Corrupted Region Restoration based on 2D Tensor Voting and Segmentation 


Vol. 15,  No. 3, pp. 205-210, Jun.  2008
10.3745/KIPSTB.2008.15.3.205


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  Abstract

A new approach is proposed for restoration of corrupted regions and segmentation in natural text images. The challenge is to fill in the corrupted regions on the basis of color feature analysis by second order symmetric stick tensor. It is show how feature analysis can benefit from analyzing features using tensor voting with chromatic and achromatic components. The proposed method is applied to text images corrupted by manifold types of various noises. Firstly, we decompose an image into chromatic and achromatic components to analyze images. Secondly, selected feature vectors are analyzed by second- order symmetric stick tensor. And tensors are redefined by voting information with neighbor voters, while restore the corrupted regions. Lastly, mode estimation and segmentation are performed by adaptive mean shift and separated clustering method respectively. This approach is automatically done, thereby allowing to easily fill-in corrupted regions containing completely different structures and surrounding backgrounds. Applications of proposed method include the restoration of damaged text images; removal of superimposed noises or streaks. We so can see that proposed approach is efficient and robust in terms of restoring and segmenting text images corrupted.

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  Cite this article

[IEEE Style]

J. H. Park and N. D. Toan, "Corrupted Region Restoration based on 2D Tensor Voting and Segmentation," The KIPS Transactions:PartB , vol. 15, no. 3, pp. 205-210, 2008. DOI: 10.3745/KIPSTB.2008.15.3.205.

[ACM Style]

Jong Hyun Park and Nguyen Dinh Toan. 2008. Corrupted Region Restoration based on 2D Tensor Voting and Segmentation. The KIPS Transactions:PartB , 15, 3, (2008), 205-210. DOI: 10.3745/KIPSTB.2008.15.3.205.

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