Compressive sensing is a methodology for the reconstruction of sparse or compressible signals using far fewer samples than required by the Nyquist criterion. However, many of the results in compressive sensing concern random sampling matrices such as Gaussian and Bernoulli matrices. In common physically feasible signal acquisition and reconstruction scenarios such as super-resolution of images,...

Super-resolution is a process that combines information from some low-resolution images in order to produce an image with higher resolution. In most of the previous related work, the blurriness that is associated with low resolution images is assumed to be due to the integral effect of the acquisition device’s image sensor. However, in practice there are other sources of blurriness as well, inc...

3D reconstruction pipelines using structure-from-motion and multi-view stereo techniques are today able to reconstruct impressive, large-scale geometry models from images but do not yield textured results. Current texture creation methods are unable to handle the complexity and scale of these models. We therefore present the first comprehensive texturing framework for large-scale, real-world 3D...

The purpose of this study was to evaluate the effects of voxel size and iterative reconstruction parameters on the radial and tangential resolution for 99mTc SPECT as a function of radial distance from isocenter. SPECT/CT scans of eight coplanar point sources of size smaller than 1 mm3 containing high concentration 99mTc solution were acquired on a SPECT/CT system with 5/8 inch NaI(Tl) detector...

In this paper, we address the problem of super-resolution from multiple low-resolution omnidirectional images with inexact registration. Such a problem is typically encountered in omnidirectional vision scenarios with reduced resolution sensors in imperfect settings. Several spherical images with arbitrary rotations in the SO(3) rotation group are used for the reconstruction of higher resolutio...

BACKGROUND One of the limiting factors in achieving the best spatial resolution in positron emission tomography (PET), especially in small-animal PET, is the positron range associated with the decay of nuclides, and usual PET image reconstruction algorithms do not provide a correction for the positron range. This work presents initial results obtained with the maximum a posteriori (MAP) algorit...

. Paraphrasing Olivier Faugeras in the foreword of [1], making a robot see is still an unsolved and challenging task after several decades of research. The traditional research has been based on the geometric models of multiple views of a scene, estimating a sparse 3D map of the scene and the camera pose. Recent advances have led to fully dense and real-time 3D reconstructions. Also, there are ...

The implementation of a Structure-from-Motion (SfM) pipeline from a synthetically generated scene as well as the investigation of the faithfulness of diverse reconstructions is the subject of this project. A series of different SfM reconstructions are implemented and their camera pose estimations are being contrasted with their respective ground truth locations. Finally, injection of ground tru...

The study of sign languages provides a wide field of applications for computer vision systems, but the analysis of such gestures often leads to complex 3D reconstructions or to ambiguities. In this paper, we describe the architecture of an image analysis system that performs sign language analysis by using a prediction/verification approach. The system integrates a model of the sign language’s ...

Undersampled images, such as those produced by the HST WFPC-2, misrepresent fine-scale structure intrinsic to the astronomical sources being imaged. Analyzing such images is difficult on scales close to their resolution limits and may produce erroneous results. A set of “dithered” images of an astronomical source generally contains more information about its structure than any single undersampl...