A Low Complexity 2D Hidden Markov Model in Application to Image Segmentation

Yu Lu; Wu Le-nan; Xie Jun

doi:10.3724/SP.J.1146.2006.00999

Volume 30 Issue 2

Jan. 2011

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Article Navigation > Journal of Electronics & Information Technology > 2008 > 30(2): 277-281

Yu Lu, Wu Le-nan, Xie Jun. A Low Complexity 2D Hidden Markov Model in Application to Image Segmentation[J]. Journal of Electronics & Information Technology, 2008, 30(2): 277-281. doi: 10.3724/SP.J.1146.2006.00999

Citation:

Yu Lu, Wu Le-nan, Xie Jun. A Low Complexity 2D Hidden Markov Model in Application to Image Segmentation[J]. Journal of Electronics & Information Technology, 2008, 30(2): 277-281. doi: 10.3724/SP.J.1146.2006.00999

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A Low Complexity 2D Hidden Markov Model in Application to Image Segmentation

doi: 10.3724/SP.J.1146.2006.00999

Yu Lu^{①②;吴乐南},
Wu Le-nan,
Xie Jun

Received Date: 2006-07-07
Rev Recd Date: 2007-03-28
Publish Date: 2008-02-19

Abstract

Abstract

The assumption of conditional independence in the relationship between adjacent blocks has been proposed by others to reduce the complexity of 2D HMM. In this paper, a more general 2D HMM relaxing this assumption is proposed. More general recursive forms of the forward and the backward algorithms are derived. And the model provides more flexibility by adjusting the weight between horizontal and vertical information. The application to image segmentation verifies the effectiveness of the model.

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References(1)

References

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