Browsing by Author "Tseng, Chen-yu"

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AUTOMATIC 3-D DEPTH RECOVERY FROM A SINGLE URBAN-SCENE IMAGE
(2012)
Tseng, Chen-yu
;
Wang, Sheng-Jyh
;
電子工程學系及電子研究所
;
Department of Electronics Engineering and Institute of Electronics
In this paper, we focus on recovering a 3-D depth map from a single image via ground-vertical boundary analysis. First, we generate a ground map from the input image based on the spectral matting method, followed by a spatial geometric inference. After that, we derive the depth information for the ground-vertical boundaries. Unlike conventional approaches which generally use plane models to reconstruct a 3-D structure that fits the estimated boundaries, we infer a dense depth map by solving a Maximum-A-Posteriori (MAP) estimation problem. In this MAP problem, we use a generalized spatial-coherence prior model based on the Matting Laplacian (ML) matrix in order to provide a more robust solution for depth inference. We demonstrate that this approach can produce more pleasant depth maps for cluttered scenes.
MAXIMUM-A-POSTERIORI ESTIMATION FOR GLOBAL SPATIAL COHERENCE RECOVERY BASED ON MATTING LAPLACIAN
(2012)
Tseng, Chen-yu
;
Wang, Sheng-Jyh
;
電子工程學系及電子研究所
;
Department of Electronics Engineering and Institute of Electronics
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.