The computation of texture and of stereoscopic depth is limited by a number of factors in the design of the optical front-end and subsequent processing stages in humans and machines. A number of limiting factors in the human visual system, such as resolution of the optics and opto-electronic interface, contrast, luminance, temporal resolution and eccentricity are reviewed and evaluated concerning their relevance for the recognition of texture and stereoscopic depth. The algorithms used by the human brain to discriminate between textures and to compute stereoscopic depth are very fast and efficient. Their study might be beneficial for the development of better algorithms in machine vision.

A mobile robot must be able to determine its own position in the world. To support truly autonomous navigation, we present a system that builds and maintains its own models of world locations and uses these models to recognize its world position from stereo vision input. The system is designed to be robust with respect to input errors and to respond to a gradually changing world by updating the world location models. We present results from tests of the system that demonstrate its reliability. The model builder and recognition system fit into a planned world modeling system that we describe.

Obtaining exact depth from binocular disparities is hard if camera calibration is needed. We will show that qualitative depth information can be obtained from stereo disparities with almost no computations and with no prior knowledge (or computation) of camera parameters. We derive two expressions that order all matched points in the images in two distinct depth-consistent ways from image coordinates only. One is a tilt-related order $\\lambda$, the other is a depth-related order $\\chi$. Using $\\lambda$ demonstrates some anomalies and unusual characteristics that have been observed in psychophysical experiments. The same approach is applied to qualitatively estimate changes in the curvature of a contour on the surface of an object, with either $x$- or $y$-coordinate fixed.

The goal of low-level vision is to estimate an underlying scene, given an observed image. Real-world scenes (e.g., albedos or shapes) can be very complex, conventionally requiring high dimensional representations which are hard to estimate and store. We propose a low-dimensional representation, called a scene recipe, that relies on the image itself to describe the complex scene configurations. Shape recipes are an example: these are the regression coefficients that predict the bandpassed shape from bandpassed image data. We describe the benefits of this representation, and show two uses illustrating their properties: (1) we improve stereo shape estimates by learning shape recipes at low resolution and applying them at full resolution; (2) Shape recipes implicitly contain information about lighting and materials and we use them for material segmentation.

With the rapid growth in infrared sensor technology and its drastic cost reduction, the potential of application of these imaging technologies in computer vision systems has increased. One potential application for IR imaging is depth from stereo. Discerning depth from stereopsis is difficult because the quality of un-cooled sensors is not sufficient for generating dense depth maps. In this thesis, we investigate the production of sparse disparity maps from un-calibrated infrared stereo images and agree that a dense depth field may not be attained directly from IR stereo images, but perhaps a sparse depth field may be obtained that can be interpolated to produce a dense/semi-dense depth field. In our proposed technique, the sparse disparity map is produced by a robust features-based stereo matching method capable of dealing with the problems of infrared images, such as low resolution and high noise; initially, a set of stable features are extracted from stereo pairs using the phase congruency model, which contrary to the gradient-based feature detectors, provides features that are invariant to geometric transformations. Then, a set of Log-Gabor wavelet coefficients at different orientations and frequencies is used to analyze and describe the extracted features for matching. The resulted sparse disparity map is then refined by triangular and epipolar geometrical constraints. In densifying the sparse map, a watershed transformation is applied to divide the image into several segments, where the disparity inside each segment is assumed to vary smoothly. The surface of each segment is then reconstructed independently by fitting a spline to its known disparities; Experiments on a set of indoor and outdoor IR stereo pairs lend credibility to the robustness of our IR stereo matching and surface reconstruction techniques and hold promise for low-resolution stereo images which don’t have a large amount of texture and local details.

Den här rapporten redovisar arbetet bakom en utveckling och framtagning av aktiva, trådlösa hifi-högtalare, där teorier om unika akustiska fenomen testas och bekräftas.  Högtalarna är tänkta att användas som utställningsobjekt och designkoncept. Även ett tillhörande styrsystem har utvecklats. Arbetet inleddes med en omfattande förstudie som innefattade användarundersökning och konkurrensstudie. Utifrån dessa kunde en kravspecifikation utformas som skulle komma att fungera som ett styrdokument genom designprocessen. Idégenereringsmetoder, konceptval, modellering samt fysisk byggnation ledde fram till gruppens önskade resultat. Stor tyngd lades även på att hålla ett genomgående formspråk i alla delar av systemet, där vi fokuserade på att utgå ifrån de former som är av stor akustisk betydelse, så att ljudegenskaperna kunde optimeras, samtidigt som en god design kunde erhållas. Resultatet blev två stycken fysiska, fullt funktionella och formmässigt korrekta hifi-högtalare, gjorda åt uppdragsgivaren Transient Design. Det blev även en hel högtalarserie med fronthögtalare i två storlekar, stativhögtalare samt ett tillhörande interaktivt styrsystem. Det unika med detta system är dels kombinationen av trådlösa fullregisterhögtalare som sätter hifi-ljudet i centrum och samtidigt interagerar med en styrenhet som kommunicerar med de ljudkällor som önskas, och dels den hornprincip som högtalarna är byggda efter som ger dem en unik karaktär. I slutet av rapporten återfinns en diskussion där projektgruppen reflekterar mer om arbetsgången och resultatet. / This report shows the development and production of active, wireless hifi-speakers, where theories about some unique acoustic phenomena are tested and confirmed. The speakers are intended to be used as exhibits and design concepts. Also, an associated control system was developed.   The work began with a comprehensive pilot study that included user survey and a competitor study. The pilot study resulted in a requirement specification which worked as a steering document through the design process. An important part through the project was also to use an consistently design in all of the parts in the system, and the design was built by shapes which gave positive results for the acoustics. This meant that the sound capacity could be optimized and mixed with an attractive design.   The result was two, fully functional hi-fi speakers, made for the client Transient Design as a design concept. It also became a finished speaker series with the front speakers in two sizes, tripod speakers, and an interactive control system. The unique part of this system is partly the combination of the wireless full registry speakers, which puts the hifi-sound in the centre and interacts with head unit, and partly the horn which the speakers have its base design from. The principals of this horn give the speakers a unique character   A discussion in which the project team reflects more about the workflow and the result is found at the end of the report.

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With the rapid growth in infrared sensor technology and its drastic cost reduction, the potential of application of these imaging technologies in computer vision systems has increased. One potential application for IR imaging is depth from stereo. Discerning depth from stereopsis is difficult because the quality of un-cooled sensors is not sufficient for generating dense depth maps. In this thesis, we investigate the production of sparse disparity maps from un-calibrated infrared stereo images and agree that a dense depth field may not be attained directly from IR stereo images, but perhaps a sparse depth field may be obtained that can be interpolated to produce a dense/semi-dense depth field. In our proposed technique, the sparse disparity map is produced by a robust features-based stereo matching method capable of dealing with the problems of infrared images, such as low resolution and high noise; initially, a set of stable features are extracted from stereo pairs using the phase congruency model, which contrary to the gradient-based feature detectors, provides features that are invariant to geometric transformations. Then, a set of Log-Gabor wavelet coefficients at different orientations and frequencies is used to analyze and describe the extracted features for matching. The resulted sparse disparity map is then refined by triangular and epipolar geometrical constraints. In densifying the sparse map, a watershed transformation is applied to divide the image into several segments, where the disparity inside each segment is assumed to vary smoothly. The surface of each segment is then reconstructed independently by fitting a spline to its known disparities; Experiments on a set of indoor and outdoor IR stereo pairs lend credibility to the robustness of our IR stereo matching and surface reconstruction techniques and hold promise for low-resolution stereo images which don’t have a large amount of texture and local details.

The capability of hand-eye coordination is one of the dexterous skills owned by human beings. In order to reproduce the skillfully operational technique and demonstrate basic hand-eye coordination technique, a robotic wrist system with stereo visual feedback strategy from the viewpoint of table tennis is presented in this thesis. Based on the concept of aerodynamics and collision mechanics, the exerted forces acting on the ball are analyzed. Three stages, including the ball flying in the air, inelastic collision between a racket and the ball, and hitting strategy of humans, will be investigated. At the same time, three-dimensional information for the scene is established using images acquired by dual cameras with a calibration process. Therefore, the flying status of the ball and its position in the space can be calculated. The robotic wrist located underneath will then be controlled to hit the ball to allow it bouncing upwards. The mechanical wrist is operated by three servo motors to simulate actual movement of human wrist. The objective is to successively hit the ball with the racket to achieve intelligent hand-eye coordination control on ball bouncing.

Stereo imaging is routinely used in Simultaneous Localization and Mapping (SLAM) systems for the navigation and control of autonomous spacecraft proximity operations, advanced robotics, and robotic mapping and surveying applications. A key step (and generally the most computationally expensive step) in the generation of high fidelity geometric environment models from image data is the solution of the dense stereo correspondence problem. A novel method for solving the stereo correspondence problem to sub-pixel accuracy in the Fourier frequency domain by exploiting the Convolution Theorem is developed. The method is tailored to challenging aerospace applications by incorporation of correction factors for common error sources. Error-checking metrics verify correspondence matches to ensure high quality depth reconstructions are generated. The effect of geometric foreshortening caused by the baseline displacement of the cameras is modeled and corrected, drastically improving correspondence matching on highly off-normal surfaces. A metric for quantifying the strength of correspondence matches is developed and implemented to recognize and reject weak correspondences, and a separate cross-check verification provides a final defense against erroneous matches. The core components of this phase based dense stereo algorithm are implemented and optimized in the Compute Uni ed Device Architecture (CUDA) parallel computation environment onboard an NVIDIA Graphics Processing Unit (GPU). Accurate dense stereo correspondence matching is performed on stereo image pairs at a rate of nearly 10Hz.