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

Robot Egomotion from the Deformation of Active Contours

Traditional sources of information for image-based computer vision algorithms have been points, lines, corners, and recently SIFT features (Lowe, 2004), which seem to represent at present the state of the art in feature definition. Alternatively, the present work explores the possibility of using tracked contours as informative features, especially in applications no

Automated B-Spline Curve Representation Incorporating MDL and Error-Minimizing Control Point Insertion Strategies

The main issues of developing an automatic and reliable scheme for splinefitting are discussed and addressed in this paper, which are not fully covered in previous papers or algorithms. The proposed method incorporates B-spline active contours, the minimum description length (MDL) principle, and a novel control point insertion strategy based on maximizing the Potential for EnergyReduction Maximisation (PERM). A comparison of test results show that it outperforms one of the best existing methods. 1 Introduction Representing curves by analytic functions instead of sets of data points allows the geometry of curves to be exploited in various ways [1], and may also be used for data smoothing [2] or for storing data efficiently. However, it is difficult to find a fully automated spline-fitting method which consistently performs as well as methods based on human assistance. A number of schemes have been proposed for fitting analytic functions to image curves. Duda and Hart [3] sugges..

Coronal loop detection from solar images and extraction of salient contour groups from cluttered images.

This dissertation addresses two different problems: 1) coronal loop detection from solar images: and 2) salient contour group extraction from cluttered images. In the first part, we propose two different solutions to the coronal loop detection problem. The first solution is a block-based coronal loop mining method that detects coronal loops from solar images by dividing the solar image into fixed sized blocks, labeling the blocks as Loop or Non-Loop , extracting features from the labeled blocks, and finally training classifiers to generate learning models that can classify new image blocks. The block-based approach achieves 64% accuracy in IO-fold cross validation experiments. To improve the accuracy and scalability, we propose a contour-based coronal loop detection method that extracts contours from cluttered regions, then labels the contours as Loop and Non-Loop , and extracts geometric features from the labeled contours. The contour-based approach achieves 85% accuracy in IO-fold cross validation experiments, which is a 20% increase compared to the block-based approach. In the second part, we propose a method to extract semi-elliptical open curves from cluttered regions. Our method consists of the following steps: obtaining individual smooth contours along with their saliency measures; then starting from the most salient contour, searching for possible grouping options for each contour; and continuing the grouping until an optimum solution is reached. Our work involved the design and development of a complete system for coronal loop mining in solar images, which required the formulation of new Gestalt perceptual rules and a systematic methodology to select and combine them in a fully automated judicious manner using machine learning techniques that eliminate the need to manually set various weight and threshold values to define an effective cost function. After finding salient contour groups, we close the gaps within the contours in each group and perform B-spline fitting to obtain smooth curves. Our methods were successfully applied on cluttered solar images from TRACE and STEREO/SECCHI to discern coronal loops. Aerial road images were also used to demonstrate the applicability of our grouping techniques to other contour-types in other real applications

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