IJSER Home >> Journal >> IJSER
International Journal of Scientific and Engineering Research
ISSN Online 2229-5518
ISSN Print: 2229-5518 4    
Website: http://www.ijser.org
scirp IJSER >> Volume 2, Issue 4, April 2011 Edition
Novel Object Removal in Video using Patch Sparsity
Full Text(PDF, 3000)  PP.  
Author(s)
B. Vidhya, S. Valarmathy
KEYWORDS
Candidate patches, edge detection, inpainting, linear sparse representation, patch sparsity, patch propagation, texture synthesis.
ABSTRACT
The process of repairing the damaged area or to remove the specific areas in a video is known as video inpainting. To deal with this kind of problems, not only a robust image inpainting algorithm is used, but also a technique of structure generation is used to fill-in the missing parts of a video sequence taken from a static camera. Most of the automatic techniques of video inpainting are computationally intensive and unable to repair large holes. To overcome this problem, inpainting method is extended by incorporating the sparsity of natural image patches in the spatio-temporal domain is proposed in this paper. First, the video is converted into individual image frames. Second, the edges of the object to be removed are identified by the SOBEL edge detection method. Third, the inpainting procedure is performed separately for each time frame of the images. Next, the inpainted image frames are displayed in a sequence, to appear as a inpainted video. For each image frame, the confidence of a patch located at the image structure (e.g., the corner or edge) is measured by the sparseness of its nonzero similarities to the neighboring patches to calculate the patch structure sparsity. The patch with larger structure sparsity is assigned higher priority for further inpainting. The patch to be inpainted is represented by the sparse linear combination of candidate patches. Patch propagation is performed automatically by the algorithm by inwardly propagating the image patches from the source region into the interior of the target region by means of patch by patch. Compared to other methods of inpainting, a better discrimination of texture and structure is obtained by the structure sparsity and also sharp inpainted regions are obtained by the patch sparse representation. This work can be extended to wide areas of applications, including video special effects and restoration and enhancement of damaged videos.
References
[1] M. Bertalmio, G. Sapiro, V. Caselles, and C. Ballester, “Image inpainting,” in Proc. SIGGRAPH, 2000, pp. 417–424.

[2] M. Bertalmio, A. L. Bertozzi, and G. Sapiro, “Navier–Strokes, fluid dynamics, and image and video inpainting,” in Proc. IEEE Computer Society Conf. Computer Vision and Pattern Recognition, pp. 417–424, 2001.

[3] Criminisi, P. Perez, and K. Toyama, “Region filling and object removal by examplar-based image inpainting,” IEEE Trans. Image Process., vol. 13, pp. 1200–1212, 2004.

[4] J. Jia and C. K. Tang, “Image repairing: Robust image synthesis by adaptive and tensor voting,” in Proc. IEEE Computer Society Conf. Computer Vision and Pattern Recogition, pp. 643–650, 2003.

[5] K.A. Patwardhan, G. Sapiro, and M. Bertalmio, “Video inpainting of occluding and occluded objects"", in Proc. ICIP 2005.Vol. II, pp. 69-72.

[6] Y. Wexler, E. Shechtman, and M. Irani, “Space-time video completion,” Proceedings. 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, 2004.

[7] Y. Zhang, J. Xiao, and M. Shah, “Motion layer based object removal in videos,” 2005 Workshop on Applications of Computer Vision, 2005.

Untitled Page