Visual motion : algorithms for analysis and application

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Architecture, 1990.Includes bibliographical references (leaves 71-73).by Michael Adam Sokolov.M.S

Neural network approach to the classification of urban images

Over the past few years considerable research effort has been devoted to the study of pattern recognition methods applied to the classification of remotely sensed images. Neural network methods have been widely explored, and been shown to be generally superior to conventional statistical methods. However, the classification of objects shown on greylevel high resolution images in urban areas presents significant difficulties. This thesis presents the results of work aimed at reducing some of these difficulties. High resolution greylevel aerial images are used as the raw material, and methods of processing using neural networks are presented. If a per-pixel approach were used there would be only one input neuron, the pixel greylevel, which would not provide a sufficient basis for successful object identification. The use of spatial neighbourhoods providing an m x m input vector centred on each pixel is investigated; this method takes into account the texture of the pixel's neighbourhood. The pixel's neighbourhood could be considered to contain more that textural information. Second order methods using mean greylevel, Laplacian and variance values derived from the pixel neighbourhood are developed to provide the neural network with a three neuron input vector. This method provides the neural network with additional information, improving the strength of the relationship between the input and output neurons, and therefore reducing the training time and improving the classification accuracy. A third method using a hierarchical set of two or more neural networks is proposed as a method of identifying the high level objects in the images. The methods were applied to representative data sets and the results were compared with manually classified images to quantify the results. Classification accuracy varied from 69% with a window of raw pixel values and 84% with a three neuron input vector of second order values

Neural network approach to the classification of urban images

Over the past few years considerable research effort has been devoted to the study of pattern recognition methods applied to the classification of remotely sensed images. Neural network methods have been widely explored, and been shown to be generally superior to conventional statistical methods. However, the classification of objects shown on greylevel high resolution images in urban areas presents significant difficulties. This thesis presents the results of work aimed at reducing some of these difficulties. High resolution greylevel aerial images are used as the raw material, and methods of processing using neural networks are presented. If a per-pixel approach were used there would be only one input neuron, the pixel greylevel, which would not provide a sufficient basis for successful object identification. The use of spatial neighbourhoods providing an m x m input vector centred on each pixel is investigated; this method takes into account the texture of the pixel's neighbourhood. The pixel's neighbourhood could be considered to contain more that textural information. Second order methods using mean greylevel, Laplacian and variance values derived from the pixel neighbourhood are developed to provide the neural network with a three neuron input vector. This method provides the neural network with additional information, improving the strength of the relationship between the input and output neurons, and therefore reducing the training time and improving the classification accuracy. A third method using a hierarchical set of two or more neural networks is proposed as a method of identifying the high level objects in the images. The methods were applied to representative data sets and the results were compared with manually classified images to quantify the results. Classification accuracy varied from 69% with a window of raw pixel values and 84% with a three neuron input vector of second order values

Fusion de données multi-capteurs pour la construction incrémentale du modèle tridimensionnel texturé d'un environnement intérieur par un robot mobile

Ce travail traite la Modélisation 3D d'un environnement intérieur par un robot mobile. La principale contribution concerne la construction d'un modèle géométrique hétérogène combinant des amers plans texturés, des lignes 3D et des points d'intérêt. Pour cela, nous devons fusionner des données géométriques et photométriques. Ainsi, nous avons d'abord amélioré la stéréovision, en proposant une approche de la mise en correspondance stéréoscopique par coupure de graphe. Notre contribution réside dans la construction d'un graphe réduit qui a permis d'accélérer la méthode globale et d'obtenir de meilleurs résultats que les méthodes locales. Aussi, pour percevoir l'environnement, le robot est équipé d'un télémètre laser 3D et d'une caméra. Nous proposons une chaîne algorithmique permettant de construire une carte hétérogène, par l'algorithme de Cartographie et Localisation Simultanées (EKF-SLAM). Le placage de la texture sur les facettes planes a permis de solidifier l'association de données.This thesis examines the problem of 3D Modelling of indoor environment by a mobile robot. Our main contribution consists in constructing a heterogeneous geometrical model containing textured planar landmarks, 3D lines and interest points. For that, we must fuse geometrical and photometrical data. Hence, we began by improving the stereo vision algorithm, and proposed a new approach of stereo matching by graph cuts. The most significant contribution is the construction of a reduced graph that allows to accelerate the global method and to provide better results than the local methods. Also, to perceive the environment, the robot is equipped by a 3D laser scanner and by a camera. We proposed an algorithmic chain allowing to incrementally constructing a heterogeneous map, using the algorithm of Simultaneous Localization and Mapping based (EKF-SLAM). Mapping the texture on the planar landmarks makes more robust the phase of data association

Computing shape using a theory of human stereo vision

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Mathematics, 1980.MICROFICHE COPY AVAILABLE IN ARCHIVES AND SCIENCE.Bibliography: leaves 225-237.by William Eric Leifur Grimson.Ph.D