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Mathematical Problems in Engineering
Volume 2014, Article ID 452803, 14 pages
http://dx.doi.org/10.1155/2014/452803
Research Article

Feature Based Stereo Matching Using Two-Step Expansion

1School of Instrumentation Science & Opto-Electronics Engineering, Beihang University, Beijing 100191, China
2National Institute of Metrology, Beijing 100029, China

Received 2 January 2014; Revised 19 June 2014; Accepted 21 July 2014; Published 18 December 2014

Academic Editor: Yi Chen

Copyright © 2014 Liqiang Wang 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.

Citations to this Article [10 citations]

The following is the list of published articles that have cited the current article.

  • Sonam Nahar, and Manjunath V. Joshi, “Dense disparity estimation based on feature matching and IGMRF regularization,” 2016 23rd International Conference on Pattern Recognition (ICPR), pp. 3804–3809, . View at Publisher · View at Google Scholar
  • Hao-Hsueh Yang, Jian-Jiun Ding, Chia-Chun Hsu, and Yih-Cherng Lee, “Disparity estimation using adaptive superpixel assignment and gradient weight, binary cost, and dilation based local matching algorithm,” TENCON 2017 - 2017 IEEE Region 10 Conference, pp. 1010–1015, . View at Publisher · View at Google Scholar
  • Hua Shi, Hong Zhu, and Shunyuan Yu, “Disparity optimization algorithm on sub-pixel accuracy for stereo matching using segmentation guided filtering,” Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence, vol. 29, no. 10, pp. 865–875, 2016. View at Publisher · View at Google Scholar
  • Rostam Affendi Hamzah, Haidi Ibrahim, and Anwar Hasni Abu Hassan, “Stereo Matching Algorithm Based On Illumination Control To Improve The Accuracy,” Image Analysis & Stereology, vol. 35, no. 1, pp. 39–52, 2016. View at Publisher · View at Google Scholar
  • Abiel Aguilar-González, and Miguel Arias-Estrada, “An FPGA stereo matching processor based on the sum of hamming distances,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9625, pp. 66–77, 2016. View at Publisher · View at Google Scholar
  • Zhong-jie Zhu, and Qin-yan Dai, “Hybrid scheme for accurate stereo matching,” Neurocomputing, 2017. View at Publisher · View at Google Scholar
  • Sonam Nahar, and Manjunath V. Joshi, “A learned sparseness and IGMRF-based regularization framework for dense disparity estimation using unsupervised feature learning,” IPSJ Transactions on Computer Vision and Applications, vol. 9, no. 1, 2017. View at Publisher · View at Google Scholar
  • Qingwu Li, Yan Liu, Jinxin Xu, and Yifei You, “Stereo Matching Algorithm Based on Color Weights and Tree Dynamic Programming,” Guangxue Xuebao/Acta Optica Sinica, vol. 37, no. 12, 2017. View at Publisher · View at Google Scholar
  • Gang Liu, Chun-Hai Hu, Shu-Tao Wang, Hua Chen, and Zhi-Juan Zhang, “A two-step stereo matching based on the local texture features and image segmentation,” Jiliang Xuebao/Acta Metrologica Sinica, vol. 38, no. 1, pp. 73–77, 2017. View at Publisher · View at Google Scholar
  • Qingwu Li, Jinyan Ni, Yunpeng Ma, and Jinxin Xu, “Stereo matching using census cost over cross window and segmentation-based disparity refinement,” Journal of Electronic Imaging, vol. 27, no. 02, pp. 1, 2018. View at Publisher · View at Google Scholar