Algorithme de contour actif appliqué à la poursuite d'avalanche

Le traitement d'image est un outil de plus en plus utilisé dans l'étude des avalanches de neige, ceci dans le but de les prévenir. On présente ici un algorithme de contour actif spécifique à notre application : l'analyse du front des avalanches. Le but de cette étude consiste en l'extraction de paramètres dynamiques de l'avalanche à partir d'un simple film vidéo. La principale difficulté réside dans le manque évident de contraste de nos images, puisque nous avons à poursuivre un objet blanc sur un fond blanc. L'algorithme présenté repose sur le principe des contours actifs, où on recherche à minimiser une forme énergétique modélisant le contour de l'avalanche. Le résultat obtenu est une séquence de contour d'avalanche

c ○ 2001 Kluwer Academic Publishers. Manufactured in The Netherlands. Bayesian Object Localisation in Images

Abstract. A Bayesian approach to intensity-based object localisation is presented that employs a learned probabilistic model of image filter-bank output, applied via Monte Carlo methods, to escape the inefficiency of exhaustive search. An adequate probabilistic account of image data requires intensities both in the foreground (i.e. over the object), and in the background, to be modelled. Some previous approaches to object localisation by Monte Carlo methods have used models which, we claim, do not fully address the issue of the statistical independence of image intensities. It is addressed here by applying to each image a bank of filters whose outputs are approximately statistically independent. Distributions of the responses of individual filters, over foreground and background, are learned from training data. These distributions are then used to define a joint distribution for the output of the filter bank, conditioned on object configuration, and this serves as an observation likelihood for use in probabilistic inference about localisation. The effectiveness of probabilistic object localisation in image clutter, using Bayesian Localisation, is illustrated. Because it is a Monte Carlo method, it produces not simply a single estimate of object configuration, but an entire sample from the posterior distribution for the configuration. This makes sequential inference of configuration possible. Two examples are illustrated here: coarse to fine scale inference, and propagation of configuration estimates over time, in image sequences. Keywords: vision, object location, Monte Carlo, filter-bank, statistical independenc

Estimation of a Coronary Vessel Wall Deformation with High-Frequency Ultrasound Elastography

Elastography, which is based on applying pressure and estimating the resulting deformation, involves the forward problem to obtain the strain distributions and inverse problem to construct the elastic distributions consistent with the obtained strains on observation points. This thesis focuses on the former problem whose solution is used as an input to the latter problem. The aim is to provide the inverse problem community with accurate strain estimates of a coronary artery vessel wall. In doing so, a new ultrasonic image-based elastography approach is developed. Because the accuracy and quality of the estimated strain fields depend on the resolution level of the ultrasound image and to date best resolution levels obtained in the literature are not enough to clearly see all boundaries of the artery, one of the main goals is to acquire high-resolution coronary vessel wall ultrasound images at different pressures. For this purpose, first an experimental setup is designed to collect radio frequency (RF) signals, and then image formation algorithm is developed to obtain ultrasound images from the collected signals. To segment the noisy ultrasound images formed, a geodesic active contour-based segmentation algorithm with a novel stopping function that includes local phase of the image is developed. Then, region-based information is added to make the segmentation more robust to noise. Finally, elliptical deformable template is applied so that a priori information regarding the shape of the arteries could be taken into account, resulting in more stable and accurate results. The use of this template also implicitly provides boundary point correspondences from which high-resolution, size-independent, non-rigid and local strain fields of the coronary vessel wall are obtained.Ph.D.Committee Chair: Benkeser, Paul; Committee Member: Koblasz, Arthur; Committee Member: Skrinjar, Oskar; Committee Member: Vidakovic, Brani; Committee Member: Yezzi, Anthon

Pedestrian head detection using automatic scale selection for feature detection and statistical edge curvature analysis

In this report we focus on pedestrian head detection and tracking in video sequences. The task is not trivial in real and complex scenarios where the deformation induced by the perspective field requires a multi-scale analy- sis. Multi-scale shape models for the human head are considered to identify the correct size of the region of interest. Anisotropic diffusion is used as a pre-processing step and edge detection is performed using an automatic scale selection process. A non parametric statistical description is given for the edge curvature and detection is performed by means of goodness-of-fit tests. The head detector is used as a validation tool in a correlation-based tracker. The local maxima of the correlation matrix are analyzed. Tracking is performed associating the displacement vector of the target with that local maximum which maximizes the goodness-of-fit with the distribution of the edge curvature of the head

Generación de mosaicos georreferenciados a partir de imágenes capturadas con vehículo aereos no tripulados

Este documento presenta el resultado del trabajo de grado, en el cual se resarrolló un sistema de generación y georreferenciación de mosaicos de fotografias aéreas, mediante el planteamiento de un método con bases matemáticas que permitió unificar y calcular las coordenadas a partir de los datos de entrada obtenidos por un hevículo aéreo no tripulado. Por medio de este desarrollo es posible obtener un insumo de mayor calidad para diferentes sistemas de información geográficos que existen en la actualidad.Ingeniero (a) de SistemasPregrad

Automated vision-based generation of event statistics for decision support

Many tasks require surveillance and analysis in order to make decisions regarding the next course of action. The people responsible for these tasks are usually concerned with any event that affects their bottom-line. Traditionally, human operators have had to either actively man a set of video displays to determine if specific events were occurring or manually review hours of collected video data to see if a specific event occurred. Actively monitoring video stream or manually reviewing and analyzing the data collected, however, is a tedious and long process which is prone to errors due to biases and inattention. Automatically processing and analyzing the video provides an alternate way of getting more accurate results because it can reduce the likelihood of missing important events and the human factors that lead to decreased efficiency. The thesis aims to contribute to the area of using computer vision as a decision support tool by integrating detector, tracker, re-identification, activity status estimation, and event processor modules to generate the necessary event statistics needed by a human operator. The contribution of this thesis is a system that uses feedback from each of the modules to provide better target detection, and tracking results for event statistics generation over an extended period of time. To demonstrate the efficacy of the proposed system, it is first used to generate event statistics that measure productivity on multiple construction work sites. The versatility of the proposed system is also demonstrated in an indoor assisted living environment by using it to determine how much of an influence a technology intervention had on promoting interactions amongst older adults in a shared space.Ph.D