Parallel implementations of a disparity estimation algorithm based on a Proximal splitting method

The Parallel Proximal Algorithm (PPXA+) has been recently introduced as an efficient tool for solving convex optimization problems. It has proved particularly effective in the context of stereo vision, used as the methodological core of a novel disparity estimation technique. In this work, the main methodological issues limiting the efficient parallelization of this technique are addressed, and further modifications are proposed to enable and optimize the design of parallel implementations. Finally, actual implementations that fit both the multi-core CPU and GPU devices are provided and tested to validate the performance potential of the proposed technique.

[1]  Joumana Farah,et al.  Disparity map estimation under convex constraints using proximal algorithms , 2011, 2011 IEEE Workshop on Signal Processing Systems (SiPS).

[2]  Guanghui Wang,et al.  Stereo matching algorithm based on curvelet decomposition and modified support weights , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[3]  Fan Zhou,et al.  Fast disparity estimation using spatio-temporal correlation of disparity field for multiview video coding , 2010, IEEE Transactions on Consumer Electronics.

[4]  Vladimir Kolmogorov,et al.  Computing visual correspondence with occlusions using graph cuts , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[5]  Nelly Pustelnik,et al.  A proximal approach for constrained cosparse modelling , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[6]  V. Volkov,et al.  Fitting FFT onto the G 80 Architecture , 2008 .

[7]  D. Scharstein,et al.  A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms , 2001, Proceedings IEEE Workshop on Stereo and Multi-Baseline Vision (SMBV 2001).

[8]  P. L. Combettes.  The foundations of set theoretic estimation , 1993 .

[9]  Michael P. Friedlander,et al.  Probing the Pareto Frontier for Basis Pursuit Solutions , 2008, SIAM J. Sci. Comput..

[10]  Béatrice Pesquet-Popescu,et al.  OpenCL implementation of motion estimation for cloud video processing , 2011, 2011 IEEE 13th International Workshop on Multimedia Signal Processing.

[11]  Daniel Cremers,et al.  A Convex Formulation of Continuous Multi-label Problems , 2008, ECCV.

[12]  J. Pesquet,et al.  A Parallel Inertial Proximal Optimization Method , 2012 .

[13]  Ruigang Yang,et al.  Global stereo matching leveraged by sparse ground control points , 2011, CVPR 2011.

[14]  Jean-Christophe Pesquet,et al.  A Convex Optimization Approach for Depth Estimation Under Illumination Variation , 2009, IEEE Transactions on Image Processing.

[15]  J. Li,et al.  An Epipolar Geometry-Based Fast Disparity Estimation Algorithm for Multiview Image and Video Coding , 2007, IEEE Transactions on Circuits and Systems for Video Technology.