A survey of thresholding techniques
Computer Vision, Graphics, and Image Processing
Automatic thresholding of gray-level pictures using two-dimensional entropy
Computer Vision, Graphics, and Image Processing
Optimal thresholding—a new approach
Pattern Recognition Letters
Entropic thresholding using a block source model
Graphical Models and Image Processing
Mathematics and Computers in Simulation - Special issue: Robotics
A fast scheme for optimal thresholding using genetic algorithms
Signal Processing
Fractional differentiation for edge detection
Signal Processing - Special issue: Fractional signal processing and applications
Image thresholding using Tsallis entropy
Pattern Recognition Letters
Optimal multi-thresholding using a hybrid optimization approach
Pattern Recognition Letters
Image thresholding using two-dimensional Tsallis-Havrda-Charvát entropy
Pattern Recognition Letters
Automatic thresholding for defect detection
Pattern Recognition Letters
Image histogram thresholding based on multiobjective optimization
Signal Processing
Pattern Recognition Letters
A thresholding method based on two-dimensional fractional differentiation
Image and Vision Computing
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Medical image thresholding using online trained neural networks
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part V
Information Sciences: an International Journal
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Various techniques have previously been proposed for single-stage thresholding of images to separate objects from the background. Although these global or local thresholding techniques have proven effective on particular types of images, none of them is able to produce consistently good results on a wide range of existing images. Here, a new image histogram thresholding method, called TDFD, based on digital fractional differentiation is presented for gray-level image thresholding. The proposed method exploits the properties of the digital fractional differentiation and is based on the assumption that the pixel appearance probabilities in the image are related. To select the best fractional differentiation order that corresponds to the best threshold, a new algorithm based on non-Pareto multiobjective optimization is presented. A new geometric regularity criterion is also proposed to select the best thresholded image. In order to illustrate the efficiency of our method, a comparison was performed with five competing methods: the Otsu method, the Kapur method, EM algorithm based method, valley emphasis method, and two-dimensional Tsallis entropy based method. With respect to the mode of visualization, object size and image contrast, the experimental results show that the segmentation method based on fractional differentiation is more robust than the other methods.