Using Fuzzy Logic to Enhance Stereo Matching in Multiresolution Images

Stereo matching is an open problem in Computer Vision, for which local features are extracted to identify corresponding points in pairs of images. The results are heavily dependent on the initial steps. We apply image decomposition in multiresolution levels, for reducing the search space, computational time, and errors. We propose a solution to the problem of how deep (coarse) should the stereo measures start, trading between error minimization and time consumption, by starting stereo calculation at varying resolution levels, for each pixel, according to fuzzy decisions. Our heuristic enhances the overall execution time since it only employs deeper resolution levels when strictly necessary. It also reduces errors because it measures similarity between windows with enough details. We also compare our algorithm with a very fast multi-resolution approach, and one based on fuzzy logic. Our algorithm performs faster and/or better than all those approaches, becoming, thus, a good candidate for robotic vision applications. We also discuss the system architecture that efficiently implements our solution

Feature based three-dimensional object recognition using disparity maps

The human vision system is able to recognize objects it has seen before even if the particular orientation of the object being viewed was not specifically seen before. This is due to the adaptability of the cognitive abilities of the human brain to categorize objects by different features. The features and experience used in the human recognition system are also applicable to a computer recognition system. The recognition of three-dimensional objects has been a popular area in computer vision research in recent years, as computer and machine vision is becoming more abundant in areas such as surveillance and product inspection. The purpose of this study is to explore and develop an adaptive computer vision based recognition system which can recognize 3D information of an object from a limited amount of training data in the form of disparity maps. Using this system, it should be possible to recognize an object in many different orientations, even if the specific orientation had not been seen before, as well as distinguish between different objects

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Methodology for high resolution spatial analysis of the physical flood susceptibility of buildings in large river floodplains

The impacts of floods on buildings in urban areas are increasing due to the intensification of extreme weather events, unplanned or uncontrolled settlements and the rising vulnerability of assets. There are some approaches available for assessing the flood damage to buildings and critical infrastructure. To this point, however, it is extremely difficult to adapt these methods widely, due to the lack of high resolution classification and characterisation approaches for built structures. To overcome this obstacle, this work presents: first, a conceptual framework for understanding the physical flood vulnerability and the physical flood susceptibility of buildings, second, a methodological framework for the combination of methods and tools for a large-scale and high-resolution analysis and third, the testing of the methodology in three pilot sites with different development conditions. The conceptual framework narrows down an understanding of flood vulnerability, physical flood vulnerability and physical flood susceptibility and its relation to social and economic vulnerabilities. It describes the key features causing the physical flood susceptibility of buildings as a component of the vulnerability. The methodological framework comprises three modules: (i) methods for setting up a building topology, (ii) methods for assessing the susceptibility of representative buildings of each building type and (iii) the integration of the two modules with technological tools. The first module on the building typology is based on a classification of remote sensing data and GIS analysis involving seven building parameters, which appeared to be relevant for a classification of buildings regarding potential flood impacts. The outcome is a building taxonomic approach. A subsequent identification of representative buildings is based on statistical analyses and membership functions. The second module on the building susceptibility for representative buildings bears on the derivation of depth-physical impact functions. It relates the principal building components, including their heights, dimensions and materials, to the damage from different water levels. The material’s susceptibility is estimated based on international studies on the resistance of building materials and a fuzzy expert analysis. Then depth-physical impact functions are calculated referring to the principal components of the buildings which can be affected by different water levels. Hereby, depth-physical impact functions are seen as a means for the interrelation between the water level and the physical impacts. The third module provides the tools for implementing the methodology. This tool compresses the architecture for feeding the required data on the buildings with their relations to the building typology and the building-type specific depth-physical impact function supporting the automatic process. The methodology is tested in three flood plains pilot sites: (i) in the settlement of the Barrio Sur in Magangué and (ii) in the settlement of La Peña in Cicuco located on the flood plain of Magdalena River, Colombia and (iii) in a settlement of the city of Dresden, located on the Elbe River, Germany. The testing of the methodology covers the description of data availability and accuracy, the steps for deriving the depth-physical impact functions of representative buildings and the final display of the spatial distribution of the physical flood susceptibility. The discussion analyses what are the contributions of this work evaluating the findings of the methodology’s testing with the dissertation goals. The conclusions of the work show the contributions and limitations of the research in terms of methodological and empirical advancements and the general applicability in flood risk management.:1 INTRODUCTION 1 1.1 Background 1 1.2 State of the art 2 1.3 Problem statement 6 1.4 Objectives 6 1.5 Approach and outline 6 2 CONCEPTUAL FRAMEWORK 9 2.1 Flood vulnerability 10 2.2 Physical flood vulnerability 12 2.3 Physical flood susceptibility 14 3 METHODOLOGICAL FRAMEWORK 23 3.1 Module 1: Building taxonomy for settlements 24 3.1.1 Extraction of building features 24 3.1.2 Derivation of building parameters for setting up a building taxonomy 38 3.1.3 Selection of representative buildings for a building susceptibility assessment 51 3.2 Module 2: Physical susceptibility of representative buildings 57 3.2.1 Identification of building components 57 3.2.2 Qualification of building material susceptibility 62 3.2.3 Derivation of a depth-physical impact function 71 3.3 Module 3: Technological integration 77 3.3.1 Combination of the depth-physical impact function with the building taxonomic code 77 3.3.2 Tools supporting the physical susceptibility analysis 78 3.3.3 The users and their requirements 79 4 RESULTS OF THE METHODOLOGY TESTING 83 4.1 Pilot site “Kleinzschachwitz” – Dresden, Germany – Elbe River 83 4.1.1 Module 1: Building taxonomy – “Kleinzschachwitz” 85 4.1.2 Module 2: Physical susceptibility of representative buildings – “Kleinzschachwitz” 97 4.1.3 Module 3: Technological integration – “Kleinzschachwitz” 103 4.2 Pilot site “La Peña” – Cicuco, Colombia – Magdalena River 107 4.2.1 Module 1: Building taxonomy – “La Peña” 108 4.2.2 Module 2: Physical susceptibility of representative buildings – “La Peña” 121 4.2.3 Module 3: Technological integration– “La Peña” 129 4.3 Pilot site “Barrio Sur” – Magangué, Colombia – Magdalena River 133 4.3.1 Module 1: Building taxonomy – “Barrio Sur” 133 4.3.2 Module 2: Physical susceptibility of representative buildings – “Barrio Sur” 141 4.3.3 Module 3: Technological integration – “Barrio Sur” 147 4.4 Empirical findings 151 4.4.1 Empirical findings of Module 1 151 4.4.2 Empirical findings of Module 2 155 4.4.3 Empirical findings of Module 3 157 4.4.4 Guidance of the methodology 157 5 DISCUSSION 161 5.1 Discussion on the conceptual framework 161 5.2 Discussion on the methodological framework 161 5.2.1 Discussion on Module 1: the building taxonomic approach 162 5.2.2 Discussion on Module 2: the depth-physical impact function 164 6 CONCLUSIONS AND OUTLOOK 167 6.1 Conclusions 167 6.2 Outlook 168 REFERENCES 171 INDEX OF FIGURES 199 INDEX OF TABLES 201 APPENDICES 203In vielen Städten nehmen die Auswirkungen von Hochwasser auf Gebäude aufgrund immer extremerer Wetterereignisse, unkontrollierbarer Siedlungsbauten und der steigenden Vulnerabilität von Besitztümern stetig zu. Es existieren zwar bereits Ansätze zur Beurteilung von Wasserschäden an Gebäuden und Infrastrukturknotenpunkten. Doch ist es bisher schwierig, diese Methoden großräumig anzuwenden, da es an einer präzisen Klassifizierung und Charakterisierung von Gebäuden und anderen baulichen Anlagen fehlt. Zu diesem Zweck sollen in dieser Arbeit erstens ein Konzept für ein genaueres Verständnis der physischen Vulnerabilität von Gebäuden gegenüber Hochwasser dargelegt, zweitens ein methodisches Verfahren zur Kombination der bestehenden Methoden und Hilfsmittel mit dem Ziel einer großräumigen und hochauflösenden Analyse erarbeitet und drittens diese Methode an drei Pilotstandorten mit unterschiedlichem Ausbauzustand erprobt werden. Die Rahmenbedingungen des Konzepts grenzen die Begriffe der Vulnerabilität, der physischen Vulnerabilität und der physischen Anfälligkeit gegenüber Hochwasser ein und erörtern deren Beziehung zur sozialen und ökonomischen Vulnerabilität. Es werden die Merkmale der physischen Anfälligkeit von Gebäuden gegenüber Hochwasser als Bestandteil der Vulnerabilität definiert. Das methodische Verfahren umfasst drei Module: (i) Methoden zur Erstellung einer Gebäudetypologie, (ii) Methoden zur Bewertung der Anfälligkeit repräsentativer Gebäude jedes Gebäudetyps und (iii) die Kombination der beiden Module mit Hilfe technologischer Hilfsmittel. Das erste Modul zur Gebäudetypologie basiert auf der Klassifizierung von Fernerkundungsdaten und GIS-Analysen anhand von sieben Gebäudeparametern, die sich für die Klassifizierung von Gebäuden bezüglich ihres Risikopotenzials bei Hochwasser als wichtig erweisen. Daraus ergibt sich ein Ansatz zur Gebäudeklassifizierung. Die anschließende Ermittlung repräsentativer Gebäude beruht auf statistischen Analysen und Zugehörigkeitsfunktionen. Das zweite Modul zur Anfälligkeit repräsentativer Gebäude beruht auf der Ableitung von Funktion von Wasserstand und physischer Einwirkung. Es setzt die relevanten Gebäudemerkmale, darunter Höhe, Maße und Materialien, in Beziehung zum erwartbaren Schaden bei unterschiedlichen Wasserständen. Die Materialanfälligkeit wird aufgrund internationaler Studien zur Festigkeit von Baustoffen sowie durch Anwendung eines Fuzzy-Logic-Expertensystems eingeschätzt. Anschließend werden Wasserstand-Schaden-Funktionen unter Einbeziehung der Hauptgebäudekomponenten berechnet, die durch unterschiedliche Wasserstände in Mitleidenschaft gezogen werden können. Funktion von Wasserstand und physischer Einwirkung dienen hier dazu, den jeweiligen Wasserstand und die physischen Auswirkung in Beziehung zueinander zu setzen. Das dritte Modul stellt die zur Umsetzung der Methoden notwendigen Hilfsmittel vor. Zur Unterstützung des automatisierten Verfahrens dienen Hilfsmittel, die die Gebäudetypologie mit der Funktion von Wasserstand und physischer Einwirkung für Gebäude in Hochwassergebieten kombinieren. Die Methoden wurden anschließend in drei hochwassergefährdeten Pilotstandorten getestet: (i) in den Siedlungsgebieten von Barrio Sur in Magangué und (ii) von La Pena in Cicuco, zwei Überschwemmungsgebiete des Magdalenas in Kolumbien, und (iii) im Stadtgebiet von Dresden, das an der Elbe liegt. Das Testverfahren umfasst die Beschreibung der Datenverfügbarkeit und genauigkeit, die einzelnen Schritte zur Analyse der. Funktion von Wasserstand und physischer Einwirkung repräsentativer Gebäude sowie die Darstellung der räumlichen Verteilung der physischen Anfälligkeit für Hochwasser. In der Diskussion wird der Beitrag dieser Arbeit zur Beurteilung der Erkenntnisse der getesteten Methoden anhand der Ziele dieser Dissertation analysiert. Die Folgerungen beleuchten abschließend die Fortschritte und auch Grenzen der Forschung hinsichtlich methodischer und empirischer Entwicklungen sowie deren allgemeine Anwendbarkeit im Bereich des Hochwasserschutzes.:1 INTRODUCTION 1 1.1 Background 1 1.2 State of the art 2 1.3 Problem statement 6 1.4 Objectives 6 1.5 Approach and outline 6 2 CONCEPTUAL FRAMEWORK 9 2.1 Flood vulnerability 10 2.2 Physical flood vulnerability 12 2.3 Physical flood susceptibility 14 3 METHODOLOGICAL FRAMEWORK 23 3.1 Module 1: Building taxonomy for settlements 24 3.1.1 Extraction of building features 24 3.1.2 Derivation of building parameters for setting up a building taxonomy 38 3.1.3 Selection of representative buildings for a building susceptibility assessment 51 3.2 Module 2: Physical susceptibility of representative buildings 57 3.2.1 Identification of building components 57 3.2.2 Qualification of building material susceptibility 62 3.2.3 Derivation of a depth-physical impact function 71 3.3 Module 3: Technological integration 77 3.3.1 Combination of the depth-physical impact function with the building taxonomic code 77 3.3.2 Tools supporting the physical susceptibility analysis 78 3.3.3 The users and their requirements 79 4 RESULTS OF THE METHODOLOGY TESTING 83 4.1 Pilot site “Kleinzschachwitz” – Dresden, Germany – Elbe River 83 4.1.1 Module 1: Building taxonomy – “Kleinzschachwitz” 85 4.1.2 Module 2: Physical susceptibility of representative buildings – “Kleinzschachwitz” 97 4.1.3 Module 3: Technological integration – “Kleinzschachwitz” 103 4.2 Pilot site “La Peña” – Cicuco, Colombia – Magdalena River 107 4.2.1 Module 1: Building taxonomy – “La Peña” 108 4.2.2 Module 2: Physical susceptibility of representative buildings – “La Peña” 121 4.2.3 Module 3: Technological integration– “La Peña” 129 4.3 Pilot site “Barrio Sur” – Magangué, Colombia – Magdalena River 133 4.3.1 Module 1: Building taxonomy – “Barrio Sur” 133 4.3.2 Module 2: Physical susceptibility of representative buildings – “Barrio Sur” 141 4.3.3 Module 3: Technological integration – “Barrio Sur” 147 4.4 Empirical findings 151 4.4.1 Empirical findings of Module 1 151 4.4.2 Empirical findings of Module 2 155 4.4.3 Empirical findings of Module 3 157 4.4.4 Guidance of the methodology 157 5 DISCUSSION 161 5.1 Discussion on the conceptual framework 161 5.2 Discussion on the methodological framework 161 5.2.1 Discussion on Module 1: the building taxonomic approach 162 5.2.2 Discussion on Module 2: the depth-physical impact function 164 6 CONCLUSIONS AND OUTLOOK 167 6.1 Conclusions 167 6.2 Outlook 168 REFERENCES 171 INDEX OF FIGURES 199 INDEX OF TABLES 201 APPENDICES 203El impacto de las inundaciones sobre los edificios en zonas urbanas es cada vez mayor debido a la intensificación de los fenómenos meteorológicos extremos, asentamientos no controlados o no planificados y su creciente vulnerabilidad. Hay métodos disponibles para evaluar los daños por inundación en edificios e infraestructuras críticas. Sin embargo, es muy difícil implementar estos métodos sistemáticamente en grandes áreas debido a la falta de clasificación y caracterización de estructuras construidas en resoluciones detalladas. Para superar este obstáculo, este trabajo se enfoca, en primer lugar, en desarrollar un marco conceptual para comprender la vulnerabilidad y susceptibilidad física de edificios por inudaciones, en segundo lugar, en desarrollar un marco metodológico para la combinación de los métodos y herramientas para una análisis de alta resolución y en tercer lugar, la prueba de la metodología en tres sitios experimentales, con distintas condiciones de desarrollo. El marco conceptual se enfoca en comprender la vulnerabilidad y susceptibility de las edificaciones frente a inundaciones, y su relación con la vulnerabilidad social y económica. En él se describen las principales características físicas de la susceptibilidad de edificicaiones como un componente de la vulnerabilidad. El marco metodológico consta de tres módulos: (i) métodos para la derivación de topología de construcciones, (ii) métodos para evaluar la susceptibilidad de edificios representativos y (iii) la integración de los dos módulos a través herramientas tecnológicas. El primer módulo de topología de construcciones se basa en una clasificación de datos de sensoramiento rémoto y procesamiento SIG para la extracción de siete parámetros de las edficaciones. Este módulo parece ser aplicable para una clasificación de los edificios en relación con los posibles impactos de las inundaciones. El resultado es una taxonomía de las edificaciones y una posterior identificación de edificios representativos que se basa en análisis estadísticos y funciones de pertenencia. El segundo módulo consiste en el análisis de susceptibilidad de las construcciones representativas a través de funciones de profundidad del impacto físico. Las cuales relacionan los principales componentes de la construcción, incluyendo sus alturas, dimensiones y materiales con los impactos físicos a diferentes niveles de agua. La susceptibilidad del material se calcula con base a estudios internacionales sobre la resistencia de los materiales y un análisis a través de sistemas expertos difusos. Aquí, las funciones de profundidad de impacto físico son considerados como un medio para la interrelación entre el nivel del agua y los impactos físicos. El tercer módulo proporciona las herramientas necesarias para la aplicación de la metodología. Estas herramientas tecnológicas consisten en la arquitectura para la alimentación de los datos relacionados a la tipología de construcciones con las funciones de profundidad del impacto físico apoyado en procesos automáticos. La metodología es probada en tres sitios piloto: (i) en el Barrio Sur en Magangué y (ii) en la barrio de La Peña en Cicuco situado en la llanura inundable del Río Magdalena, Colombia y (iii) en barrio Kleinzschachwitz de la ciudad de Dresden, situado a orillas del río Elba, en Alemania. Las pruebas de la metodología abarca la descripción de la disponibilidad de los datos y la precisión, los pasos a seguir para obtener las funciones profundidad de impacto físico de edificios representativos y la presentación final de la distribución espacial de la susceptibilidad física frente inundaciones El discusión analiza las aportaciones de este trabajo y evalua los resultados de la metodología con relación a los objetivos. Las conclusiones del trabajo, muestran los aportes y limitaciones de la investigación en términos de avances metodológicos y empíricos y la aplicabilidad general de gestión del riesgo de inundaciones.:1 INTRODUCTION 1 1.1 Background 1 1.2 State of the art 2 1.3 Problem statement 6 1.4 Objectives 6 1.5 Approach and outline 6 2 CONCEPTUAL FRAMEWORK 9 2.1 Flood vulnerability 10 2.2 Physical flood vulnerability 12 2.3 Physical flood susceptibility 14 3 METHODOLOGICAL FRAMEWORK 23 3.1 Module 1: Building taxonomy for settlements 24 3.1.1 Extraction of building features 24 3.1.2 Derivation of building parameters for setting up a building taxonomy 38 3.1.3 Selection of representative buildings for a building susceptibility assessment 51 3.2 Module 2: Physical susceptibility of representative buildings 57 3.2.1 Identification of building components 57 3.2.2 Qualification of building material susceptibility 62 3.2.3 Derivation of a depth-physical impact function 71 3.3 Module 3: Technological integration 77 3.3.1 Combination of the depth-physical impact function with the building taxonomic code 77 3.3.2 Tools supporting the physical susceptibility analysis 78 3.3.3 The users and their requirements 79 4 RESULTS OF THE METHODOLOGY TESTING 83 4.1 Pilot site “Kleinzschachwitz” – Dresden, Germany – Elbe River 83 4.1.1 Module 1: Building taxonomy – “Kleinzschachwitz” 85 4.1.2 Module 2: Physical susceptibility of representative buildings – “Kleinzschachwitz” 97 4.1.3 Module 3: Technological integration – “Kleinzschachwitz” 103 4.2 Pilot site “La Peña” – Cicuco, Colombia – Magdalena River 107 4.2.1 Module 1: Building taxonomy – “La Peña” 108 4.2.2 Module 2: Physical susceptibility of representative buildings – “La Peña” 121 4.2.3 Module 3: Technological integration– “La Peña” 129 4.3 Pilot site “Barrio Sur” – Magangué, Colombia – Magdalena River 133 4.3.1 Module 1: Building taxonomy – “Barrio Sur” 133 4.3.2 Module 2: Physical susceptibility of representative buildings – “Barrio Sur” 141 4.3.3 Module 3: Technological integration – “Barrio Sur” 147 4.4 Empirical findings 151 4.4.1 Empirical findings of Module 1 151 4.4.2 Empirical findings of Module 2 155 4.4.3 Empirical findings of Module 3 157 4.4.4 Guidance of the methodology 157 5 DISCUSSION 161 5.1 Discussion on the conceptual framework 161 5.2 Discussion on the methodological framework 161 5.2.1 Discussion on Module 1: the building taxonomic approach 162 5.2.2 Discussion on Module 2: the depth-physical impact function 164 6 CONCLUSIONS AND OUTLOOK 167 6.1 Conclusions 167 6.2 Outlook 168 REFERENCES 171 INDEX OF FIGURES 199 INDEX OF TABLES 201 APPENDICES 20

Analog VLSI implementation for stereo correspondence between 2-D images

Many robotics and navigation systems utilizing stereopsis to determine depth have rigid size and power constraints and require direct physical implementation of the stereo algorithm. The main challenges lie in managing the communication between image sensor and image processor arrays, and in parallelizing the computation to determine stereo correspondence between image pixels in real-time. This paper describes the first comprehensive system level demonstration of a dedicated low-power analog VLSI (very large scale integration) architecture for stereo correspondence suitable for real-time implementation. The inputs to the implemented chip are the ordered pixels from a stereo image pair, and the output is a two-dimensional disparity map. The approach combines biologically inspired silicon modeling with the necessary interfacing options for a complete practical solution that can be built with currently available technology in a compact package. Furthermore, the strategy employed considers multiple factors that may degrade performance, including the spatial correlations in images and the inherent accuracy limitations of analog hardware, and augments the design with countermeasures

Visual Human Tracking and Group Activity Analysis: A Video Mining System for Retail Marketing

Thesis (PhD) - Indiana University, Computer Sciences, 2007In this thesis we present a system for automatic human tracking and activity recognition from video sequences. The problem of automated analysis of visual information in order to derive descriptors of high level human activities has intrigued computer vision community for decades and is considered to be largely unsolved. A part of this interest is derived from the vast range of applications in which such a solution may be useful. We attempt to find efficient formulations of these tasks as applied to the extracting customer behavior information in a retail marketing context. Based on these formulations, we present a system that visually tracks customers in a retail store and performs a number of activity analysis tasks based on the output from the tracker. In tracking we introduce new techniques for pedestrian detection, initialization of the body model and a formulation of the temporal tracking as a global trans-dimensional optimization problem. Initial human detection is addressed by a novel method for head detection, which incorporates the knowledge of the camera projection model.The initialization of the human body model is addressed by newly developed shape and appearance descriptors. Temporal tracking of customer trajectories is performed by employing a human body tracking system designed as a Bayesian jump-diffusion filter. This approach demonstrates the ability to overcome model dimensionality ambiguities as people are leaving and entering the scene. Following the tracking, we developed a two-stage group activity formulation based upon the ideas from swarming research. For modeling purposes, all moving actors in the scene are viewed here as simplistic agents in the swarm. This allows to effectively define a set of inter-agent interactions, which combine to derive a distance metric used in further swarm clustering. This way, in the first stage the shoppers that belong to the same group are identified by deterministically clustering bodies to detect short term events and in the second stage events are post-processed to form clusters of group activities with fuzzy memberships. Quantitative analysis of the tracking subsystem shows an improvement over the state of the art methods, if used under similar conditions. Finally, based on the output from the tracker, the activity recognition procedure achieves over 80% correct shopper group detection, as validated by the human generated ground truth results

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Advances in Stereo Vision

Stereopsis is a vision process whose geometrical foundation has been known for a long time, ever since the experiments by Wheatstone, in the 19th century. Nevertheless, its inner workings in biological organisms, as well as its emulation by computer systems, have proven elusive, and stereo vision remains a very active and challenging area of research nowadays. In this volume we have attempted to present a limited but relevant sample of the work being carried out in stereo vision, covering significant aspects both from the applied and from the theoretical standpoints

Fast and robust image feature matching methods for computer vision applications

Service robotic systems are designed to solve tasks such as recognizing and manipulating objects, understanding natural scenes, navigating in dynamic and populated environments. It's immediately evident that such tasks cannot be modeled in all necessary details as easy as it is with industrial robot tasks; therefore, service robotic system has to have the ability to sense and interact with the surrounding physical environment through a multitude of sensors and actuators. Environment sensing is one of the core problems that limit the deployment of mobile service robots since existing sensing systems are either too slow or too expensive. Visual sensing is the most promising way to provide a cost effective solution to the mobile robot sensing problem. It's usually achieved using one or several digital cameras placed on the robot or distributed in its environment. Digital cameras are information rich sensors and are relatively inexpensive and can be used to solve a number of key problems for robotics and other autonomous intelligent systems, such as visual servoing, robot navigation, object recognition, pose estimation, and much more. The key challenges to taking advantage of this powerful and inexpensive sensor is to come up with algorithms that can reliably and quickly extract and match the useful visual information necessary to automatically interpret the environment in real-time. Although considerable research has been conducted in recent years on the development of algorithms for computer and robot vision problems, there are still open research challenges in the context of the reliability, accuracy and processing time. Scale Invariant Feature Transform (SIFT) is one of the most widely used methods that has recently attracted much attention in the computer vision community due to the fact that SIFT features are highly distinctive, and invariant to scale, rotation and illumination changes. In addition, SIFT features are relatively easy to extract and to match against a large database of local features. Generally, there are two main drawbacks of SIFT algorithm, the first drawback is that the computational complexity of the algorithm increases rapidly with the number of key-points, especially at the matching step due to the high dimensionality of the SIFT feature descriptor. The other one is that the SIFT features are not robust to large viewpoint changes. These drawbacks limit the reasonable use of SIFT algorithm for robot vision applications since they require often real-time performance and dealing with large viewpoint changes. This dissertation proposes three new approaches to address the constraints faced when using SIFT features for robot vision applications, Speeded up SIFT feature matching, robust SIFT feature matching and the inclusion of the closed loop control structure into object recognition and pose estimation systems. The proposed methods are implemented and tested on the FRIEND II/III service robotic system. The achieved results are valuable to adapt SIFT algorithm to the robot vision applications