A convex optimization approach for depth estimation under illumination variation
IEEE Transactions on Image Processing
Automatically Estimating and Updating Input-Output Tables
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part II
An integrated intelligent system for estimating and updating a large-size matrix
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Robust obstacle detection based on dense disparity maps
EUROCAST'07 Proceedings of the 11th international conference on Computer aided systems theory
Joint depth-motion dense estimation for multiview video coding
Journal of Visual Communication and Image Representation
Dense disparity MAP representations for stereo image coding
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Fixed point optimization algorithm and its application to network bandwidth allocation
Journal of Computational and Applied Mathematics
A modified parallel optimization system for updating large-size time-evolving flow matrix
Information Sciences: an International Journal
Distributed Intelligence for Constructing Economic Models
International Journal of Organizational and Collective Intelligence
| Hi-index | 35.68 |
A block-iterative parallel decomposition method is proposed to solve general quadratic signal recovery problems under convex constraints. The proposed method proceeds by local linearizations of blocks of constraints, and it is therefore not sensitive to their analytical complexity. In addition, it naturally lends itself to implementation on parallel computing architectures due to its flexible block-iterative structure. Comparisons with existing methods are carried out, and the case of inconsistent constraints is also discussed. Numerical results are presented.