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
Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised texture segmentation using Gabor filters
Pattern Recognition
Texture Classification by Wavelet Packet Signatures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Summed-area tables for texture mapping
SIGGRAPH '84 Proceedings of the 11th annual conference on Computer graphics and interactive techniques
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Image thresholding using Tsallis entropy
Pattern Recognition Letters
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Image thresholding using two-dimensional Tsallis-Havrda-Charvát entropy
Pattern Recognition Letters
Image thresholding using type II fuzzy sets
Pattern Recognition
Automatic histogram threshold using fuzzy measures
IEEE Transactions on Image Processing
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
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We propose GLGM (gray-level & gradient-magnitude) histogram as a novel image histogram for thresholding. GLGM histogram explicitly captures the gray level occurrence probability and spatial distribution property simultaneously. Different from previous histograms that also consider the spatial information, GLGM histogram employs the Fibonacci quantized gradient magnitude to characterize spatial information effectively. In this paper, it is applied to entropic image thresholding. For threshold selection, we define a new spatial property weighting function to depict the roles played by different kinds of pixels. The experiments demonstrate the effectiveness and robustness of our thresholding approach, containing wide range comparisons with the well established thresholding methods.