This paper presents a novel wavelet transform saliency model to detect salient objects. In this model, a saliency map is generated by combining orientation feature maps obtained from wavelet transform of different sca...
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This paper presents a novel wavelet transform saliency model to detect salient objects. In this model, a saliency map is generated by combining orientation feature maps obtained from wavelet transform of different scale images derived from the same image. Then, the order map of a saliency map is obtained by using Fourier descriptor, which could be used as a guidance to process the most important objects. Experiments indicate that this saliency model is robust to noise and superior to other saliency models in the literature.
In this paper,three dimensions kinematics and kinetics simulation are discussed for hardware realization of a physical biped walking-chair *** direct and inverse close-form kinematics solution of the biped walking-cha...
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In this paper,three dimensions kinematics and kinetics simulation are discussed for hardware realization of a physical biped walking-chair *** direct and inverse close-form kinematics solution of the biped walking-chair robot is *** gaits are realized with the kinematics solution,including walking straight on level floor,going up stair,squatting down and standing *** Moment Point(ZMP)equation is analyzed considering the movement of the *** simulated biped walking-chair robot is used for mechanical design,gaits development and validation before they are tested on real robot.
Automatic recognition of artists is very important in acoustic music indexing, browsing, and contentbased acoustic music retrieving, but synchronously it is still a challenging errand to extract the most representativ...
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Automatic recognition of artists is very important in acoustic music indexing, browsing, and contentbased acoustic music retrieving, but synchronously it is still a challenging errand to extract the most representative and salient attributes to depict diversiform artists. In this paper, we developed a novel system to complete the reorganization of artist automatically. The proposed system can efficiently identify the artist's voice of a raw song by analyzing substantive features extracted from both pure music and singing song mixed with accompanying music. The experiments on different genres of songs illustrate that the proposed system is possible.
The Euclidean Steiner minimum tree problem is a classical NP-hard combinatorial optimization *** of the intrinsic characteristic of the hard computability,this problem cannot be solved accurately by efficient algorith...
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The Euclidean Steiner minimum tree problem is a classical NP-hard combinatorial optimization *** of the intrinsic characteristic of the hard computability,this problem cannot be solved accurately by efficient algorithms up to *** to the extensive applications in real world,it is quite important to find some heuristics for *** stochastic diffusion search algorithm is a newly population-based algorithm whose operating mechanism is quite different from ordinary intelligent algorithms,so this algorithm has its own advantage in solving some optimization *** paper has carefully studied the stochastic diffusion search algorithm and designed a cellular automata stochastic diffusion search algorithm for the Euclidean Steiner minimum tree problem which has low time *** results show that the proposed algorithm can find approving results in short time even for the large scale size,while exact algorithms need to cost several hours.
Aiming at the adverse effect caused by limited detecting probability of sensors on filtering preci- sion of a nonlinear system state, a novel muhi-sensor federated unscented Kalman filtering algorithm is proposed. Fir...
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Aiming at the adverse effect caused by limited detecting probability of sensors on filtering preci- sion of a nonlinear system state, a novel muhi-sensor federated unscented Kalman filtering algorithm is proposed. Firstly, combined with the residual detection strategy, effective observations are cor- rectly identified. Secondly, according to the missing characteristic of observations and the structural feature of unscented Kalman filter, the iterative process of the single-sensor unscented Kalman filter in intermittent observations is given. The key idea is that the state estimation and its error covariance matrix are replaced by the state one-step prediction and its error covariance matrix, when the phe- nomenon of observations missing occurs. Finally, based on the realization mechanism of federated filter, a new fusion framework of state estimation from each local node is designed. And the filtering precision of system state is improved further by the effective management of observations missing and the rational utilization of redundancy and complementary information among multi-sensor observa- tions. The theory analysis and simulation results show the feasibility and effectiveness of the pro- posed algorithm.
In order to perform a high-quality interactive rendering of large medical data sets on a single off-the-shelf PC, a LOD selection algorithm for multi-resolution volume rendering using 3D texture mapping is presented, ...
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In order to perform a high-quality interactive rendering of large medical data sets on a single off-the-shelf PC, a LOD selection algorithm for multi-resolution volume rendering using 3D texture mapping is presented, which uses an adaptive scheme that renders the volume in a region-of-interest at a high resolution and the volume away from this region at lower resolutions. The algorithm is based on several important criteria, and rendering is done adaptively by selecting high-resolution cells close to a center of attention and low-resolution cells away from this area. In addition, our hierarchical level-of-detail representation guarantees consistent interpolation between different resolution levels. Experiments have been applied to a number of large medical data and have produced high quality images at interactive frame rates using standard PC hardware.
Orientation field is one of the intrinsic charac-teristics of fingerprints, and it plays very important role in many processing phases of fingerprint recognition such as enhancement, minutiae extraction and matching. ...
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ISBN:
(纸本)9781467321969
Orientation field is one of the intrinsic charac-teristics of fingerprints, and it plays very important role in many processing phases of fingerprint recognition such as enhancement, minutiae extraction and matching. Therefore, accurate estimation of orientation filed is much necessary. In this paper, a novel PDE-based method is proposed for regularization of orientation field for low-quality fingerprint images. The method consists of four steps. Firstly, the coarse orientation field is computed using traditional gradient-based approach. Secondly, the reliability map of the orientation field is computed based on a procedure of multiscale coherence analysis. Then the orientation in the low-reliable region is reconstructed with the surrounding data by means of image inpainting technique. Finally, nonlinear diffusion filtering with adaptive diffusivity is performed on the whole orientation field. Experiments on the NIST SD4 fingerprint database indicated that the proposed algorithm is capable to estimate the orien-tation field accurately, especially for poor-quality fingerprints, and it can be integrated into fingerprint recognition systems to improve the performance.
A neural network appraoch for classification using features extracted by a mapping is presented. When the number of sample dimensions is much larger than the number of classes and no deviations are given but the means...
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A neural network appraoch for classification using features extracted by a mapping is presented. When the number of sample dimensions is much larger than the number of classes and no deviations are given but the means of classes, a mapping from class space to a new one whose dimensions is exactly equal to the number of classes is proposed. The vectors in the new space are considered as the feature vectors to be inputted to a neural network for classification. The property that the mapping does not change the separability of the original classification problem is given. Simulation results for object recognition are presented.
In this paper, we introduce a novel class of coplanar conics, the pencil of which can doubly contact to calibrate camera and estimate pose. We first analyze the properties of con-axes and con-eccentricity ellipses, wh...
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In this paper, we introduce a novel class of coplanar conics, the pencil of which can doubly contact to calibrate camera and estimate pose. We first analyze the properties of con-axes and con-eccentricity ellipses, which consist of a naturM extending pattern of concentric circles. Then the general case that two ellipses have two repeated complex intersection points is presented. This degenerate configuration results in a one-parameter family of homographies which map the planar pattern to its image. Although it is unable to compute the complete homography, an indirect 3-degree polynomial or 5-degree polynomial constraint on intrinsic parameters from one image can also be used for camera calibration and pose estimation under the minimal conditions. Furthermore, this nonlinear problem can be treated as a polynomial optimization problem （POP） and the global optimization solution can be also obtained by using SparsePOP （a sparse semidefinite programming relaxation of POPs）, Finally, the experiments with simulated data and real images are shown to verify the correctness and robustness of the proposed technique.
In this paper we discuss using the stratified ATMS to realize explanation-based learning. As the stratified ATMS can record and maintain the reasonings for beliefs efficiently and can deal with nonmonotonic reasoning,...
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In this paper we discuss using the stratified ATMS to realize explanation-based learning. As the stratified ATMS can record and maintain the reasonings for beliefs efficiently and can deal with nonmonotonic reasoning, so the ATMS-based EBL system can improve the efficiency of explanation-based learning, deal with multiple explanation problems in learning from imperfect theories by prioritized reasoning and multiple example verification and can give biases for induction in integrated learning. Copyright (C) 1996 Elsevier Science Ltd
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