3D Super-resolution Using Generalised Sampling Expansion

Using a probabilistic interpretation of Papoulis' generalized sampl ing theorem, an iterative algorithm has been devised for 3D reconstruction of a Lambertian surface at sub-pixel accuracy. The problem has been formulated as a n optimization one in a Bayesian framework. The latter allows for introducing { \em a priori} information on the solution, using Markov Random Fields (MRF). Th e estimated 3D features of the surface are the albedo and the height which are obtained simultaneously using a set of low resolution images

Subpixel Image Registration by Estimating the Polyphase Decomposition of the Cross Power Spectrum

A method of registering images at subpixel accuracy has been propos ed, which does not resort to interpolation. The method is based on the phase co rrelation method and is remarkably robust to correlated noise and uniform varia tions of luminance. We have shown that the cross power spectrum of two images, containing subpixel shifts, is a polyphase decomposition of a Dirac delta funct ion. By estimating the sum of polyphase components one can then determine subpi xel shifts along each axis

Reconstruction of high resolution 3D visual information

Given a set of low resolution camera images, it is possible to reconstruct high resolution luminance and depth information, specially if the relative displacements of the image frames are known. We have proposed iterative algorithms for recovering high resolution albedo and depth maps that require no a a priori knowledge of the schene and therefore do not depend on other methods, as regards boundary and initial conditions. The problem of surface reconstruction has been formulated as that of Expectation Maximization (EM) and has been tackled in a probabilistic framework using Markov Random Fields (MRF). As for the depth map, our method is directly recovering surface heights without refering to surface orientations, while increasing the resolution by camera jittering. Conventional statistical models have been coupled with geometrical techniques to construct general models of the world and the imaging process

Direct Search Generalized Simplex Algorithm for Optimizing Non-linear Functions

: Multivariable optimisation techniques have long been used in all fields for improving the design and performance of systems. Yet the number of well known algorithms that can effectively be used under realistic conditions is usually limited due to many practical considerations such as the limit of applicability to certain classes of problems, the time and computational cost of them under conditions of the problem and more importantly, the efficiency of these algorithms under noisy conditions, which is indeed the case in almost all practical problems. Variants of simplex algorithm have been named since 60's as efficient algorithms in noisy situations. However, no theoretical results have been stablished as regards their convergence and computational efficiency. In this report, we have generalized the simplex method and have addressed theoretical aspects concerning the convergence of the algorithm. Key-words: simplex method, search algorithms, optimization. (R'esum'e : tsvp) hshekar@s..

Reconstruction of high resolution 3D visual information

Given a set of low resolution camera images, it is possible to reconstruct high resolution luminance and depth information, specially if the relative displacements of the image frames are known. We have proposed iterative algorithms for recovering high resolution albedo and depth maps that require no a a priori knowledge of the schene and therefore do not depend on other methods, as regards boundary and initial conditions. The problem of surface reconstruction has been formulated as that of Expectation Maximization (EM) and has been tackled in a probabilistic framework using Markov Random Fields (MRF). As for the depth map, our method is directly recovering surface heights without refering to surface orientations, while increasing the resolution by camera jittering. Conventional statistical models have been coupled with geometrical techniques to construct general models of the world and the imaging process

3D super-resolution using generalised sampling expansion

Programme 4 - Robotique, image et vision. Projet PASTISSIGLEAvailable at INIST (FR), Document Supply Service, under shelf-number : 14802 E, issue : a.1995 n.2706 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

Subpixel image registration by estimating the polyphase decomposition of the cross power spectrum

Programme 4 - Robotique, image et vision. Projet PASTISSIGLEAvailable at INIST (FR), Document Supply Service, under shelf-number : 14802 E, issue : a.1995 n.2707 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

Reconstruction of high resolution 3D visual information

Programme 4 : robotique, image et visionSIGLEAvailable at INIST (FR), Document Supply Service, under shelf-number : 14802 E, issue : a.1993 n.2142 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc