Abstract

Thresholding algorithms are quite easy and effective for bi-level thresholding but in case of multilevel thresholding, the performance becomes unreliable due to complexity in computation because the complexity will exponentially increase. In this approach, multilevel thresholding is done for comparison by taking help of Otsu's clustering method and PSO clustering method. A dendogram of gray levels is created based on histogram of an image. The bottom-up generation of clusters employing a dendogram by the proposed method yields good separation of the clusters and obtains a robust estimate of the threshold. Such cluster organization will yield a clear separation between object and background even for the case of nearly unimodal or multimodal histogram. Since the hierarchical clustering method performs an iterative merging operation, it is extended to multi-level thresholding problem by eliminating grouping of clusters when the pixel values are obtained from the expected number of clusters.

References

• M. Sezgin, B. Sankur, Image Thresholding Techniques: Quantitative Performance Evaluation, submitted to Pattern Recognition, 2001
• S. U. Le, S. Y. R. H. Park, A Comparative Performance Study of Several Global Thresholding Techniques for Segmentation, Graphical Models and Image Processing, 52, 171-190, 1990
• J. S. Weszka, A. Rosenfeld, Threshold evaluation techniques, IEEE Trans. Systems, Man and Cybernetics, SMC-8(8), 627-629, 1978
• P. W. Palumbo, P. Swaminathan, S. N. Srihari, Document image binarization: Evaluation of algorithms, Proc. SPIE Applications of Digital Image Proc. , SPIE Vol. 697, pp: 278-286, 1986
• M. Kamel, A. Zhao, Extraction of Binary Character/Graphics Images From Grayscale Document Images, Graphical Models and Image Processing, 55, No. 3, 203-217, 1993
• P. K. Sahoo, S. Soltani, A. K. C. Wong , Y. Chen. , A Survey of Thresholding Techniques, Computer Graphics and Image Process. , 41, 233-260, 1988
• C. A. Glasbey, An analysis of histogram-based thresholding algorithms, Graphical Models and Image Processing, 55,532-537, , 1993
• M. I. Sezan, A Peak Detection Algorithm and its Application to Histogram-Based Image Data Reduction, Graphical Models and Image Processing, 29, 47-59, 1985
• N. Ramesh, J. H. Yoo, I. K. Sethi, Thresholding Based on Histogram Approximation, IEEE Proc. Vis. Image, Signal Proc. , 142(5), 271-279, 1995
• N. Otsu, A Threshold Selection Method From Gray Level Histograms, IEEE Transactions on Systems, Man, and Cybernetics, SMC-9, 62-66, 1979
• J. S . Weszka, "A survey of threshold selection techniques," Comput. Graph. Image Processing, vol. 7, no. 2, pp. 259-265. Apr. , 1978
• K. S. Fu and J. K. Mui, "A survey on image segmentation," Pattern Recogrt. , vol. 13, no. 1, pp. 3-16, 1981
• P. K. Sahoo, S. Soltani, A. K. C. Wong, and Y. C. Chen, "A survey of thresholding techniques," Compur. Vision, Graph. , image processing, vol. 41, no. 2, pp. 233-260, Feb. 1988
• B. Bhanu. "Automatic target recognition: State of the art survey," IEEE Trans. Aerosp. Electron. Syst. , vol. AES-22, no. 4, pp. 364-379, July 1986
• T. W. Ridler and S. Calvard, "Picture thresholding using an iterative selection method," IEEE Trans. Syst. Man Cyber-. , vol. SMC-8, no. 8, pp. 63M32, Aug. 1978