An estimation-based approach to the reconstruction of optical flow

Cover title.Includes bibliographical references.Supported in part by the National Science Foundation. ECS-83-12921 Supported in part by the Army Research Office. DAAG-29-84-K-0005Anne Rougée, Bernard C. Levy, Alan S. Willsky

Visual recognition of objects : behavioral, computational, and neurobiological aspects

I surveyed work on visual object recognition and perception. In animals, vision has been studied mainly on the behavioral and neurobiological levels. Behavioral data typically show what the visual system, by itself or together with the rest of the organism, is capable of. They show, for example, that humans can recognie objects regardless of size and position, but that rotated objects pose problems. Important insights into the organization of behavior have also been provided by people who suffered localized brain damage. We have learned that the brain is divided into areas subserving different and relatively well-defined behaviors. The visual system itself is also organized in different subsystems; the visual cortex alone contains nearly twenty maps of the visual field. And individual neurons respond selectively to visual stimuli, e.g., the orientation of line segments, color, direction of motion, and, most intriguingly, faces. The question is how the actions of all these neurons produce the behavior we observe. How do neurons represent the shape of objects such that they can be recognized? Before we can answer the question, we have to understand the computational aspect of shape representation, the nature of the problem as it were. Many methods for representing shape have been explored, mainly by computer scientists, but so far no satisfactory answers have been found

Obtención de mapas de profundidad densos mediante visión activa por movimiento controlado de una cámara : Aplicación a tareas de reconocimiento

En este trabajo se presenta una aproximación novedosa al problema de obtención de información tridimensional de una escena a partir de distintas vistas de la misma. El montaje sobre el que se ha realizado la validación experimental de los algoritmos desarrollados es del tipo hand-eye por ser un tipo de montaje suficientemente contrastado en la literatura y con claras aplicaciones industriales. Entre ellas están las de prototipado rápido, agarre o construcción de entornos virtuales para teleoperación. No obstante, los algoritmos obtenidos son fácilmente realizables sobre otro tipo de sistemas tales como robots móviles. En este caso las aplicaciones en navegación visual y levantamiento de mapas de entornos desconocidos son claramente identificables. La consecución del objetivo final ha supuesto la investigación y la obtención de resultados reseñables en distintas áreas. Ha sido necesario el desarrollo de un proceso de calibración del sistema en el que se hacen aportaciones a la calibración de cámaras con fuerte distorsión radial. La estructura tridimensional de los objetos de la escena se ha obtenido de manera incremental. La investigación en este campo ha dado como fruto la evolución de un método clásico (MCE) de obtención de distancias por estereoscopía y el desarrollo de otro novedoso (RRD). La determinación de la posición de la siguiente reconstrucción incremental ha hecho necesario el estudio de las diferentes estrategias presentes en la literatura para la solución del problema de determinación de la siguiente mejor vista (NBV). Como resultado se presenta el desarrollo de un nuevo algoritmo NBV especialmente adaptado al empleo de cámaras como elemento de toma de datos. El trabajo concluye presentando los resultados obtenidos al utilizar los resultados obtenidos en un sistema de reconocimiento tridimensional de objetos. Este tipo de aplicación se ha elegido por su gran exigencia en la calidad de los datos de entrada y la clara aplicación práctica de un sistema de este tipo

Shape classification: towards a mathematical description of the face

Recent advances in biostereometric techniques have led to the quick and easy acquisition of 3D data for facial and other biological surfaces. This has led facial surgeons to express dissatisfaction with landmark-based methods for analysing the shape of the face which use only a small part of the data available, and to seek a method for analysing the face which maximizes the use of this extensive data set. Scientists working in the field of computer vision have developed a variety of methods for the analysis and description of 2D and 3D shape. These methods are reviewed and an approach, based on differential geometry, is selected for the description of facial shape. For each data point, the Gaussian and mean curvatures of the surface are calculated. The performance of three algorithms for computing these curvatures are evaluated for mathematically generated standard 3D objects and for 3D data obtained from an optical surface scanner. Using the signs of these curvatures, the face is classified into eight 'fundamental surface types' - each of which has an intuitive perceptual meaning. The robustness of the resulting surface type description to errors in the data is determined together with its repeatability. Three methods for comparing two surface type descriptions are presented and illustrated for average male and average female faces. Thus a quantitative description of facial change, or differences between individual's faces, is achieved. The possible application of artificial intelligence techniques to automate this comparison is discussed. The sensitivity of the description to global and local changes to the data, made by mathematical functions, is investigated. Examples are given of the application of this method for describing facial changes made by facial reconstructive surgery and implications for defining a basis for facial aesthetics using shape are discussed. It is also applied to investigate the role played by the shape of the surface in facial recognition