Monocular object pose computation with the foveal-peripheral camera of the humanoid robot Armar-III

Active contour modelling is useful to fit non-textured objects, and algorithms have been developed to recover the motion of an object and its uncertainty. Here we show that these algorithms can be used also with point features matched in textured objects, and that active contours and point matches complement in a natural way. In the same manner we also show that depth-from-zoom algorithms, developed for zooming cameras, can be exploited also in the foveal-peripheral eye configuration present in the Armar-III humanoid robot.Peer Reviewe

Extending Linear System Models to Characterize the Performance Bounds of a Fixating Active Vision System

If active vision systems are to be used reliably in practical applications, it is crucial to understand their limits and failure modes. In the work presented here, we derive, theoretically and experimentally, bounds on the performance of an active vision system in a fixation task. In particular, we characterize the tracking limits that are imposed by the finite field of view. Two classes of target motion are examined: sinusoidal motions, representative for targets moving with high turning rates, and constant-velocity motions, exemplary for slowly varying target movements. For each class of motion, we identify a linear model of the fixating system from measurements on a real active vision system and analyze the range of target motions that can be handled with a given field of view. To illustrate the utility of such performance bounds, we sketch how the tracking performance can be maximized by dynamically adapting optical parameters of the system to the characteristics of the target motion. The originality of our work arises from combining the theoretical analysis of a complete active vision system with rigorous performance measurements on the real system. We generate repeatable and controllable target motions with the help of two robot manipulators and measure the real-time performance of the system. The experimental results are used to verify or identify a linear model of the active vision system. A major difference to related work lies in analyzing the limits of the linear models that we develop. Active vision systems have been modeled as linear systems many times before, but the performance limits at which the models break down and the system loses its target have not attracted much attention so far. With our work we hope to demonstrate how the knowledge of such limits can be used to actually extend the performance of an active vision system

Multiple-View Scenes: Reconstruction and Virtual Views

The problem of generating a virtual view of a scene, i.e. a view from a point where there is not a physical camera to capture the scene, has received recently a lot of attention from the computer vision community. This is probably due to the increase of the computational power of computers, which allows to deal with multiple view systems (systems composed of multiple cameras) efficiently. In this document, an introduction to virtual view generation techniques is presented. In a first part, geometric constraints of multiple view systems are presented. This geometric constraints allow to reconstruct the 3D information of the observed scene, and therefore they allow to generate virtual views from everywhere (although problems with occlusions will arise). In the second part of the document, the state-of-the-art on Image Based Rendering (IBR) techniques is presented. IBR techniques allow to generate virtual views from some constrained regions of the space without requiring a complete 3D reconstruction of the scene. To finish, some concussions are given

Multiple-View Scenes: Reconstruction and Virtual Views

The problem of generating a virtual view of a scene, i.e. a view from a point where there is not a physical camera to capture the scene, has received recently a lot of attention from the computer vision community. This is probably due to the increase of the computational power of computers, which allows to deal with multiple view systems (systems composed of multiple cameras) efficiently. In this document, an introduction to virtual view generation techniques is presented. In a first part, geometric constraints of multiple view systems are presented. This geometric constraints allow to reconstruct the 3D information of the observed scene, and therefore they allow to generate virtual views from everywhere (although problems with occlusions will arise). In the second part of the document, the state-of-the-art on Image Based Rendering (IBR) techniques is presented. IBR techniques allow to generate virtual views from some constrained regions of the space without requiring a complete 3D reconstruction of the scene. To finish, some concussions are given

Active tracking of foveated feature clusters using affine structure

We describe a novel method of obtaining a fixation point on a moving object for a real-time gaze control system. The method makes use of a real-time implementation of a corner detector and tracker and reconstructs the image position of the desired fixation point from a cluster of corners detected on the object using the affine structure available from two or three views. The method is fast, reliable, viewpoint invariant, and insensitive to occlusion and/or individual corner dropout or reappearance. We compare two- and three-dimensional forms of the algorithm, present results for the method in use with a high performance head/eye platform, and compare the results with two naive fixation methods. © 1996 Kluwer Academic Publishers,

Long Range Automated Persistent Surveillance

This dissertation addresses long range automated persistent surveillance with focus on three topics: sensor planning, size preserving tracking, and high magnification imaging. field of view should be reserved so that camera handoff can be executed successfully before the object of interest becomes unidentifiable or untraceable. We design a sensor planning algorithm that not only maximizes coverage but also ensures uniform and sufficient overlapped camera’s field of view for an optimal handoff success rate. This algorithm works for environments with multiple dynamic targets using different types of cameras. Significantly improved handoff success rates are illustrated via experiments using floor plans of various scales. Size preserving tracking automatically adjusts the camera’s zoom for a consistent view of the object of interest. Target scale estimation is carried out based on the paraperspective projection model which compensates for the center offset and considers system latency and tracking errors. A computationally efficient foreground segmentation strategy, 3D affine shapes, is proposed. The 3D affine shapes feature direct and real-time implementation and improved flexibility in accommodating the target’s 3D motion, including off-plane rotations. The effectiveness of the scale estimation and foreground segmentation algorithms is validated via both offline and real-time tracking of pedestrians at various resolution levels. Face image quality assessment and enhancement compensate for the performance degradations in face recognition rates caused by high system magnifications and long observation distances. A class of adaptive sharpness measures is proposed to evaluate and predict this degradation. A wavelet based enhancement algorithm with automated frame selection is developed and proves efficient by a considerably elevated face recognition rate for severely blurred long range face images

Estimació del moviment de robots mitjançant contorns actius

Aquesta tesi versa sobre l'estimació del moviment d'un robot mòbil a partir dels canvis en les imatges captades per una càmera muntada sobre el robot. El moviment es dedueix amb un algorisme prèviament proposat en el marc de la navegació qualitativa. Per tal d'emprar aquest algorisme en casos reals s'ha fet un estudi de la seva precisió. Per augmentar-ne l'aplicabilitat, s'ha adaptat l'algorisme al cas d'una càmera amb moviments d'orientació i de zoom.Quan els efectes perspectius no són importants, dues vistes d'una escena captades pel robot es poden relacionar amb una transformació afí (o afinitat), que normalment es calcula a partir de correspondències de punts. En aquesta tesi es vol seguir un enfoc alternatiu, i alhora complementari, fent servir la silueta d'un objecte modelada mitjançant un contorn actiu. El marc es el següent: a mesura que el robot es va movent, la projecció de l'objecte a la imatge va canviant i el contorn actiu es deforma convenientment per adaptar-s'hi; de les deformacions d'aquest contorn, expressades en espai de forma, se'n pot extreure el moviment del robot fins a un factor d'escala. Els contorns actius es caracteritzen per la rapidesa en la seva extracció i la seva robustesa a oclusions parcials. A més, un contorn és fàcil de trobar fins i tot en escenes poc texturades, on sovint és difícil trobar punts característics i la seva correspondència.La primera part d'aquest treball té l'objectiu de caracteritzar la precisió i la incertesa en l'estimació del moviment. Per avaluar la precisió, primer es duen a terme un parell d'experiències pràctiques, que mostren la potencialitat de l'algorisme en entorns reals i amb diferents robots. Estudiant la geometria epipolar que relaciona dues vistes d'un objecte planar es demostra que la direcció epipolar afí es pot recuperar en el cas que el moviment de la càmera estigui lliure de ciclorotació. Amb una bateria d'experiments, tant en simulació com reals, es fa servir la direcció epipolar per caracteritzar la precisió global de l'afinitat en diferents situacions, com ara, davant de diferents formes dels contorns, condicions de visualització extremes i soroll al sistema.Pel que fa a la incertesa, gràcies a que la implementació es basa en el filtre de Kalman, per a cada estimació del moviment també es té una estimació de la incertesa associada, però expressada en espai de forma. Per tal propagar la incertesa de l'espai de forma a l'espai de moviment 3D s'han seguit dos camins diferents: un analític i l'altre estadístic. Aquest estudi ha permès determinar quins graus de llibertat es recuperen amb més precisió, i quines correlacions existeixen entre les diferents components. Finalment, s'ha desenvolupat un algorisme que permet propagar la incertesa del moviment en temps de vídeo. Una de les limitacions més importants d'aquesta metodologia és que cal que la projecció de l'objecte estigui dins de la imatge i en condicions de visualització de perspectiva dèbil durant tota la seqüència. En la segona part d'aquest treball, s'estudia el seguiment de contorns actius en el marc de la visió activa per tal de superar aquesta limitació. És una relació natural, atès que el seguiment de contorns actius es pot veure com una tècnica per fixar el focus d'atenció. En primer lloc, s'han estudiat les propietats de les càmeres amb zoom i s'ha proposat un nou algorisme per determinar la profunditat de la càmera respecte a un objecte qualsevol. L'algorisme inclou un senzill calibratge geomètric que no implica cap coneixement sobre els paràmetres interns de la càmera. Finalment, per tal d'orientar la càmera adequadament, compensant en la mesura del possible els moviments del robot, s'ha desenvolupat un algorisme per al control dels mecanismes de zoom, capcineig i guinyada, i s'ha adaptat l'algorisme d'estimació del moviment incorporant-hi els girs coneguts del capcineig i la guinyada.This thesis deals with the motion estimation of a mobile robot from changes in the images acquired by a camera mounted on the robot itself. The motion is deduced with an algorithm previously proposed in the framework of qualitative navigation. In order to employ this algorithm in real situations, a study of its accuracy has been performed. Moreover, relationships with the active vision paradigm have been analyzed, leading to an increase in its applicability.When perspective effects are not significant, two views of a scene are related by an affine transformation (or affinity), that it is usually computed from point correspondences. In this thesis we explore an alternative and at the same time complementary approach, using the contour of an object modeled by means of an active contour. The framework is the following: when the robot moves, the projection of the object in the image changes and the active contour adapts conveniently to it; from the deformation of this contour, expressed in shape space, the robot egomotion can be extracted up to a scale factor. Active contours are characterized by the speed of their extraction and their robustness to partial occlusions. Moreover, a contour is easy to find even in poorly textured scenes, where often it is difficult to find point features and their correspondences.The goal of the first part of this work is to characterize the accuracy and the uncertainty in the motion estimation. Some practical experiences are carried out to evaluate the accuracy, showing the potentiality of the algorithm in real environments and with different robots. We have studied also the epipolar geometry relating two views of a planar object. We prove that the affine epipolar direction between two images can be recovered from a shape vector when the camera motion is free of cyclorotation. With a battery of simulated as well as real experiments, the epipolar direction allows us to analyze the global accuracy of the affinity in a variety of situations: different contour shapes, extreme visualization conditions and presence of noise.Regarding uncertainty, since the implementation is based on a Kalman filter, for each motion estimate we have also its covariance matrix expressed in shape space. In order to propagate the uncertainty from shape space to 3D motion space, two different approaches have been followed: an analytical and a statistical one. This study has allowed us to determine which degrees of freedom are recovered with more accuracy, and what correlations exist between the different motion components. Finally, an algorithm to propagate the motion uncertainty at video rate has been proposed.One of the most important limitations of this methodology is that the object must project onto the image under weak-perspective visualization conditions all along the sequence. In the second part of this work, active contour tracking is studied within the framework of active vision to overcome this limitation. Both relate naturally, as active contour tracking can be seen as a focus-of-attention strategy.First, the properties of zooming cameras are studied and a new algorithm is proposed to estimate the depth of the camera with respect to an object. The algorithm includes a simple geometric calibration that does not require any knowledge about the camera internal parameters.Finally, in order to orientate the camera so as to suitably compensate for robot motion when possible, a new algorithm has been proposed for the control of zoom, pan and tilt mechanisms, and the motion estimation algorithm has been updated conveniently to incorporate the active camera state information