Rami Ben-Ari

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This paper addresses the problem of correspondence establishment in binocular stereo vision. We suggest a novel variational approach that considers both the discontinuities and occlusions. It deals with color images as well as gray levels. The proposed method divides the image domain into the visible and occluded regions where each region is handled(More)
This paper addresses the problem of correspondence establishment in binocular stereo vision. We suggest a novel spatially continuous approach for stereo matching based on the variational framework. The proposed method suggests a unique regularization term based on Mumford-Shah functional for discontinuity preserving, combined with a new energy functional(More)
We evaluate the dense optical flow between two frames via vari-ational approach. In this paper, a new framework for deriving the regularization term is introduced giving a geometric insight into the action of a smoothing term. The framework is based on the Beltrami paradigm in image denoising. It includes a general formulation that unifies several previous(More)
The problem of dense optical flow computation is addressed from a variational viewpoint. A new geometric framework is introduced. It unifies previous art and yields new efficient methods. Along with the framework a new alignment criterion suggests itself. It is shown that the alignment between the gradients of the optical flow components and between the(More)
Visual tracking is considered a common procedure in many real-time applications. Such systems are required to track objects under changes in illumination, dynamic viewing angle, image noise and occlusions (to name a few). But to maintain real-time performance despite these challenging conditions, tracking methods should require extremely low computational(More)
Every stereovision application must cope with the correspondence problem. The space of the matching variables, often consisting of spatial coordinates, intensity and disparity, is commonly referred as the data term (space). Since the data is often noisy a-priori, preference is required to result a smooth disparity (or piecewise smooth). To this end, each(More)
The correspondence problem in stereo vision is notoriously difficult. In many approaches a noisy solution is extracted from the correspondence space. Various sophisticated regularization techniques are applied then on this noisy solution. We study here the possibility to denoise the correspondence/correlation space before extracting the solution, by a(More)
Depth from Defocus (DFD) suggests a simple optical set-up to recover the shape of a scene through imaging with shallow depth of field. Although numerous methods have been proposed for DFD, less attention has been paid to the particular problem of alignment between the captured images. The inherent shift-variant defocus often prevents standard registration(More)