We present an algorithm that reduces significantly the level of the registration errors between all pairs in a set of range views. This algorithm refines initial estimates of the transformation matrices obtained from ...
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We present an algorithm that reduces significantly the level of the registration errors between all pairs in a set of range views. This algorithm refines initial estimates of the transformation matrices obtained from either the calibrated acquisition setup or a crude manual alignment. It is an instance of a category of registration algorithms known as iterated closest-point (ICP) algorithms. The algorithm considers the network of views as a whole and minimizes the registration errors of all views simultaneously. This leads to a well-balanced network of views in which the registration errors are equally distributed, an objective not met by previously published ICP algorithms which all process the views sequentially. Experimental results show that this refinement technique improves the calibrated registrations and the quality of the integrated model for complex multipart objects. In the case of scenes comprising man-made objects of very simple shapes, the basic algorithm faces problems common to all ICP algorithms and must thus be extended.
A novel paradigm for the synthesis of convergent-axis stereo geometries is presented. This paradigm incorporates constraints that represent task-oriented properties that can be easily derived in many applications of s...
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In this paper, we review the various methods for robust autonomous mobile robot navigation and scene modeling in structured environments. The techniques vary with availibility of a priori knowledge of the environment ...
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A novel paradigm for the synthesis of convergent-axis stereo geometries is presented. This paradigm incorporates constraints that represent task-oriented properties that can be easily derived in many applications of s...
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A novel paradigm for the synthesis of convergent-axis stereo geometries is presented. This paradigm incorporates constraints that represent task-oriented properties that can be easily derived in many applications of stereo-ranging, particularly those involving manipulation. Given these properties, a solution that selects the set of optimal design parameters, in the sense of accuracy, under the given constraints is presented. Results of this algorithm are discussed and analyzed in terms of the optimality of the minimum-area technique used in the derivation.
Two neural network models were developed for the prediction of postural sway response due to exposure to risk factors including environmental lighting, job-tasks, standing surface firmness, surface oiliness, work load...
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Two neural network models were developed for the prediction of postural sway response due to exposure to risk factors including environmental lighting, job-tasks, standing surface firmness, surface oiliness, work load, peripheral vision conditions, age and gender. Variables used to measure the loss of balance were index of proximity to stability boundary and sway length. Tests showed that job-task is the main risk factor that changes the output while there is some impact by age or gender on the outcome of the model. The results from these models can be used to find risk factors that have great impact on loss of balance and therefore can help in designing intervention programs.
An automatic target recognition (ATR) classifier is proposed that uses modularly cascaded vector quantizers (VQs) and multilayer perceptrons (MLPs). A dedicated VQ codebook is constructed for each target class at a sp...
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An automatic target recognition (ATR) classifier is proposed that uses modularly cascaded vector quantizers (VQs) and multilayer perceptrons (MLPs). A dedicated VQ codebook is constructed for each target class at a specific range of aspects, which is trained with the K-means algorithm and a modified learning vector quantization (LVQ) algorithm. Each final codebook is expected to give the lowest mean squared error (MSE) for its correct target class at a given range of aspects. These MSEs are then processed by an array of window MLPs and a target MLP consecutively. In the spatial domain, target recognition rates of 90.3 and 65.3 percent are achieved for moderately and highly cluttered test sets, respectively. Using the wavelet decomposition with an adaptive and independent codebook per sub-band, the VQs alone have produced recognition rates of 98.7 and 69.0 percent on more challenging training and test sets, respectively.
Visibility analysis algorithms use digital elevation models (DEMs), which represent terrain topography, to determine visibility at each point on the terrain from a given location in space. This analysis can be computa...
Visibility analysis algorithms use digital elevation models (DEMs), which represent terrain topography, to determine visibility at each point on the terrain from a given location in space. This analysis can be computationally very demanding, particularly when manipulating high resolution DEMs accurately at interactive response rates. Massively data-parallel computers offer high computing capabilities and are very well-suited to handling and processing large regular spatial data structures. In the paper, the authors present a new scanline-based data-parallel algorithm for visibility analysis. Results from an implementation onto a MasPar massively data-parallel SIMD computer are also presented.
In the emerging paradigm of animate vision, the visual processes are not thought of as being independent of cognitive or motor processing, but as an integrated system within the context of visual behavior. Intimate co...
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In the emerging paradigm of animate vision, the visual processes are not thought of as being independent of cognitive or motor processing, but as an integrated system within the context of visual behavior. Intimate coupling of sensory and motor systems have found to improve significantly the performance of behavior based vision systems. In order to study active vision systems one requires sensory-motor systems. Designing, building, and operating such a test bed is a challenging task. In this paper we describe the status of on-going work in developing a sensory-motor robotic system, R2H, with ten degrees of freedoms (DOF) for research in active vision. To complement the R2H system a Graphical Simulation and Animation (GSA) environment is also developed. The objective of building the GSA system is to create a comprehensive design tool to design and study the behavior of active systems and their interactions with the environment. GSA system aids the researchers to develop high performance and reliable software and hardware in a most effective manner. The GSA environment integrates sensing and motor actions and features complete kinematic simulation of the R2H system, it's sensors and it's workspace. With the aid of the GSA environment a Depth from Focus (DFF), Depth from Vergence, and Depth from Stereo modules are implemented and tested. The power and usefulness of the GSA system as a research tool is demonstrated by acquiring and analyzing images in the real and virtual worlds using the same software implemented and tested in the virtual world.
A necessary goal in the calibration of any instrument is an indication of how well the particular device performs. In the specific case where a camera system is used in the process of three-dimensional reconstruction ...
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A necessary goal in the calibration of any instrument is an indication of how well the particular device performs. In the specific case where a camera system is used in the process of three-dimensional reconstruction or stereopsis, geometric measures of the accuracy, precision, and repeatability of 3-D point measurements give a quantitative evaluation of the success of that algorithm. This paper presents an experimental performance analysis of a traditional calibration algorithm as applied to the problem of three-dimensional vision. The study begins with an analysis of the algorithm in a single camera or monocular system. Monocular analysis concludes with a derivation of error boundaries under different. geometric constraints. Showing how this residual error propagates from the component monocular systems into a three-dimensional reconstruction, a binocular analysis derives the desired performance monitors. The paper concludes with a geometric interpretation of the results.
Present day robot systems are manufactured to perform within industry accepted tolerances. However, to use such systems for tasks requiring high precision, various methods of robot calibration are generally required. ...
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Present day robot systems are manufactured to perform within industry accepted tolerances. However, to use such systems for tasks requiring high precision, various methods of robot calibration are generally required. These procedures can improve the accuracy of a robot within a small volume of the robot's workspace. The objective of this paper is to demonstrate the use of a single camera 3D computervision system as a position sensor in order to perform robot calibration. A vision feedback scheme, termed vision-guided Robot Control (VRC), is described which can improve the accuracy of a robot in an on-line iterative manner. This system demonstrates the advantage that can be achieved by a Cartesian space robot control scheme when end effector position/orientation are actually sensed instead of calculated from the kinematic equations. The degree of accuracy is determined by setting a tolerance level for each of the six robot Cartesian space coordinates. In general, a small tolerance level requires a large number of iterations in order to position the end effector, and a large tolerance level requires fewer iterations. The viability of using a vision system for robot calibration is demonstrated by experimentally showing that the accuracy of a robot can be drastically improved. In addition, the vision system can also be used to determine the repeatability and accuracy of a robot in a simple, efficient, and quick manner. Experimental work with an IBM Electric Drive Robot (EDR) and the proposed vision system produced a 97 and a 145 fold improvement in the position and orientation accuracy of the robot, respectively.
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