NDLI: Matching Cost Fusion in Dense Depth Recovery for Camera-Array via Global Optimization

Content Provider	IEEE Xplore Digital Library
Author	Lipeng Si Qing Wang Zhaolin Xiao
Copyright Year	2014
Description	Author affiliation: Sch. of Comput. Sci., Northwestern Polytech. Univ., Xi'an, China (Lipeng Si; Qing Wang; Zhaolin Xiao)
Abstract	This paper proposes a novel method of fusing different matching cost for dense depth map recovery in a global optimization framework. Two simple classical cost functions, NCC and SAD, are combined to make a complementary costs fusion, which is robust against noises and weak radiometric difference. We address complicated difficulties as texture-less region and occlusion in a multi-view energy based global optimization, which is efficiently solved via graph cuts algorithm. We evaluate our cost fusion and optimization algorithm on camera-array captured scenes. The experimental results demonstrate that, our cost fusion get better result than single cost function, and our multi-view optimization gains greatly than stereo method, that means our algorithm is appropriate for camera-array against complex difficulties.
Starting Page	180
Ending Page	185
File Size	819237
Page Count	6
File Format	PDF
e-ISBN	9781479968541
DOI	10.1109/ICVRV.2014.67
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-30
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Correlation Camera-array Noise Cost function Cameras Radiometry Matching cost fusion Robustness Dense depth Graph cuts
Content Type	Text
Resource Type	Article

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