… systems over noisy channels employing Bregman divergences along with regularization … at the reception due to bit-errors or cell loss in packet networks. Bregman regularization … accuracy and stability. This technique has an adaptive nature: the regularization …
In Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning. 2012: 650-657 [doi]… regularization. The outcome of the regularized solution is dependent upon the value of the regularization parameter. This work employs a data-driven approach called … of the regularization parameter for each pixel. Experimental results are presented …
icip 1998: 200-203 [doi]… systems over noisy channels employing Bregman divergences along with regularization … at the reception due to bit-errors or cell loss in packet networks. Bregman regularization … accuracy and stability. This technique has an adaptive nature: the regularization …
ideal 2012: 650-657 [doi]… that requires regulation. In the past, regularization for pel-recursive estimations … and regularization parameters are estimated in an iterative fashion by means … utilizes the local image properties to regularize the motion vector estimates …
2000: [doi]… regularization matrix such as ?=diag{?1,…,?K} to robustify motion estimation instead …, for the optical flow problem. Intuitively, this regularization scheme makes sense …
International Journal of Computer Applications, 51(19):17-24, 2012. [doi]… The computation of 2-D optical flow by means of regularized pel-recursive … to estimate the best regularization scheme for a given pixel. In our model, the regularization parameter is a matrix whose entries can account for diverse sources …
sibgrapi 2003: 331-338 [doi]… of regularization; (ii) to define and to explain the role of a particular type of regularization called total variation norm (TV-norm) in computer vision tasks; (iii … blur the edges of the estimated images, however TV regularization preserves …
ArXiv e-prints, , mar 2016.