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
A Fast Algorithm for Color Image Segmentation
ICICIC '06 Proceedings of the First International Conference on Innovative Computing, Information and Control - Volume 2
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)
Hi-index | 12.05 |
Gray-level clustering is an important procedure in image processing, which reduces the gray-level intensity of an image. In order to display a high gray-level image on low gray-level device screen, a good gray-level clustering reduction algorithm is necessary to complete this task. Based on the mean values and standard deviations of image histogram within different sub-intervals, a recursive algorithm for the gray-level reduction is proposed in this paper. It divides the image histogram into different sub-intervals recursively until the difference between original image and clustered image within given thresholds are reached. We experimented our proposed algorithm in comparison with other state-of-the-art algorithms on different high gray-level images. Our experimental results show our proposed algorithm outperformed others' in terms of high visual quality of clustered images and computational inexpensiveness.