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.
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.
A novel image registration scheme is proposed. In the proposed scheme, the complete isometric mapping (Isomap) is used to extract features from the image sets, and these features are input vectors of feedforward neura...
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We introduce a velocity-difference-separation model that modifies the previous models in the literature. The improvement of this new model over the previous ones lies in that it not only theoretically retains many str...
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We introduce a velocity-difference-separation model that modifies the previous models in the literature. The improvement of this new model over the previous ones lies in that it not only theoretically retains many strong points of the previous ones, but also performs more realistically than others in the dynamical evolution of congestion. Furthermore, the proposed model is investigated with analytic and numerical methods, with the finding that it can demonstrate some complex physical features observed in real traffic such as the existence of three phases： free flow, synchronized flow, and wide moving jam; sudden flow drop in flow-density plane; and traffic hysteresis in transition between the free and the synchronized flow.
A novel image registration scheme is proposed. In the proposed scheme, two-dimensional principal component analysis (2DPCA) combined with principal component analysis (PCA) is used to extract features from the image s...
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We propose to use a low order autoregressive (AR) model to describe the temporal structure of source. Then we adopt Variational Bayesian (VB) method for source separation which models additive noise into the mixing sy...
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In this paper, we propose a new method to improve the image registration accuracy in feedforward neural networks (FNN) based scheme. In the proposed method, Bayesian regularization is applied to improve the generaliza...
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Independent Component Analysis (ICA) feature extraction is an efficient sparse coding method for noise suppression. However, single channel signal can not be directly applied in ICA feature extraction. In this paper, ...
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By introducing the velocity difference between the preceding car and the car before the preceding one into the optimal velocity model (OVM), we present an extended dynamical model which takes into account the next-nea...
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We propose a new algorithm to find minimal rough set reducts by using Particle Swarm Optimization (PSO). Like Genetic Algorithm, PSO is also a type of evolutionary algorithm. But compared with GA, PSO does not need co...
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