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Research paper

Accurate stereo matching algorithm based on cost aggregation with adaptive support weight

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Pages 423-432 | Received 03 Mar 2015, Accepted 11 Jul 2015, Published online: 30 Jul 2015

References

  • Hancock, J., Hebert, M. and Thorpe, C. Laser intensity-based obstacle detection intelligent robots and systems. IEEE/RSJ Int. Conf. Intell. Rob. Syst., 1998, 3, 1541–1546.
  • Burguera, A., González, Y. and Oliver, G. Sonar sensor models and their application to mobile robot localization. Sensors, 2009, 9/12, 10217–10243. doi: 10.3390/s91210217
  • Kinect for windows, voice, movement and gesture recognition technology. http://www.microsoft.com/en-us/kinectforwindows/, accessed August 2013..
  • Kanade, T. and Okutomi, M. A stereo matching algorithm with adaptive window: theory and experiments. IEEE Trans. Pattern Anal. Mach. Intell., 1994, 16/9, 920–932. doi: 10.1109/34.310690
  • Scharstein, D. and Szeliski, R. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vision, 2002, 47/1-3, 7–42. doi: 10.1023/A:1014573219977
  • Sizintsev, M. and Wildes, R. P. Coarse-to-fine stereo vision with accurate 3D boundaries. Image Vision Comput., 2010, 28, 352–366. doi: 10.1016/j.imavis.2009.06.008
  • Yang, R. and Pollefeys, M. Multi-resolution real-time stereo on commodity graphics hardware. ‘Proc. Int. Conf. on ‘Computer vision and pattern recognition’, 2003, pp. 211–217.
  • Nalpantidis, L. and Gasteratos, A. Biologically and psychophysically inspired adaptive support weights algorithm for stereo correspondence. Rob. Auton. Syst., 2010, 58, 457–464. doi: 10.1016/j.robot.2010.02.002
  • Zhang, K., Lu, J. and Lafruit, G. Cross-based local stereo matching using orthogonal integral images. IEEE Trans. Circuits Syst. Video Technol., 2009, 19/7, 1073–1079. doi: 10.1109/TCSVT.2009.2020478
  • Fusiello, A., Roberto, V. and Trucco, E. Efficient stereo with multiple windowing. ‘Proc. Comput. Vision Pattern Recognit’, 1997, pp. 858–863.
  • Boykov, Y., Veksler, O. and Zabih, R. A variable window approach to early vision. IEEE Trans. Pattern Anal. Mach. Intell., 1998, 20/12, 1283–1294. doi: 10.1109/34.735802
  • Bertozzi, M., Broggi, A. and Fascioli, A. Stereo inverse perspective mapping: theory and applications. Image Vision Comput., 1998, 16/8, 585–590. doi: 10.1016/S0262-8856(97)00093-0
  • Deng, Y. and Lin, X. A fast line segment based dense stereo algorithm using tree dynamic programming. ‘Proc. 9th European Conf. on ‘Computer vision’, Graz, Austria 2006, pp. 201–212.
  • Fang, Y., Masaki, I. and Horn, B. Depth-based target segmentation for intelligent vehicles fusion of radar and binocular stereo. IEEE Intell. Prot. Syst., 2002, 3/3, 196–202.
  • Yoon, K. J. and Kweon, I. S. Adaptive support-weight approach for correspondence search. IEEE Trans. Pattern Anal. Mach. Intell., 2006, 28, 650–656. doi: 10.1109/TPAMI.2006.70
  • Nalpantidis, L. and Gasteratos, A. Stereo vision for robotic applications in the presence of non-ideal lighting conditions. Image Vision Comput., 2010, 28, 940–951. doi: 10.1016/j.imavis.2009.11.011
  • Birchfield, S., Natarajan, B. and Tomasi, C. Correspondence as energy-based segmentation. Image Vision Comput., 2007, 25/8, 1329–1340. doi: 10.1016/j.imavis.2006.08.001
  • Klaus, A., Sormann, M. and Karner, K. Segment-based stereo matching using belief propagation and a self-adapting dissimilarity measure. ‘Proc. Int. Conf. on ‘Pattern recognition’, 2006, pp. 15–18.
  • Tombari, F., Mattoccia, S. and Stefano, L. D. Segmentation-based adaptive support for accurate stereo correspondence. Adv. Image Video Technol., 2007, 2872/10, 427–438. doi: 10.1007/978-3-540-77129-6_38
  • Camera calibration toolbox for Matlab. http://www.vision.caltech.edu/bouguetj/calib_doc/, accessed July 2010..
  • Brown, M. Z., Burschka, D. and Hager, G. D. Advances in computational stereo. IEEE Trans. Pattern Anal. Mach. Intell., 2003, 25/8) doi: 10.1109/TPAMI.2003.1217603
  • Kang, S. B., Szeliski, R. and Chai, J. Handling occlusions in dense multi-view stereo., Proc. Conf. on ‘Computer vision and pattern recognition’ 2001, pp. 103–110..
  • Mei, X., Sun, X., Zhou, M. S., et al. On building an accurate stereo matching system on graphics hardware. IEEE Int. Conf. on ‘Computer vision workshops. Nov 2011, 467–474.
  • Rhemann, C., Hosni, A., Bleyer, M., et al. Fast cost-volume filtering for visual correspondence and beyond., Proc. Conf. Computer Vision and Pattern Recognition, Washington, DC, USA 2011, pp. 3017–3024..
  • Zhang, Z. Determining the epipolar geometry and its uncertainty: a review. Int. J. Comput. Vision, 1998, 27/2, 161–195. doi: 10.1023/A:1007941100561
  • Egnal, G. and Wildes, R. P. Detecting binocular half-occlusions:empirical comparisons of five approaches. IEEE Trans. Pattern Anal. Mach. Intell., 2002, 24/8, 1127–1133. doi: 10.1109/TPAMI.2002.1023808
  • Middlebury stereo datasets., http://vision.middlebury.edu/stereo/data/, accessed July 2010..

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