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On Phase Based Stereo Matching And Its Related Issues

Authors: András Rövid; Takeshi Hashimoto;

On Phase Based Stereo Matching And Its Related Issues

Abstract

{"references": ["M. Young, E. Beeson, J. Davis, S. Rusinkiewicz and R. Ramamoorthi,\n\"Viewpoint-Coded Structured Light\", IEEE Computer Society Conference\non Computer Vision and Pattern Recognition, pages 18, June 2007", "E. Lilienblum, B. Michaelis, \"Optical 3D Surface Reconstruction by a\nMulti-Period Phase Shift Method, Journal of Computers, Vol. 2, No. 2,\npp. 73-83, 2007", "Yan Cui, Schuon, S., Chan, D., Thrun, S., Theobalt, C., \"3D Shape\nScanning with a Time-of-Flight Camera,\" 2010 IEEE Conference on\nComputer Vision and Pattern Recognition (CVPR), pp. 1173-1180, 13-18\nJune 2010", "N. Karpinsky, S. Zhang, \"High-Resolution, Real-Time 3D Imaging\nwith Fringe Analysis,\" Journal of Real-Time Image Processing,\nSpringer-Verlag, Vol. 7, Issue 1, pp. 55-66, 2010", "Raj Kumar Gupta, Siu-Yeung Cho, \"Window-Based Approach for Fast\nStereo Correspondence,\" IET Computer Vision, Vol. 7, Issue 2, ISSN\n1751-9632, pp. 123-134, April 2013.", "Adhyapak Satyajit Anil, Kehtarnavaz Nasser, Nadin Mihai, \"Stereo\nmatching via selective multiple windows,\" Journal of Electronic Imaging\nVol. 16, No. 1, pp. 013012-1\u2013013012-14, 2007.", "Boykov, Y.; Veksler, O.; Zabih, R., \"Fast approximate energy\nminimization via graph cuts,\" IEEE Transactions on Pattern Analysis and\nMachine Intelligence, Vol. 23, No. 11, pp. 1222\u20131239, Nov. 2001.", "M. Bleyer, M. Gelautz, \"Graph-Cut-Based Stereo Matching Using\nImage Segmentation with Symmetrical Treatment of Occlusions, Signal\nProcessing: Image Communication, Vol. 22, Issue 2, ISSN 0923-5965,\npp. 127\u2013143, Feb. 2007.", "Liu, Yonghuai, \"A Mean Field Annealing Approach to Accurate Free\nForm Shape Matching,\" Journal of Pattern Recognition, Vol. 40, Issue 9,\nISSN: 0031-3203, pp. 2418-2436, 2007.\n[10] Jian Sun; Nan-Ning Zheng; Heung-Yeung Shum, \"Stereo matching using\nbelief propagation,\" IEEE Transactions on Pattern Analysis and Machine\nIntelligence, Vol. 25, No. 7, pp. 787\u2013800, July 2003.\n[11] Sawires, E.F.; Hamdy, A.M.; Amer, F.Z.; Bakr, E. M., \"Disparity map\nusing suboptimal cost with dynamic programming,\" IEEE International\nSymposium on Signal Processing and Information Technology,\npp. 209\u2013214, 15-18 Dec. 2010.\n[12] Kachouane, M.; Sahki, S.; Lakrouf, M.; Ouadah, N., \"HOG based\nfast human detection,\" 24th International Conference on Microelectronics\n(ICM), pp. 1\u20134, 16\u201320 Dec. 2012.\n[13] Hoge, W.S., \"A Subspace Identification Extension to the Phase\nCorrelation Method [MRI application],\" IEEE Transactions on Medical\nImaging, Vol. 22, No. 2, pp. 277\u2013280, Feb. 2003.\n[14] Muquit, M. A., Shibahara, T., Aoki, T., \"A High-Accuracy Passive\n3D Measurement System Using Phase-Based Image Matching,\" IEICE\nTransactions, Vol. E89, No. 3, pp. 686\u2013697, 2006.\n[15] Z. Zhang, \"A Flexible New Technique for Camera Calibration,\"\nIEEE Transactions on Pattern Analysis and Machine Intelligence,\n22(11):1330\u20131334, 2000."]}

The paper focuses on the problem of the point correspondence matching in stereo images. The proposed matching algorithm is based on the combination of simpler methods such as normalized sum of squared differences (NSSD) and a more complex phase correlation based approach, by considering the noise and other factors, as well. The speed of NSSD and the preciseness of the phase correlation together yield an efficient approach to find the best candidate point with sub-pixel accuracy in stereo image pairs. The task of the NSSD in this case is to approach the candidate pixel roughly. Afterwards the location of the candidate is refined by an enhanced phase correlation based method which in contrast to the NSSD has to run only once for each selected pixel.

Keywords

phase correlation, Sub-pixel accuracy, SVD, Stereo matching, NSSD.

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