Sorry, you need to enable JavaScript to visit this website.

Inverse problems encountered in video processing often require to minimize criteria involving a high number of variables. Among available optimization techniques, proximal methods have shown their efficiency in solving large-scale possibly nonsmooth problems. When some of the proximity operators involved in these methods do not have closed form expressions, they may constitute a bottleneck in terms of computational complexity and memory

Correlation-based watermark detection inherently assumes that a pseudo-random watermark pattern is present in the cover signal at a given location. However, disparity-coherent watermarks for stereo video content are broken into pieces which are offset one with respect to the others. Acoustic transmission yields echoes due to multi-path propagation and may thus introduce watermark replicates in the recorded audio track. In such cases, the watermark energy is not concentrated in a single element of the cross-correlation array but rather distributed over a number of them.

Accurate and robust spot tracking is a necessary tool for quantitative motion analysis in fluorescence microscopy images. In this work, we exploits the underlying stationary motion in biological systems, e.g. the movement of crowds, bacteria swarming and cyclosis in plant cells, and then propose a multi-frame optical flow based tracker. We obtain the stationary motion by adapting a recent optical flow algorithm that relates one image to another locally using an all-pass filter.

In this work, a 3D modelization of the surrounding environment is enabled with an improvised ad-hoc camera networks of both static and mobile devices (cloud vision network).

Several problems in signal processing and machine learning can be casted as optimization problems. In many cases, they are of large-scale, nonlinear, have constraints, and nonsmooth in the unknown parameters. There exists plethora of fast algorithms for smooth convex optimization, but these algorithms are not readily applicable to nonsmooth problems, which has led to a considerable amount of research in this direction. In this paper, we propose a general algorithm for nonsmooth bound-constrained convex optimization problems.

Several problems in signal processing and machine learning can be casted as optimization problems. In many cases, they are of large-scale, nonlinear, have constraints, and nonsmooth in the unknown parameters. There exists plethora of fast algorithms for smooth convex optimization, but these algorithms are not readily applicable to nonsmooth problems, which has led to a considerable amount of research in this direction. In this paper, we propose a general algorithm for nonsmooth bound-constrained convex optimization problems.

Low-cost physically unclonable functions (PUFs) can be deployed with consumer products to deter counterfeiting. An intrinsic physical property - unique textures of paper or other surface - has received strong interest. Extrinsically introduced features, such as randomly positioned bubbles and fiber segments, have also been deployed in the industry to facilitate authentication. This paper carries out a study to gain a better understanding in the factors affecting the authentication performance, with a consideration of the friendliness under mobile imaging.