Research on Lloyd-Max Quantizer with Two-Stage Otsu’s Method

Ping Sung Liao; Chein Hua Lee

doi:10.4028/www.scientific.net/AMM.540.480

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 540Research on Lloyd-Max Quantizer with Two-Stage...

Research on Lloyd-Max Quantizer with Two-Stage Otsu’s Method

Abstract:

Otsu’s method is often used in image segmentation applications such as defect detections, medical image diagnosis and object shape recognitions. However it is very time-consuming for multilevel segmentation. Lloyd-Max quantizer is a popular and efficient data compressor. Fundamentally, Otsu’s method and Lloyd-Max quantizer are equivalent to maximum a posteriori probability estimate. Applying them on multilevel image segmentation, we can find their segmented results over an image are very approximate, but Otsu’s method running in exhaustive search consumes more processing time than Lloyd-Max quantizer with iterative characteristics does. Thus, Lloyd-Max quantizer is strongly recommended as the fast and first-stage agent for Otsu’s method to find the optimal threshold values for image segmentation.

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Periodical:

Applied Mechanics and Materials (Volume 540)

Pages:

480-483

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.540.480

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Online since:

April 2014

Authors:

Ping Sung Liao*, Chein Hua Lee

Keywords:

Between-Class Variance, Llyod-Max Quantizer, Otsu Method

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References

[1] M.F. Zakaria, H.S. Choon, and S.A. Suandi, Object Shape Recognition in Image for Machine Vision Application, International Journal of Computer Theory and Engineering, Vol. 4, No. 1, (2012), pp.76-80.

DOI: 10.7763/ijcte.2012.v4.428

Google Scholar

[2] D. Unay, and B. Gosselin, Thresholding-based Segmentation and Apple Grading by Machine Vision, 13th European Signal Processing Conference, (2013).

Google Scholar

[3] H.F. Ng, Automatic Thresholding for Defect Detection, Pattern Recognition Letter, Vol. 27, ( 2006), pp.1644-1649.

DOI: 10.1016/j.patrec.2006.03.009

Google Scholar

[4] P.S. Liao, T.S. Chen, P.C. Chung, A Fast Algorithm For Multilevel Thresholding, Journal of Information Science and Engineering, Vol. 17, (2001) , pp.713-727.

Google Scholar

[5] J. MAX, Quantizing for Minimum Distortion, IRE Trans. on Information Theory, IT-6(1), (1960), pp.7-12.

Google Scholar

[6] S.P. Lloyd, Least squares quantization in PCM, IEEE Trans. Information Theory, IT-28, (1982), pp.129-137.

DOI: 10.1109/tit.1982.1056489

Google Scholar