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
Two-dimensional entropic segmentation
Non-Linear Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Edge detection by scale multiplication in wavelet domain
Pattern Recognition Letters
Canny Edge Detection Enhancement by Scale Multiplication
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image thresholding using two-dimensional Tsallis-Havrda-Charvát entropy
Pattern Recognition Letters
Image segmentation by clustering of spatial patterns
Pattern Recognition Letters
The Thresholding Methods Based on Two-Dimensional Non-extensive Entropy
CISP '08 Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 3 - Volume 03
Image Segmentation Based on 2D Otsu Method with Histogram Analysis
CSSE '08 Proceedings of the 2008 International Conference on Computer Science and Software Engineering - Volume 06
Improved Image Thresholding Based on 2-D Tsallis Entropy
ESIAT '09 Proceedings of the 2009 International Conference on Environmental Science and Information Application Technology - Volume 01
Interactive image segmentation by maximal similarity based region merging
Pattern Recognition
Active contours driven by local image fitting energy
Pattern Recognition
Active contours with selective local or global segmentation: A new formulation and level set method
Image and Vision Computing
Thresholding using two-dimensional histogram and fuzzy entropy principle
IEEE Transactions on Image Processing
Journal of Visual Communication and Image Representation
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When using 2-D thresholding to segment images, the used threshold would partition the 2-D histogram into four quadrants, two of which are corresponding to the object and background, while the other two are corresponding to edges and noise. However, unsuccessful segmentation will often happen because many existing 2-D thresholding methods ignore edges and noise quadrants in calculation. To solve this problem, in this paper a novel 2-D threshold line segmentation strategy is proposed, in which the second threshold point is determined adaptively by considering the information of incorrectly classified pixels. The experiments on typical images demonstrated that the proposed method achieves very competitive segmentation results in comparison with the existing representative methods.