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Journal of Sensors
Volume 2017, Article ID 9374870, 12 pages
https://doi.org/10.1155/2017/9374870
Research Article

A Nonlocal Method with Modified Initial Cost and Multiple Weight for Stereo Matching

1College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
2School of Information Engineering, Zhejiang University of Water Resources and Electric Power, Hangzhou 310018, China
3School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China

Correspondence should be addressed to Shenyong Gao; nc.ude.udh@ysoag

Received 7 April 2017; Revised 5 June 2017; Accepted 20 June 2017; Published 6 August 2017

Academic Editor: Wendy Flores-Fuentes

Copyright © 2017 Shenyong Gao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper presents a new nonlocal cost aggregation method for stereo matching. The minimum spanning tree (MST) employs color difference as the sole component to build the weight function, which often leads to failure in achieving satisfactory results in some boundary regions with similar color distributions. In this paper, a modified initial cost is used. The erroneous pixels are often caused by two pixels from object and background, which have similar color distribution. And then inner color correlation is employed as a new component of the weight function, which is determined to effectively eliminate them. Besides, the segmentation method of the tree structure is also improved. Thus, a more robust and reasonable tree structure is developed. The proposed method was tested on Middlebury datasets. As can be expected, experimental results show that the proposed method outperforms the classical nonlocal methods.