Non-Rigid Structure from Motion for Complex Motion

Recovering deformable 3D motion from temporal 2D point tracks in a monocular video is an open problem with many everyday applications throughout science and industry, or the new augmented reality. Recently, several techniques have been proposed to deal the problem called Non-Rigid Structure from Motion (NRSfM), however, they can exhibit poor reconstruction performance on complex motion. In this project, we will analyze these situations for primitive human actions such as walk, run, sit, jump, etc. on different scenarios, reviewing first the current techniques to finally present our novel method. This approach is able to model complex motion into a union of subspaces, rather than the summation occurring in standard low-rank shape methods, allowing better reconstruction accuracy. Experiments in a wide range of sequences and types of motion illustrate the benefits of this new approac

Inclusive distributions near kinematic thresholds.

The main challenge in computing inclusive cross sections and decay spectra in QCD is posed by kinematic thresholds. The threshold region is characterized by stringent phase–space constraints that are reflected in large perturbative corrections due to soft and collinear radiation as well as large non-perturbative effects. Major progress in addressing this problem was made in recent years by Dressed Gluon Exponentiation (DGE), a formalism that combines Sudakov and renormalon resummation in moment space. DGE has proven effective in extending the range of applicability of perturbation theory well into the threshold region and in identifying the relevant non-perturbative corrections. Here we review the method from a general perspective using examples from deep inelastic structure functions, event– shape distributions, heavy–quark fragmentation and inclusive decay spectra. A special discussion is devoted to the applications of DGE to radiative and semileptonic B decays that have proven valuable for the interpretation of data from the B factories

Space and camera path reconstruction for omni-directional vision

In this paper, we address the inverse problem of reconstructing a scene as well as the camera motion from the image sequence taken by an omni-directional camera. Our structure from motion results give sharp conditions under which the reconstruction is unique. For example, if there are three points in general position and three omni-directional cameras in general position, a unique reconstruction is possible up to a similarity. We then look at the reconstruction problem with m cameras and n points, where n and m can be large and the over-determined system is solved by least square methods. The reconstruction is robust and generalizes to the case of a dynamic environment where landmarks can move during the movie capture. Possible applications of the result are computer assisted scene reconstruction, 3D scanning, autonomous robot navigation, medical tomography and city reconstructions

Temporally coherent 4D reconstruction of complex dynamic scenes

This paper presents an approach for reconstruction of 4D temporally coherent models of complex dynamic scenes. No prior knowledge is required of scene structure or camera calibration allowing reconstruction from multiple moving cameras. Sparse-to-dense temporal correspondence is integrated with joint multi-view segmentation and reconstruction to obtain a complete 4D representation of static and dynamic objects. Temporal coherence is exploited to overcome visual ambiguities resulting in improved reconstruction of complex scenes. Robust joint segmentation and reconstruction of dynamic objects is achieved by introducing a geodesic star convexity constraint. Comparative evaluation is performed on a variety of unstructured indoor and outdoor dynamic scenes with hand-held cameras and multiple people. This demonstrates reconstruction of complete temporally coherent 4D scene models with improved nonrigid object segmentation and shape reconstruction.Comment: To appear in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016 . Video available at: https://www.youtube.com/watch?v=bm_P13_-Ds

Human-display interactions: Context-specific biases

Recent developments in computer engineering have greatly enhanced the capabilities of display technology. As displays are no longer limited to simple alphanumeric output, they can present a wide variety of graphic information, using either static or dynamic presentation modes. At the same time that interface designers exploit the increased capabilities of these displays, they must be aware of the inherent limitation of these displays. Generally, these limitations can be divided into those that reflect limitations of the medium (e.g., reducing three-dimensional representations onto a two-dimensional projection) and those reflecting the perceptual and conceptual biases of the operator. The advantages and limitations of static and dynamic graphic displays are considered. Rather than enter into the discussion of whether dynamic or static displays are superior, general advantages and limitations are explored which are contextually specific to each type of display

An Efficient Coding Theory for a Dynamic Trajectory Predicts non-Uniform Allocation of Grid Cells to Modules in the Entorhinal Cortex

Grid cells in the entorhinal cortex encode the position of an animal in its environment using spatially periodic tuning curves of varying periodicity. Recent experiments established that these cells are functionally organized in discrete modules with uniform grid spacing. Here we develop a theory for efficient coding of position, which takes into account the temporal statistics of the animal's motion. The theory predicts a sharp decrease of module population sizes with grid spacing, in agreement with the trends seen in the experimental data. We identify a simple scheme for readout of the grid cell code by neural circuitry, that can match in accuracy the optimal Bayesian decoder of the spikes. This readout scheme requires persistence over varying timescales, ranging from ~1ms to ~1s, depending on the grid cell module. Our results suggest that the brain employs an efficient representation of position which takes advantage of the spatiotemporal statistics of the encoded variable, in similarity to the principles that govern early sensory coding.Comment: 23 pages, 5 figures. Supplemental Information available from the authors on request. A previous version of this work appeared in abstract form (Program No. 727.02. 2015 Neuroscience Meeting Planner. Chicago, IL: Society for Neuroscience, 2015. Online.