A novel dominant correlogram based particle filter was proposed for an object tracking in visual surveillance. Particle filter outperforms the Kalman filter in non-linear and non-Gaussian estimation problem. This pape...
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A novel dominant correlogram based particle filter was proposed for an object tracking in visual surveillance. Particle filter outperforms the Kalman filter in non-linear and non-Gaussian estimation problem. This paper proposed incorporating spatial information into visual feature, and yields a reliable likelihood description of the observation and prediction. A similarity-ratio is defined to evaluate the effectivity of different similarity measurements in weighing samples. The experimental results demonstrate the effective and robust performance compared with the histogram based tracking in traffic scenes.
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.
Since in most blind source separation(BSS)algorithms the estimations of probability density function(pdf)of sources are fixed or can only switch between one sup-Gaussian and other sub-Gaussian model,they may not be ef...
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Since in most blind source separation(BSS)algorithms the estimations of probability density function(pdf)of sources are fixed or can only switch between one sup-Gaussian and other sub-Gaussian model,they may not be efficient to separate sources with different *** to solve the problem of pdf mismatch and the separation of hybrid mixture in BSS,the generalized Gaussian model(GGM)is introduced to model the pdf of the sources since it can provide a general structure of univariate *** great advantage is that only one parameter needs to be determined in modeling the pdf of different sources,so it is less complex than Gaussian mixture *** using maximum likelihood(ML)approach,the convergence of the proposed algorithm is *** computer simulations show that it is more efficient and valid than conventional methods with fixed pdf estimation.
For the fast requirement of motion object detection under complex environment, a background subtraction motion object detection method based on real-time background update is presented in this paper.
ISBN:
(纸本)9781467389808
For the fast requirement of motion object detection under complex environment, a background subtraction motion object detection method based on real-time background update is presented in this paper.
This paper studies an adaptive regulation problem for the modified FitzHugh-Nagumo neuron model under external electrical stimulation. We first present the solution of the global robust output regulation problem for o...
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This paper studies an adaptive regulation problem for the modified FitzHugh-Nagumo neuron model under external electrical stimulation. We first present the solution of the global robust output regulation problem for output feedback system with an uncertain exosystem which models the external electrical stimulation with unknown frequency and amplitude. Then, we show that the robust control problem for the modified FitzHugh-Nagumo neuron model can be formulated as the global robust output regulation problem and the solvability conditions for the output regulation problem for the modified FitzHugh-Nagumo neuron model are all satisfied. Then, we apply the obtained output regulation result to constructing an output feedback control law for the modified FitzHugh-Nagumo neuron model to achieve global stability of the closed-loop system in the presence of uncertain parameters and external stimulus. An example is given to show that the proposed algorithm can completely reject the external electrical stimulation.
patternrecognition methods were used to treat the experimentally measured data of Pitzer’s coefficients of 107 electrolytes, to find the relationships between the ionic structural parameters of these electrolytes an...
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patternrecognition methods were used to treat the experimentally measured data of Pitzer’s coefficients of 107 electrolytes, to find the relationships between the ionic structural parameters of these electrolytes and Pitzer’s coefficients. It is found that these relationships can be approximately expressed as linear equations of four dimensionless numbers, (R_+/R_-), (R_++R_-)/Z_+Z_-, (Z_+/Z_-) and (R_t/R_l), where R_+ and R_- are the cationic and anionic radii respectively; Z_+ and Z_- are the cationic and anionic charge numbers respectively, and (R_t/R_l) denotes the nonsphericity of some non-spherical ions. Besides, it is found that the difference of the nuclear magnetic resonance measured rotational relaxation time of water molecules around cations and anions, |Δτ|, has good correlation with Pitzer’s coefficients. The relationships can be interpreted by the theory of corresponding states of ionic solutions. Based on the relationships, an example of application to some hydrometallurgical process was discussed.
An improved approach for J-value segmentation （JSEG） is presented for unsupervised color image segmentation. Instead of color quantization algorithm, an automatic classification method based on adaptive mean shift ...
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An improved approach for J-value segmentation （JSEG） is presented for unsupervised color image segmentation. Instead of color quantization algorithm, an automatic classification method based on adaptive mean shift （AMS） based clustering is used for nonparametric clustering of image data set. The clustering results are used to construct Gaussian mixture modelling （GMM） of image data for the calculation of soft J value. The region growing algorithm used in JSEG is then applied in segmenting the image based on the multiscale soft J-images. Experiments show that the synergism of JSEG and the soft classification based on AMS based clustering and GMM overcomes the limitations of JSEG successfully and is more robust.
Color quantization is bound to lose spatial information of color distribution. If too much necessary spatial distribution information of color is lost in JSEG, it is difficult or even impossible for JSEG to segment im...
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Color quantization is bound to lose spatial information of color distribution. If too much necessary spatial distribution information of color is lost in JSEG, it is difficult or even impossible for JSEG to segment image correctly. Enlightened from segmentation based on fuzzy theories, soft class-map is constracted to solve that problem. The definitions of values and other related ones are adjusted according to the soft class-map. With more detailed values obtained from soft class map, more color distribution information is preserved. Experiments on a synthetic image and many other color images illustrate that JSEG with soft class-map can solve efficiently the problem that in a region there may exist color gradual variation in a smooth transition. It is a more robust method especially for images which haven＇ t been heavily blurred near boundaries of underlying regions.
Support vector machine (SVM), as a novel approach in patternrecognition, has demonstrated a success in face detection and face recognition. In this paper, a face recognition approach based on the SVM classifier with ...
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Support vector machine (SVM), as a novel approach in patternrecognition, has demonstrated a success in face detection and face recognition. In this paper, a face recognition approach based on the SVM classifier with the nearest neighbor classifier (NNC) is proposed. The principal component analysis (PCA) is used to reduce the dimension and extract features. Then one-against-all stratedy is used to train the SVM classifiers. At the testing stage, we propose an al-
To modeling and classify underwater sound, hidden Markov tree (HMT) model in wavelet domain is adopted. Taking advantage of the models, the simulation time sequence of ocean noise can be produced. An improved classifi...
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