A technique for the calibration of an active camera system is presented. The calibration of manipulator, camera-to-manipulator, camera, and base-to-world is treated in a unified and elegant way. In this approach, the ...
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A fitting relationship in an assembly implies that the relative location of the bodies belongs to a coset of the symmetry group of the mating feature pair. When a symmetry group is continuous, there are infinitesimal ...
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A fitting relationship in an assembly implies that the relative location of the bodies belongs to a coset of the symmetry group of the mating feature pair. When a symmetry group is continuous, there are infinitesimal displacements which preserve the relationship. Assembly of two bodies normally involves the establishment of successively more constraining relations, many of which are fitting relations. The continuous topological structure of the associated group determines possible directions of assembly at any state in the assembly process. To accommodate to errors, it is necessary to choose a stiffness matrix appropriate to a given assembly state, which allows the robot to comply with wrenches normal to the possible assembly directions. The derivation of such matrices from a computational geometric representation of the mating feature symmetry group is considered.< >
The rediscovery and popularization of the backpropagation training technique for multilayer perceptrons as well as the invention of the Boltzmann machine learning algorithm has given a new boost to the study on superv...
The rediscovery and popularization of the backpropagation training technique for multilayer perceptrons as well as the invention of the Boltzmann machine learning algorithm has given a new boost to the study on supervised learning networks. In recent years, besides widely spread applications and various further improvements of the classical backpropagation technique, many new supervised learning models, techniques as well as theories, have also been proposed in a vast number of publications. This paper tries to give a rather systematic review on the recent advances on supervised learning techniques and models for static feedforward networks. We summarize a great number of developments into four aspects: (1) Various improvements and variants made on the classical backpropagation techniques for Multilayer(static) perceptron nets, for speeding up training, avoiding local minima, increasing the generalization ability as well as for many other interesting purposes. (2) A number of other learning methods for training multilayer (static) perceptron, such as derivative estimation by perturbation, direct weight update by perturbation, genetic algorithms, recursive least square estimate and extended Kalman filters, linear programming, the policy of fixing one layer while updating another, constructing networks by converting decision tree classifiers and others. (3) Various other feedforward models which are also able to implement function approximation, probability density estimation and classification, including various models of basis function expansion (e.g. radial basis functions, restricted coulomb energy, multivariate adaptive regression splines, trigonometric and polynomial bases, projection pursuit, basis function tree and many others) and several other supervised learning models. (4) Models with complex structures, e.g. modular architecture, hierarchy architecture and others. Altogether, we try to give a global picture of the present state of supervised learning tech
The framework of controlled active vision is applied to the problem of monocular full 3-D robotic visual tracking (three translations and three rotations). Full 3-D tracking of a moving target by a monocular hand-eye ...
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The framework of controlled active vision is applied to the problem of monocular full 3-D robotic visual tracking (three translations and three rotations). Full 3-D tracking of a moving target by a monocular hand-eye system is demonstrated. A single camera is used. A simple adaptive scheme is proposed, and the relative distance of the target from the camera is assumed to be partially unknown. The number of parameters that must be estimated online is minimal, resulting in a feasible real-time implementation of the scheme. The strong coupling of the rotational and translational degrees of freedom is treated in a way that guarantees robust tracking of the object. The limitations of the approach are discussed, and the results from its application to the TROIKABOT system (a set of three PUMA 560's manipulators) are presented.< >
Using active monocular vision for 3-D visual control tasks is difficult since the translational and the rotational degrees of freedom are strongly coupled. The paper addresses several issues in 3-D visual control and ...
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Using active monocular vision for 3-D visual control tasks is difficult since the translational and the rotational degrees of freedom are strongly coupled. The paper addresses several issues in 3-D visual control and presents adaptive control schemes for the problem of robotic visual servoing (eye-in-hand configuration) around a static rigid target. The objective is to move the image projections of several feature points of the static rigid target to some desired image positions. The inverse perspective transformation is assumed partially unknown. The adaptive controllers compensate for the servoing errors, the partially unknown camera parameters, and the computational delays which are introduced by the time-consuming vision algorithms. The authors present a stability analysis along with a study of the conditions that the feature points must satisfy in order for the problem to be solvable. Finally, several experimental results are presented to verify the validity and the efficacy of the proposed algorithms.< >
An approach for tracing, representation, and recognition of a handwritten numeral in an offline environment is presented. A 2D spatial representation of a numeral is first transformed into a 3D spatiotemporal represen...
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An approach for tracing, representation, and recognition of a handwritten numeral in an offline environment is presented. A 2D spatial representation of a numeral is first transformed into a 3D spatiotemporal representation by identifying the tracing sequence based on a set of heuristic rules acting as transformation operators. Given the dynamic information of the tracing sequence, a multiresolution critical-point segmentation method is proposed to extract local feature points, at varying degrees of scale and coarseness. A neural network architecture, the hierarchically self-organizing learning (HSOL) network (S. Lee, J.C. Pan, 1989), especially for handwritten numeral recognition, is presented. Experimental results based on a bidirectional HSOL network indicated that the method is robust in terms of variations, deformations, and corruption, achieving about 99% recognition rate for the test patterns.< >
A novel approach to obstacle detection using optical flow without recovering range information has been developed. This method can be used for ground vehicles to navigate through man-made roadways or natural outdoor t...
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Presents a new approach to detect step edges with subpixel accuracy. The proposed approach is based on a set of orthogonal complex moments of the image known as Zernike moments. An ideal 2-D step edge is modeled in te...
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Presents a new approach to detect step edges with subpixel accuracy. The proposed approach is based on a set of orthogonal complex moments of the image known as Zernike moments. An ideal 2-D step edge is modeled in terms of four parameters: the background gray level, the step size, the distance of the edge from the center of the mask, and the orientation of the edge. Discrete Zernike moments are used to obtain a total of three masks to compute all the edge parameters for subpixel detection. For pixel-level edge detection only two masks (one real and one complex) are required. The theoretical analysis of the influence of noise on the location and the orientation of an edge is presented. This analysis reveals that the accuracy of the proposed approach is virtually unaffected by the additive noise. Experimental results are presented to demonstrate the efficacy of the proposed technique.< >
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