Regular Stereo Matching Improvement System Based on Kinect-supporting Mechanism


In this paper, we built a stereoscopic video associated experimental model, which is referenced as Kinect-supporting improved stereo matching scheme. As the depth maps offered by the Kinect IR-projector are resolution-inadequate, noisy, distance-limited, unstable, and material-sensitive, the appropriated de-noising, stabilization and filtering are first performed for retrieving useful IR-projector depths. The disparities are linearly computed from the refined IR-projector depths to provide specifically referable disparity resources. By exploiting these resources with sufficiency, the proposed mechanism can lead to great enhancement on both speed and accuracy of stereo matching processing to offer better extra virtual view generation and the possibility of price-popularized IR-projector embedded stereoscopic camera.

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D. Chan and C. Hsu, "Regular Stereo Matching Improvement System Based on Kinect-supporting Mechanism," Open Journal of Applied Sciences, Vol. 3 No. 1B, 2013, pp. 22-26. doi: 10.4236/ojapps.2013.31B005.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Y. Taguchi, T. Koike, K. Takahashi, T. Naemura, “TransCAIP: A Live 3D TV System Using a Camera Array and an Integral Photography Display with Interac-tive Control of Viewing Parameters,” IEEE Transactions on Visualization and Computer Graphics, vol.15, no.5, pp.841-852, Sep. 2009.
[2] W. J. Tam, G. Alain, L. Zhang, T. Martin, R. Renaud, “Smoothing depth maps for improved stereoscopic image quality,” Three-Dimensional TV, Video, and Display III(Proceedings of the SPIE), Vol. 5599, pp. 162-172,2004.
[3] L. Zhang, W.J. Tam, “Stereoscopic image generation based on depth images for 3D TV,” IEEE Transactions on Broadcasting, vol.51, no.2, pp.191-199, Jun. 2005.
[4] J. Zhu, L. Wang, R. Yang, J.E. Davis, Z. Pan, “Reliability Fusion of Time-of-Flight Depth and Stereo Geometry for High Quality Depth Maps,” IEEE Transactions on Pattern Analysis and Ma-chine Intelligence, vol.33, no.7, pp.1400-1414, Jul. 2011.
[5] W.C. Chiu, U. Blanke, M. Fritz, “Improving the Kinect by Cross-Modal Stereo,” The 22nd British Machine Vision Conference (BMVC 2011), pp.116.1-116.10, Sep. 2010.
[6] K.J. Yoon and I.S. Kweon, “Locally adaptive support-weight approach for visual correspondence search,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp.924-931, 2005.
[7] X. Mei, X. Sun, M. Zhou, S. Jiao, H. Wang and X. Zhang, “On Building an Accurate Stereo Matching System on Graphics Hardware,” GPUCV'11: ICCV Workshop on GPU in Computer Vision Applications, 2011

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