53 resultados para Robot Vision

em CentAUR: Central Archive University of Reading - UK


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A vision system for recognizing rigid and articulated three-dimensional objects in two-dimensional images is described. Geometrical models are extracted from a commercial computer aided design package. The models are then augmented with appearance and functional information which improves the system's hypothesis generation, hypothesis verification, and pose refinement. Significant advantages over existing CAD-based vision systems, which utilize only information available in the CAD system, are realized. Examples show the system recognizing, locating, and tracking a variety of objects in a robot work-cell and in natural scenes.

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Researchers in the rehabilitation engineering community have been designing and developing a variety of passive/active devices to help persons with limited upper extremity function to perform essential daily manipulations. Devices range from low-end tools such as head/mouth sticks to sophisticated robots using vision and speech input. While almost all of the high-end equipment developed to date relies on visual feedback alone to guide the user providing no tactile or proprioceptive cues, the “low-tech” head/mouth sticks deliver better “feel” because of the inherent force feedback through physical contact with the user's body. However, the disadvantage of a conventional head/mouth stick is that it can only function in a limited workspace and the performance is limited by the user's strength. It therefore seems reasonable to attempt to develop a system that exploits the advantages of the two approaches: the power and flexibility of robotic systems with the sensory feedback of a headstick. The system presented in this paper reflects the design philosophy stated above. This system contains a pair of master-slave robots with the master being operated by the user's head and the slave acting as a telestick. Described in this paper are the design, control strategies, implementation and performance evaluation of the head-controlled force-reflecting telestick system.

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This paper presents a review of the design and development of the Yorick series of active stereo camera platforms and their integration into real-time closed loop active vision systems, whose applications span surveillance, navigation of autonomously guided vehicles (AGVs), and inspection tasks for teleoperation, including immersive visual telepresence. The mechatronic approach adopted for the design of the first system, including head/eye platform, local controller, vision engine, gaze controller and system integration, proved to be very successful. The design team comprised researchers with experience in parallel computing, robot control, mechanical design and machine vision. The success of the project has generated sufficient interest to sanction a number of revisions of the original head design, including the design of a lightweight compact head for use on a robot arm, and the further development of a robot head to look specifically at increasing visual resolution for visual telepresence. The controller and vision processing engines have also been upgraded, to include the control of robot heads on mobile platforms and control of vergence through tracking of an operator's eye movement. This paper details the hardware development of the different active vision/telepresence systems.

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A robot mounted camera is useful in many machine vision tasks as it allows control over view direction and position. In this paper we report a technique for calibrating both the robot and the camera using only a single corresponding point. All existing head-eye calibration systems we have encountered rely on using pre-calibrated robots, pre- calibrated cameras, special calibration objects or combinations of these. Our method avoids using large scale non-linear optimizations by recovering the parameters in small dependent groups. This is done by performing a series of planned, but initially uncalibrated robot movements. Many of the kinematic parameters are obtained using only camera views in which the calibration feature is at, or near the image center, thus avoiding errors which could be introduced by lens distortion. The calibration is shown to be both stable and accurate. The robotic system we use consists of camera with pan-tilt capability mounted on a Cartesian robot, providing a total of 5 degrees of freedom.

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Model based vision allows use of prior knowledge of the shape and appearance of specific objects to be used in the interpretation of a visual scene; it provides a powerful and natural way to enforce the view consistency constraint. A model based vision system has been developed within ESPRIT VIEWS: P2152 which is able to classify and track moving objects (cars and other vehicles) in complex, cluttered traffic scenes. The fundamental basis of the method has been previously reported. This paper presents recent developments which have extended the scope of the system to include (i) multiple cameras, (ii) variable camera geometry, and (iii) articulated objects. All three enhancements have easily been accommodated within the original model-based approach

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The paper describes a novel integrated vision system in which two autonomous visual modules are combined to interpret a dynamic scene. The first module employs a 3D model-based scheme to track rigid objects such as vehicles. The second module uses a 2D deformable model to track non-rigid objects such as people. The principal contribution is a novel method for handling occlusion between objects within the context of this hybrid tracking system. The practical aim of the work is to derive a scene description that is sufficiently rich to be used in a range of surveillance tasks. The paper describes each of the modules in outline before detailing the method of integration and the handling of occlusion in particular. Experimental results are presented to illustrate the performance of the system in a dynamic outdoor scene involving cars and people.

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This paper discusses and compares the use of vision based and non-vision based technologies in developing intelligent environments. By reviewing the related projects that use vision based techniques in intelligent environment design, the achieved functions, technical issues and drawbacks of those projects are discussed and summarized, and the potential solutions for future improvement are proposed, which leads to the prospective direction of my PhD research.

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The intelligent controlling mechanism of a typical mobile robot is usually a computer system. Research is however now ongoing in which biological neural networks are being cultured and trained to act as the brain of an interactive real world robot – thereby either completely replacing or operating in a cooperative fashion with a computer system. Studying such neural systems can give a distinct insight into biological neural structures and therefore such research has immediate medical implications. The principal aims of the present research are to assess the computational and learning capacity of dissociated cultured neuronal networks with a view to advancing network level processing of artificial neural networks. This will be approached by the creation of an artificial hybrid system (animat) involving closed loop control of a mobile robot by a dissociated culture of rat neurons. This paper details the components of the overall animat closed loop system architecture and reports on the evaluation of the results from preliminary real-life and simulated robot experiments.

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This paper presents the development of an indoor localization system using camera vision. The localization system has a capability to determine 2D coordinate (x, y) for a team of mobile robots, Miabot. The experimental results show that the system outperforms our existing sonar localizer both in accuracy and a precision.

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In an immersive virtual environment, observers fail to notice the expansion of a room around them and consequently make gross errors when comparing the size of objects. This result is difficult to explain if the visual system continuously generates a 3-D model of the scene based on known baseline information from interocular separation or proprioception as the observer walks. An alternative is that observers use view-based methods to guide their actions and to represent the spatial layout of the scene. In this case, they may have an expectation of the images they will receive but be insensitive to the rate at which images arrive as they walk. We describe the way in which the eye movement strategy of animals simplifies motion processing if their goal is to move towards a desired image and discuss dorsal and ventral stream processing of moving images in that context. Although many questions about view-based approaches to scene representation remain unanswered, the solutions are likely to be highly relevant to understanding biological 3-D vision.

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It is twenty-five years since the posthumous publication of David Marr's book Vision [1]. Only 35 years old when he died, Man, had already dramatically influenced vision research. His book, and the series of papers that preceded it, have had a lasting impact on the way that researchers approach human and computer vision.

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In an immersive virtual reality environment, subjects fail to notice when a scene expands or contracts around them, despite correct and consistent information from binocular stereopsis and motion parallax, resulting in gross failures of size constancy (A. Glennerster, L. Tcheang, S. J. Gilson, A. W. Fitzgibbon, & A. J. Parker, 2006). We determined whether the integration of stereopsis/motion parallax cues with texture-based cues could be modified through feedback. Subjects compared the size of two objects, each visible when the room was of a different size. As the subject walked, the room expanded or contracted, although subjects failed to notice any change. Subjects were given feedback about the accuracy of their size judgments, where the “correct” size setting was defined either by texture-based cues or (in a separate experiment) by stereo/motion parallax cues. Because of feedback, observers were able to adjust responses such that fewer errors were made. For texture-based feedback, the pattern of responses was consistent with observers weighting texture cues more heavily. However, for stereo/motion parallax feedback, performance in many conditions became worse such that, paradoxically, biases moved away from the point reinforced by the feedback. This can be explained by assuming that subjects remap the relationship between stereo/motion parallax cues and perceived size or that they develop strategies to change their criterion for a size match on different trials. In either case, subjects appear not to have direct access to stereo/motion parallax cues.

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The intelligent controlling mechanism of a typical mobile robot is usually a computer system. Some recent research is ongoing in which biological neurons are being cultured and trained to act as the brain of an interactive real world robot�thereby either completely replacing, or operating in a cooperative fashion with, a computer system. Studying such hybrid systems can provide distinct insights into the operation of biological neural structures, and therefore, such research has immediate medical implications as well as enormous potential in robotics. The main aim of the research is to assess the computational and learning capacity of dissociated cultured neuronal networks. A hybrid system incorporating closed-loop control of a mobile robot by a dissociated culture of neurons has been created. The system is flexible and allows for closed-loop operation, either with hardware robot or its software simulation. The paper provides an overview of the problem area, gives an idea of the breadth of present ongoing research, establises a new system architecture and, as an example, reports on the results of conducted experiments with real-life robots.

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The relative roles of olfaction and vision in the crepuscular host-finding process of a major lepidopteran pest of cruciferous crops, the diamondback moth Plutella xylostella are investigated in a series of laboratory and semi-field experiments. Flying female moths use volatile plant chemical cues to locate and to promote landing on their host, even in complex mixed-crop environments in large cages. Multiple regression analysis shows that both the plant position (front, middle or back rows) and the type of plant (host plant, nonhost plant) are needed to explain the distribution of insects in such a mixed-crop situation. This strong plant position effect indicates that, when host plants are present in a mixture, foraging P. xylostella are more likely to alight on the first row of the plants. The findings are discussed with regard to current theories of host-plant location by phytophagous insects and the possible implications for integrated pest management.