamma densities corresponding to edge and nonedge pixels is used to identify a threshold. Results closely match human perceptual thresholds even under low signal-to-noise ratio (SNR) levels