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An efficient local method for stereo matching using daisy features

Conference Paper


Abstract


  • In this paper, a local method is proposed to estimate the visibility and disparity of pixels from a stereo pair using the DAISY feature. The problem is formulated as a joint optimization over disparity and visibility of individual pixels. The constraints on the range of disparities and the binary visibility variables are enforced by incorporating penalty terms into the cost function. Finally, the unconstrained optimization problem is solved using a Newton scheme with appropriate approximations to the Hessian matrices and gradients. The computation time of the proposed optimization method is around one minute to run for 768 × 512 stereo pairs using the DAISY feature descriptor in a C++ implementation.

Publication Date


  • 2017

Citation


  • X. Peng, A. Bouzerdoum & S. Phung, "An efficient local method for stereo matching using daisy features," in IEEE International Conference on Image Processing, ICIP 2017, 2017, pp. 2503-2507.

Scopus Eid


  • 2-s2.0-85045329519

Start Page


  • 2503

End Page


  • 2507

Place Of Publication


  • United States

Abstract


  • In this paper, a local method is proposed to estimate the visibility and disparity of pixels from a stereo pair using the DAISY feature. The problem is formulated as a joint optimization over disparity and visibility of individual pixels. The constraints on the range of disparities and the binary visibility variables are enforced by incorporating penalty terms into the cost function. Finally, the unconstrained optimization problem is solved using a Newton scheme with appropriate approximations to the Hessian matrices and gradients. The computation time of the proposed optimization method is around one minute to run for 768 × 512 stereo pairs using the DAISY feature descriptor in a C++ implementation.

Publication Date


  • 2017

Citation


  • X. Peng, A. Bouzerdoum & S. Phung, "An efficient local method for stereo matching using daisy features," in IEEE International Conference on Image Processing, ICIP 2017, 2017, pp. 2503-2507.

Scopus Eid


  • 2-s2.0-85045329519

Start Page


  • 2503

End Page


  • 2507

Place Of Publication


  • United States