Cost volume refinement filter for post filtering of visual corresponding

Shu Fujita; Takuya Matsuo; Norishige Fukushima; Yutaka Ishibashi

doi:10.1117/12.2083086

16 March 2015 Cost volume refinement filter for post filtering of visual corresponding

Shu Fujita, Takuya Matsuo, Norishige Fukushima, Yutaka Ishibashi

Author Affiliations +

Proceedings Volume 9399, Image Processing: Algorithms and Systems XIII; 93990Q (2015) https://doi.org/10.1117/12.2083086
Event: SPIE/IS&T Electronic Imaging, 2015, San Francisco, California, United States

Abstract

In this paper, we propose a generalized framework of cost volume refinement filtering for visual corresponding problems. When we estimate a visual correspondence map, e.g., depth map, optical flow, segmentation and so on, the estimated map often contains a number of noises and blurs. One of the solutions for this problem is post filtering. Edge-preserving filtering, such as joint bilateral filtering, can remove the noises, but it causes blurs on object boundaries at the same time. As an approach to remove noises without blurring, there is cost volume refinement filtering (CVRF) that is an effective solution for the refinement of such labeling of correspondence problems. There are some papers that propose several methods categorized into CVRF for various applications. These methods use various reconstructing metrics functions, which are L1 norm, L2 norm or exponential function, and various edge-preserving filters, which are joint bilateral filtering, guided image filtering and so on. In this paper, we generalize these factors and add range-spacial domain resizing factor for CVRF. Experimental results show that our generalized formulation outperform the conventional approaches, and also show what the format of CVRF is appropriate for various applications of stereo matching and optical flow estimation.

Citation Download Citation

Shu Fujita, Takuya Matsuo, Norishige Fukushima, and Yutaka Ishibashi "Cost volume refinement filter for post filtering of visual corresponding", Proc. SPIE 9399, Image Processing: Algorithms and Systems XIII, 93990Q (16 March 2015); https://doi.org/10.1117/12.2083086

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