DOI QR코드

DOI QR Code

Corrupted Region Restoration based on 2D Tensor Voting

2D 텐서 보팅에 기반 한 손상된 텍스트 영상의 복원 및 분할

  • 박종현 (전남대학교 전자컴퓨터공학부 BK21) ;
  • ;
  • 이귀상 (전남대학교 전자컴퓨터공학부)
  • Published : 2008.06.30

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.

본 논문에서는 잡음에 의해 손상된 텍스트 영상으로부터 복원 및 분할을 위한 새로운 접근 방법을 제안한다. 제안된 방법은 손상된 영역의 복원을 위하여 색상 및 비색상 성분을 2차 대칭 스틱 텐서로 표현하고 보팅 기반의 손상된 영역을 복원하였으며, 마지막으로 클러스터링 방법에 의해 분할을 수행한다. 먼저 우리는 제안된 색상 선택함수에 의해 잡음에 강건한 색상과 비색상 성분을 선택한다. 두 번째 단계에서는 각각의 선택된 특징 벡터들은 스틱 텐서로 표현하였으며 제한된 보팅 커널의 필드내에서 이웃하는 보터들과 통신을 통하여 새롭게 정의된다. 따라서 2차 보팅 후 각각의 스틱 텐서는 이웃하는 텐서와 같은 특성을 가지며 손상된 영역들을 복원할 수 있다. 마지막으로 복원된 영상의 성능을 평가하기 위하여 적응적 평균 이동 알고리즘과 클러스터링 알고리즘을 이용하여 영상 분할을 수행하였다. 실험에서 제안된 방법은 전체적인 처리과정을 자동적으로 수행 가능하였으며 배경 및 객체의 영역에서 효율적인 복원 및 분할을 수행할 수 있었다.

Keywords

References

  1. H. Kaiqu, W. Zhenyang and W. Qiao, “Image Enhancement based on the Statistics of Visual Representation,” Image and Vision Computing, Vol.23, pp.51-57, 2005 https://doi.org/10.1016/j.imavis.2004.07.005
  2. M. Motwani, M. Gadiya, R. Motwani and F.C. Harris, “A Survey of Image Denoising Techniques,” International Signal Processing Conference of GSPx, 2004
  3. M. Bertalmoi, G. Sapiro, V. Caselles and C. Ballester, “Image Inpainting,” Proceeding of SIGGRAPH, 2000
  4. C. Ballester, M. Bertalmoi, V. Caselles, G. Sapiro and J. Verdera, “Filling-in by Joint Interpolation of Vector Fields and Gray Level,” IEEE Transactions on Image Processing, Vol.10, No.8, pp.1200-1211, 2001 https://doi.org/10.1109/83.935036
  5. G. Medioni, M.S. Lee, and C.K. Tang, A Computational Framework for Segmentation and Grouping, Elsevier, 2000
  6. J. Jia, C.K. Tang, Inference of Segmented Color and Texture Description by Tensor Voting,“IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.26, No.6, pp.771-786, 2004 https://doi.org/10.1109/TPAMI.2004.10
  7. C. Zhang, P. Wang, “A New Method of Color Image Segmentation based on Intensity and Hue Clustering,” IEEE International Conference on Pattern Recognition, Vol.3, pp.3617-3621, 2000 https://doi.org/10.1109/ICPR.2000.903620
  8. D. Comaniciu and P. Meer, “Mean Shift Analysis and Application,” IEEE International Conference on Computer Vision, pp.1197-1203, 1999 https://doi.org/10.1109/ICCV.1999.790416
  9. D. Comaniciu and P. Meer, Mean Shift: A Robust Approach Toward Feature Space Analysis,“ IEEE Transactions on Pattern Analysis Machine Intelligence, Vol.24, No.5, pp.603-619, 2002 https://doi.org/10.1109/34.1000236
  10. http://algoval.essex.ac.uk/icdar/Competitions.html
  11. S.M. Lucas, A. Panaretos, L.Sosa, A. Tang. S. Wong and R. Young, “ICDAR 2003 Robust Reading Competitions,” IEEE International Conference on Document Analysis and Recognition, pp.682-687, 2003
  12. S. Sural, G. Qian, S. Pramanik, “Segmentation and Histogram Generation using the HSV Color Space for Image Retrieval,” IEEE International Conference on Image Processing, Vol.2, pp.589-592, 2000 https://doi.org/10.1109/ICIP.2002.1040019