Title :
Maximum-A-Posteriori estimation for global spatial coherence recovery based on Matting Laplacian
Author :
Chen-Yu Tseng ; Sheng-Jyh Wang
Author_Institution :
Dept. of Electron. Eng., Nat. Chiao Tung Univ., Hsinchu, Taiwan
fDate :
Sept. 30 2012-Oct. 3 2012
Abstract :
Global spatial coherence is an important criterion in the performance evaluation of many image applications, such as image segmentation, image enhancement, depth estimation, motion estimation, and many others. In this paper, we treat the recovery of spatial coherence as a Maximum-A-Posteriori (MAP) estimation problem, with a generalized spatial-coherence prior model based on Matting Laplacian (ML) matrix. Besides, to enhance computational efficiency, a cell-based Matting-Laplacian (CML) framework is further proposed. In our experiments, we demonstrate that the proposed approach can greatly improve the spatial coherence of the output results in variant applications, like the shape-from-focus process and the SIFT-flow refinement process.
Keywords :
image enhancement; image segmentation; matrix algebra; maximum likelihood estimation; motion estimation; CML framework; MAP estimation; ML matrix; SIFT-flow refinement process; cell-based matting-Laplacian framework; depth estimation; global spatial coherence recovery; image enhancement; image segmentation; matting Laplacian matrix; maximum-a-posteriori estimation; motion estimation; scale invariant feature transform; shape-from-focus process; Computational efficiency; Computational modeling; Estimation; Image color analysis; Laplace equations; Spatial coherence; Vectors; depth estimation; image filtering; matting Laplacian; spatial coherence; spectral graph;
Conference_Titel :
Image Processing (ICIP), 2012 19th IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
978-1-4673-2534-9
Electronic_ISBN :
1522-4880
DOI :
10.1109/ICIP.2012.6466853
