A survey of thresholding techniques
Computer Vision, Graphics, and Image Processing
Binarization and multithresholding of document images using connectivity
CVGIP: Graphical Models and Image Processing
On hierarchical segmentation for image compression
Pattern Recognition Letters
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
A Fast Algorithm for Color Image Segmentation
ICICIC '06 Proceedings of the First International Conference on Innovative Computing, Information and Control - Volume 2
Multilevel thresholding for image segmentation through a fast statistical recursive algorithm
Pattern Recognition Letters
A Novel Image Text Extraction Method Based on K-Means Clustering
ICIS '08 Proceedings of the Seventh IEEE/ACIS International Conference on Computer and Information Science (icis 2008)
A shadow detection method for remote sensing images using affinity propagation algorithm
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
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Gray-level clustering is an important procedure in image processing, which reduces the gray-level of an image. In order to display an image with high gray level in a screen with lower gray level, a good gray-level clustering algorithm is necessary to complete this job. Based on the mean value and standard deviation of histogram within a sub-interval, a novel recursive algorithm for solving the gray-level reduction is proposed in this paper. It divides the sub-interval recursively until the difference between original image and clustered image within a given threshold. Experiments are carried out for some samples with high gray level to demonstrate the computational advantage of the proposed method.