Learning Rank Reduced Interpolation with Principal Component Analysis

In computer vision most iterative optimization algorithms, both sparse and dense, rely on a coarse and reliable dense initialization to bootstrap their optimization procedure. For example, dense optical flow algorithms profit massively in speed and robustness if they are initialized well in the basin of convergence of the used loss function. The same holds true for methods as sparse feature tracking when initial flow or depth information for new features at arbitrary positions is needed. This makes it extremely important to have techniques at hand that allow to obtain from only very few available measurements a dense but still approximative sketch of a desired 2D structure (e.g. depth maps, optical flow, disparity maps, etc.). The 2D map is regarded as sample from a 2D random process. The method presented here exploits the complete information given by the principal component analysis (PCA) of that process, the principal basis and its prior distribution. The method is able to determine a dense reconstruction from sparse measurement. When facing situations with only very sparse measurements, typically the number of principal components is further reduced which results in a loss of expressiveness of the basis. We overcome this problem and inject prior knowledge in a maximum a posterior (MAP) approach. We test our approach on the KITTI and the virtual KITTI datasets and focus on the interpolation of depth maps for driving scenes. The evaluation of the results show good agreement to the ground truth and are clearly better than results of interpolation by the nearest neighbor method which disregards statistical information.Comment: Accepted at Intelligent Vehicles Symposium (IV), Los Angeles, USA, June 201

Sistemas de navegación en plataformas móviles mediante odometría visual

En este documento se presenta un modo de detectar y rastrear características en secuencias de imágenes (vídeos), grabadas por una cámara monocular móvil (localizada en una plataforma móvil). El objetivo último de estas detecciones es el de estimar la trayectoria recorrida por la cámara. También se presentan los cálculos matemáticos detrás de esta estimación. Se desarrolló un programa principal como parte del proyecto, capaz de rastrear puntos a lo largo de las secuencias, registrando sus trayectorias. Este programa realizado no estima la trayectoria de la cámara. También se mencionan varios métodos, usados por distintos autores, de interés para la odometría.This document introduces a way to detect and track features in image sequences (videos), recorded by a mobile monocular camera (placed on a mobile platform). The nal scope of those detections is to estimate the trajectory of the camera. The mathematical calculations behind cameras' trajectory estimation are also presented. One main program was created as part of this project, able to track some points throughout the sequences, registering their trajectories. This developed program does not estimate the camera's trajectory. Several methods, used by di erent authors, of special interest for the odometry are also mentioned.Grado en Sistemas de Telecomunicació