A survey of thresholding techniques
Computer Vision, Graphics, and Image Processing
Threshold selection based on cluster analysis
Pattern Recognition Letters
Image thresholding using Tsallis entropy
Pattern Recognition Letters
Automatic thresholding for defect detection
Pattern Recognition Letters
On minimum variance thresholding
Pattern Recognition Letters
A novel image thresholding method based on Parzen window estimate
Pattern Recognition
Multilevel thresholding for image segmentation through a fast statistical recursive algorithm
Pattern Recognition Letters
A hidden Markov model-based character extraction method
Pattern Recognition
Supervised grayscale thresholding based on transition regions
Image and Vision Computing
Optimal multi-level thresholding using a two-stage Otsu optimization approach
Pattern Recognition Letters
Morphological preprocessing method to thresholding degraded word images
Pattern Recognition Letters
Gradient histogram: Thresholding in a region of interest for edge detection
Image and Vision Computing
Image thresholding using type II fuzzy sets
Pattern Recognition
Robust threshold estimation for images with unimodal histograms
Pattern Recognition Letters
Fast three-dimensional Otsu thresholding with shuffled frog-leaping algorithm
Pattern Recognition Letters
Image Thresholding Using Graph Cuts
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Supervised range-constrained thresholding
IEEE Transactions on Image Processing
Detection of soldering defects in Printed Circuit Boards with Hierarchical Marked Point Processes
Pattern Recognition Letters
Modified local entropy-based transition region extraction and thresholding
Applied Soft Computing
Tsallis entropy and the long-range correlation in image thresholding
Signal Processing
Image thresholding based on semivariance
Pattern Recognition Letters
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Three range-constrained thresholding methods are proposed in the light of human visual perception. The new methods first implement gray level range-estimation, using image statistical characteristics in the light of human visual perception. An image transformation is followed by virtue of estimated ranges. Criteria of conventional thresholding approaches are then applied to the transformed image for threshold selection. The key issue in the process lies in image transformation which is based on unsupervised estimation for gray level ranges of object and background. The transformation process takes advantage of properties of human visual perception and simplifies an original image, which is helpful for image thresholding. Three new methods were compared with their counterparts on a variety of images including nondestructive testing ones, and the experimental results show its effectiveness.