This paper attempts to introduce an improved difference model that modifies a car-following model, which takes the next-nearest-neighbor interaction into account. The hnprovement of this model over the previous one li...
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This paper attempts to introduce an improved difference model that modifies a car-following model, which takes the next-nearest-neighbor interaction into account. The hnprovement of this model over the previous one lies in that it performs more realistically in the dynamical motion for small delay time. The traffic behavior of the improved model is investigated with analytic and numerical methods with the finding that the new consideration could further stabilize traffic flow. And some simulation tests verify that the proposed model can demonstrate some complex physical features observed recently in real traffic such as the existence of three phases： free flow, coexisting flow, and jam flow; spontaneous formation of density waves; sudden flow drop in flow-density plane; traffic hysteresis in transition between the free and the coexisting flow. Furthermore, th.e improved model also predicts that the stable state to relative density in the coexisting flow is insusceptible to noise.
A hierarchical retrieval scheme of the accessory image database is proposed based on textile industrial accessory contour feature and region feature. At first smallest enclosed rectangle[1] feature (degree of accessor...
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A hierarchical retrieval scheme of the accessory image database is proposed based on textile industrial accessory contour feature and region feature. At first smallest enclosed rectangle[1] feature (degree of accessory coordination) is used to filter the image database to decouple the image search scope. After the accessory contour information and region information are extracted, the fusion multi-feature of the centroid distance Fourier descriptor and distance distribution histogram is adopted to finish image retrieval accurately. All the features above are invariable under translation, scaling and rotation. Results from the test on the image database including 1,000 accessory images demonstrate that the method is effective and practical with high accuracy and fast speed.
The fast computation of Zernike moments from normalized geometric moments has been developed in this paper. The computation is multiplication free and only additions are needed to generate Zernike moments. Geometric m...
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The fast computation of Zernike moments from normalized geometric moments has been developed in this paper. The computation is multiplication free and only additions are needed to generate Zernike moments. Geometric moments are generated using Hatamian's filter up to high orders by a very simple and straightforward computation scheme. Other kinds of moments (e.g., Legendre, pseudo Zernike) can be computed using the same algorithm after giving the proper transformations that state their relations to geometric moments. Proper normalizations of geometric moments are necessary so that the method can be used in the efficient computation of Zernike moments. To ensure fair comparisons, recursive algorithms are used to generate Zernike polynomials and other coefficients. The computational complexity model and test programs show that the speed-up factor of the proposed algorithm is superior with respect to other fast and/or direct computations. It perhaps is the first time that Zernike moments can be computed in real time rates, which encourages the use of Zernike momentfeatures in different image retrieval systems that support huge databases such as the XM experimental model stated for the MPEG-7 experimental core. It is concluded that choosing direct computation would be impractical.
A new method for natural color image segmentation using integrated mechanism is proposed in this paper. Edges are first detected in term of the high phase congruency in the gray-level image. K-mean cluster is used to ...
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A new method for natural color image segmentation using integrated mechanism is proposed in this paper. Edges are first detected in term of the high phase congruency in the gray-level image. K-mean cluster is used to label long edge lines based on the global color information to estimate roughly the distribution of objects in the image, while short ones are merged based on their positions and local color differences to eliminate the negative affection caused by texture or other trivial features in image. Region growing technique is employed to achieve final segmentation results. The proposed method unifies edges, whole and local color distributions, as well as spatial information to solve the natural image segmentation problem. The feasibility and effectiveness of this method have been demonstrated by various experiments.
Pace recognition subjected to various conditions is a challenging task. This paper presents a combined feature improved Fisher classifier method for face recognition. Both of the facial holistic information and local ...
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Pace recognition subjected to various conditions is a challenging task. This paper presents a combined feature improved Fisher classifier method for face recognition. Both of the facial holistic information and local information are used for face representation. In addition, the improved linear discriminant analysis (I-LDA) is employed for good generalization capability. Experiments show that the method is not only robust to moderate changes of illumination, pose and facial expression but also superior to the traditional methods, such as eigenfaces and Fisherfaces.
Face recognition has attracted great interest due to its importance in many real-world applications. In this paper,we present a novel low-rank sparse representation-based classification(LRSRC) method for robust face r...
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Face recognition has attracted great interest due to its importance in many real-world applications. In this paper,we present a novel low-rank sparse representation-based classification(LRSRC) method for robust face recognition. Given a set of test samples, LRSRC seeks the lowest-rank and sparsest representation matrix over all training samples. Since low-rank model can reveal the subspace structures of data while sparsity helps to recognize the data class, the obtained test sample representations are both representative and discriminative. Using the representation vector of a test sample, LRSRC classifies the test sample into the class which generates minimal reconstruction error. Experimental results on several face image databases show the effectiveness and robustness of LRSRC in face imagerecognition.
Effects of the speed relaxation time on the optimal velocity car-following model (OVM) with delay time due to driver reaction time proposed by Bando et al.(1995) were studied by numerical methods. Results showed that ...
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Effects of the speed relaxation time on the optimal velocity car-following model (OVM) with delay time due to driver reaction time proposed by Bando et al.(1995) were studied by numerical methods. Results showed that the OVM including the delay is not physically sensitive to the speed relaxation times. A modified car-following model is proposed to overcome the deficiency. Analyses of the linear stability of the modified model were conducted. It is shown that coexisting flows appear if the initial homogeneous headway of the traffic flow is between critical values. In addition, phase transitions occur on varying the initially homogeneous headway.
Given a smooth surface that is z-axis symmetric, illuminated in an arbitrary direction and fully contained within the field of view, it is proven that shape-from-shading is uniquely determined by using a polar coordin...
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Given a smooth surface that is z-axis symmetric, illuminated in an arbitrary direction and fully contained within the field of view, it is proven that shape-from-shading is uniquely determined by using a polar coordinate system and a Fourier expansion.
A new model-based speech enhancement algorithm by variational Bayesian learning was proposed in this paper. Autoregressive process was used to model speech signal and its order was determined automatically. Clean spee...
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A new model-based speech enhancement algorithm by variational Bayesian learning was proposed in this paper. Autoregressive process was used to model speech signal and its order was determined automatically. Clean speech signal could be estimated using a variational Kalman smoother. Moreover, overfitting was avoided in the learning of model parameter and model structure. Experimental results compared with Kalman filter-based enhancement and spectral subtraction methods demonstrate the performance of our algorithm.
An additive discussion for the validity of using the weighted information entropy to evaluate the complex degree of infrared （IR） backgronnds is given, Since small targets can be temporarily lost in actual infrared ...
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An additive discussion for the validity of using the weighted information entropy to evaluate the complex degree of infrared （IR） backgronnds is given, Since small targets can be temporarily lost in actual infrared video sequences, an adaptive binarization threshold for small targets detection is presented. Experimental results show the robustness of our method,
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