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Proceedings Paper

Cost volume refinement filter for post filtering of visual corresponding
Author(s): Shu Fujita; Takuya Matsuo; Norishige Fukushima; Yutaka Ishibashi
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Paper 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.

Paper Details

Date Published: 16 March 2015
PDF: 9 pages
Proc. SPIE 9399, Image Processing: Algorithms and Systems XIII, 93990Q (16 March 2015); doi: 10.1117/12.2083086
Show Author Affiliations
Shu Fujita, Nagoya Institute of Technology (Japan)
Takuya Matsuo, Nagoya Institute of Technology (Japan)
Norishige Fukushima, Nagoya Institute of Technology (Japan)
Yutaka Ishibashi, Nagoya Institute of Technology (Japan)

Published in SPIE Proceedings Vol. 9399:
Image Processing: Algorithms and Systems XIII
Karen O. Egiazarian; Sos S. Agaian; Atanas P. Gotchev, Editor(s)

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