A system is presented, which monitors the execution of an assigned robot program, in order to detect execution errors. The assigned program is supposed to include sensor instructions, which allow to adapt the executio...
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This paper describes a technique for developing a CAD model-based 3-D robot vision system which can be used for recognizing and assembling parts or objects on an automated assembly line. A notable feature of the syste...
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This paper describes a technique for developing a CAD model-based 3-D robot vision system which can be used for recognizing and assembling parts or objects on an automated assembly line. A notable feature of the system is that a single eye-on-hand configuration can be used for computing disparity data and stereo matching between two 2-D images obtained by an accurately moving camera mounted on the end-arm of robot. An approach to stereo matching based on the edge-relation is proposed. The image linear feature and edge-relation set are translated to the 3-D space, and geometric models residing in a database are then used to obtain possible solutions. A novel method of computing sparse depth information is developed for matching two 3-D objects. Experimental result has shown the feasibility and effectiveness of the proposed technique. The system has been successfully implemented for recognizing a class of industrial parts.
Results are presented which generalize the known solution to the problem of minimizing the l 1 norm of the error transfer function. The optimization framework is a dual formulation employing the theory of convex funct...
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Results are presented which generalize the known solution to the problem of minimizing the l 1 norm of the error transfer function. The optimization framework is a dual formulation employing the theory of convex functionals applied to the problem of designing optimal compensators for linear feedback control systems. A wide range of cost functions can be optimized using this theory and it allows a rather general treatment of the topic of time-domain shaping of linear systems. The results obtained can be applied to the mathematically equivalent problems of optimal disturbance rejection and optimal tracking.
Selecting a good model of a set of input points by cross validation is a computationally intensive process, especially if the number of possible models or the number of training points is high. Techniques such as grad...
Selecting a good model of a set of input points by cross validation is a computationally intensive process, especially if the number of possible models or the number of training points is high. Techniques such as gradient descent are helpful in searching through the space of models, but problems such as local minima, and more importantly, lack of a distance metric between various models reduce the applicability of these search methods. Hoeffding Races is a technique for finding a good model for the data by quickly discarding bad models, and concentrating the computational effort at differentiating between the better ones. This paper focuses on the special case of leave-one-out cross validation applied to memory-based learning algorithms, but we also argue that it is applicable to any class of model selection problems.
The vehicle routing problem with time deadlines (VRPTD) is an extension of the classical vehicle routing problem (VRP) withconstraints on the latest allowable time (deadline) for servicing each customer. The objective...
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Automatic medical image analysis shows that image segmentation is a crucial task for any practical AI system in this field. On the basis of evaluation of the existing segmentation methods, a new image segmentation met...
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Automatic medical image analysis shows that image segmentation is a crucial task for any practical AI system in this field. On the basis of evaluation of the existing segmentation methods, a new image segmentation method is presented. To seek the perfect solution to knowledge representation in low level machine vision, a new knowledge representation approach—— 'Notebook' approach is proposed and the processing of visual knowledge is discussed at all levels. To integrate the computer vision theory with Gestalt psychology and knowledge engineering, a new integrated method for intelligent image segmentation of sonargraphs —— 'Generalized pattern guided segmentation' is proposed. With the meth- ods and techniques mentioned above, the medical diagnosis export system for sonargraphs can be built. The work on the preliminary experiments is also introduced.
A new texture classification method intended for classifying leathers made of the same materials is presented in this paper. Unlike the conventional methods which use global features mainly, we use both global feature...
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A new texture classification method intended for classifying leathers made of the same materials is presented. Unlike the conventional methods which mainly use global features, the authors use both global features ext...
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A new texture classification method intended for classifying leathers made of the same materials is presented. Unlike the conventional methods which mainly use global features, the authors use both global features extracted from the two dimensional power spectrum of enhanced leather trench image and the local features based on mathematical morphology operation of a segmented leather image. The experiment is conducted on a group of sample leathers and the results are satisfactory.< >
This paper focuses on reasoning with action and time. A framework for representation of activities and time in the form of a dependency network is discussed. While in earlier works an RMS framework has been used for r...
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The authors propose a neural network algorithm for adaptive pattern recognition. The algorithm consists of five steps: local feature vector forming, statistical distribution measurement, adaptive clustering, optimal c...
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The authors propose a neural network algorithm for adaptive pattern recognition. The algorithm consists of five steps: local feature vector forming, statistical distribution measurement, adaptive clustering, optimal criteria guidance, and a recursive mechanism. Based on the local feature vectors formed in parallel from the neighbors of the original data set in the correspondent pattern space, the statistical distribution is computed in parallel, and the adaptive pattern recognition is performed on the feature space vectors and not directly on the pattern vectors themselves. The optimal criteria guide the clustering procedure and determine the goodness of the clusters. Asymptotical results in the optimal sense could be achieved by the recursive mechanism. The algorithm is efficient and was applied to the image pattern recognition system.< >
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