An Improved Multi-Level Edge-Based Stereo Correspondence Technique for Snake Based Object Segmentation

Disparity maps generated by stereo correspondence are very useful for stereo object segmentation because based on disparity background clutter can be effectively removed from the image. This enables conventional methods such as snake-based to efficiently detect the object of interest contour. In this research I propose two main enhancements on Alattar’s method first I increased the number of edge levels, and utilized the color information in the matching process. Besides a few minor modifications, these enhancements achieve a more accurate disparity map which eventually helps achieve higher segmentation accuracy by the snake. Experiments were performed in various indoor and outdoor image conditions to evaluate the matching performance of the proposed method compared to the previous work

Pedestrian Validation in Infrared Images by Means of Active Contours and Neural Networks

This paper presents two different modules for the validation of human shape presence in far-infrared images. These modules are part of a more complex system aimed at the detection of pedestrians by means of the simultaneous use of two stereo vision systems in both far-infrared and daylight domains. The first module detects the presence of a human shape in a list of areas of attention using active contours to detect the object shape and evaluating the results by means of a neural network. The second validation subsystem directly exploits a neural network for each area of attention in the far-infrared images and produces a list of votes

Active Contour Based Segmentation Techniques for Medical Image Analysis

Image processing is a technique which is used to derive information from the images. Segmentation is a section of image processing for the separation or segregation of information from the required target region of the image. There are different techniques used for segmentation of pixels of interest from the image. Active contour is one of the active models in segmentation techniques, which makes use of the energy constraints and forces in the image for separation of region of interest. Active contour defines a separate boundary or curvature for the regions of target object for segmentation. The contour depends on various constraints based on which they are classified into different types such as gradient vector flow, balloon and geometric models. Active contour models are used in various image processing applications specifically in medical image processing. In medical imaging, active contours are used in segmentation of regions from different medical images such as brain CT images, MRI images of different organs, cardiac images and different images of regions in the human body. Active contours can also be used in motion tracking and stereo tracking. Thus, the active contour segmentation is used for the separation of pixels of interest for different image processing

A survey of visual preprocessing and shape representation techniques

Many recent theories and methods proposed for visual preprocessing and shape representation are summarized. The survey brings together research from the fields of biology, psychology, computer science, electrical engineering, and most recently, neural networks. It was motivated by the need to preprocess images for a sparse distributed memory (SDM), but the techniques presented may also prove useful for applying other associative memories to visual pattern recognition. The material of this survey is divided into three sections: an overview of biological visual processing; methods of preprocessing (extracting parts of shape, texture, motion, and depth); and shape representation and recognition (form invariance, primitives and structural descriptions, and theories of attention)

Fourier-based geometric shape prior for snakes

International audienceA novel method of snakes with shape prior is presented in this paper. We propose to add a new force which makes the curve evolve to particular shape corresponding to a template to overcome some well-known problems of snakes. The template is an instance or a sketch of the researched contour without knowing its exact geometric pose in the image. The prior information is introduced through a set of complete and locally stable invariants to Euclidean transformations (translation, rotation and scale factor) computed using Fourier Transform on contours. The method is evaluated with the segmentation of myocardial scintigraphy slices and the tracking of an object in a video sequence

A learning algorithm for visual pose estimation of continuum robots

Continuum robots offer significant advantages for surgical intervention due to their down-scalability, dexterity, and structural flexibility. While structural compliance offers a passive way to guard against trauma, it necessitates robust methods for online estimation of the robot configuration in order to enable precise position and manipulation control. In this paper, we address the pose estimation problem by applying a novel mapping of the robot configuration to a feature descriptor space using stereo vision. We generate a mapping of known features through a supervised learning algorithm that relates the feature descriptor to known ground truth. Features are represented in a reduced sub-space, which we call eigen-features. The descriptor provides some robustness to occlusions, which are inherent to surgical environments, and the methodology that we describe can be applied to multi-segment continuum robots for closed-loop control. Experimental validation on a single-segment continuum robot demonstrates the robustness and efficacy of the algorithm for configuration estimation. Results show that the errors are in the range of 1°

Multiple cue integration for robust tracking in dynamic environments: application to video relighting

L'anàlisi de moviment i seguiment d'objectes ha estat un dels pricipals focus d'atenció en la comunitat de visió per computador durant les dues darreres dècades. L'interès per aquesta àrea de recerca resideix en el seu ample ventall d'aplicabilitat, que s'extén des de tasques de navegació de vehicles autònoms i robots, fins a aplications en la indústria de l'entreteniment i realitat virtual.Tot i que s'han aconseguit resultats espectaculars en problemes específics, el seguiment d'objectes continua essent un problema obert, ja que els mètodes disponibles són propensos a ser sensibles a diversos factors i condicions no estacionàries de l'entorn, com ara moviments impredictibles de l'objecte a seguir, canvis suaus o abruptes de la il·luminació, proximitat d'objectes similars o fons confusos. Enfront aquests factors de confusió la integració de múltiples característiques ha demostrat que permet millorar la robustesa dels algoritmes de seguiment. En els darrers anys, degut a la creixent capacitat de càlcul dels ordinadors, hi ha hagut un significatiu increment en el disseny de complexes sistemes de seguiment que consideren simultàniament múltiples característiques de l'objecte. No obstant, la majoria d'aquests algoritmes estan basats enheurístiques i regles ad-hoc formulades per aplications específiques, fent-ne impossible l'extrapolació a noves condicions de l'entorn.En aquesta tesi proposem un marc probabilístic general per integrar el nombre de característiques de l'objecte que siguin necessàries, permetent que interactuin mútuament per tal d'estimar-ne el seu estat amb precisió, i per tant, estimar amb precisió la posició de l'objecte que s'està seguint. Aquest marc, s'utilitza posteriorment per dissenyar un algoritme de seguiment, que es valida en diverses seqüències de vídeo que contenen canvis abruptes de posició i il·luminació, camuflament de l'objecte i deformacions no rígides. Entre les característiques que s'han utilitzat per representar l'objecte, cal destacar la paramatrització robusta del color en un espai de color dependent de l'objecte, que permet distingir-lo del fons més clarament que altres espais de color típicament ulitzats al llarg de la literatura.En la darrera part de la tesi dissenyem una tècnica per re-il·luminar tant escenes estàtiques com en moviment, de les que s'en desconeix la geometria. La re-il·luminació es realitza amb un mètode 'basat en imatges', on la generació de les images de l'escena sota noves condicions d'il·luminació s'aconsegueix a partir de combinacions lineals d'un conjunt d'imatges de referència pre-capturades, i que han estat generades il·luminant l'escena amb patrons de llum coneguts. Com que la posició i intensitat de les fonts d'il.luminació que formen aquests patrons de llum es pot controlar, és natural preguntar-nos: quina és la manera més òptima d'il·luminar una escena per tal de reduir el nombre d'imatges de referència? Demostrem que la millor manera d'il·luminar l'escena (és a dir, la que minimitza el nombre d'imatges de referència) no és utilitzant una seqüència de fonts d'il·luminació puntuals, com es fa generalment, sinó a través d'una seqüència de patrons de llum d'una base d'il·luminació depenent de l'objecte. És important destacar que quan es re-il·luminen seqüències de vídeo, les imatges successives s'han d'alinear respecte a un sistema de coordenades comú. Com que cada imatge ha estat generada per un patró de llum diferent il·uminant l'escena, es produiran canvis d'il·luminació bruscos entre imatges de referència consecutives. Sota aquestes circumstàncies, el mètode de seguiment proposat en aquesta tesi juga un paper fonamental. Finalment, presentem diversos resultats on re-il·luminem seqüències de vídeo reals d'objectes i cares d'actors en moviment. En cada cas, tot i que s'adquireix un únic vídeo, som capaços de re-il·luminar una i altra vegada, controlant la direcció de la llum, la seva intensitat, i el color.Motion analysis and object tracking has been one of the principal focus of attention over the past two decades within the computer vision community. The interest of this research area lies in its wide range of applicability, extending from autonomous vehicle and robot navigation tasks, to entertainment and virtual reality applications.Even though impressive results have been obtained in specific problems, object tracking is still an open problem, since available methods are prone to be sensitive to several artifacts and non-stationary environment conditions, such as unpredictable target movements, gradual or abrupt changes of illumination, proximity of similar objects or cluttered backgrounds. Multiple cue integration has been proved to enhance the robustness of the tracking algorithms in front of such disturbances. In recent years, due to the increasing power of the computers, there has been a significant interest in building complex tracking systems which simultaneously consider multiple cues. However, most of these algorithms are based on heuristics and ad-hoc rules formulated for specific applications, making impossible to extrapolate them to new environment conditions.In this dissertation we propose a general probabilistic framework to integrate as many object features as necessary, permitting them to mutually interact in order to obtain a precise estimation of its state, and thus, a precise estimate of the target position. This framework is utilized to design a tracking algorithm, which is validated on several video sequences involving abrupt position and illumination changes, target camouflaging and non-rigid deformations. Among the utilized features to represent the target, it is important to point out the use of a robust parameterization of the target color in an object dependent colorspace which allows to distinguish the object from the background more clearly than other colorspaces commonly used in the literature.In the last part of the dissertation, we design an approach for relighting static and moving scenes with unknown geometry. The relighting is performed through an -image-based' methodology, where the rendering under new lighting conditions is achieved by linear combinations of a set of pre-acquired reference images of the scene illuminated by known light patterns. Since the placement and brightness of the light sources composing such light patterns can be controlled, it is natural to ask: what is the optimal way to illuminate the scene to reduce the number of reference images that are needed? We show that the best way to light the scene (i.e., the way that minimizes the number of reference images) is not using a sequence of single, compact light sources as is most commonly done, but rather to use a sequence of lighting patterns as given by an object-dependent lighting basis. It is important to note that when relighting video sequences, consecutive images need to be aligned with respect to a common coordinate frame. However, since each frame is generated by a different light pattern illuminating the scene, abrupt illumination changes between consecutive reference images are produced. Under these circumstances, the tracking framework designed in this dissertation plays a central role. Finally, we present several relighting results on real video sequences of moving objects, moving faces, and scenes containing both. In each case, although a single video clip was captured, we are able to relight again and again, controlling the lighting direction, extent, and color.Postprint (published version