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An Independent Component Analysis Algorithm through Solving Gradient Equation Combined with Kernel Density Estimation

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Journal of Shanghai Jiaotong university(Science) 2009年第2期14卷 204-209页

作者：薛云峰王宇嘉杨杰 Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Department of Automation Shanghai Jiaotong University

A new algorithm for linear instantaneous independent component analysis is proposed based on maximizing the log-likelihood contrast function which can be changed into a gradient *** iterative method is introduced to solve this equation *** unknown probability density functions as well as their first and second derivatives in the gradient equation are estimated by kernel density *** simulations on artificially generated signals and gray scale natural scene images confirm the efficiency and accuracy of the proposed algorithm.

关键词： independent component analysis blind source separation gradient method kernel density estimation

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A novel fusion-based method for expression-invariant gender classification

A novel fusion-based method for expression-invariant gender ...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Li Lu Pengfei Shi Institute of Image Processing and Pattern Recognition Shanghai JiaoTong University China

In this paper, we propose a novel fusion-based gender classification method that is able to compensate for facial expression even when training samples contain only neutral expression. We perform experimental investigation to evaluate the significance of different facial regions in the task of gender classification. Three most significant regions are used in our fusion-based method. The classification is performed by using support vector machines based on the features extracted using two-dimension principal component analysis. Experiments show that our fusion-based method is able to compensate for facial expressions and obtained the highest correct classification rate of 95.33%.

关键词： Nose Mouth Feature extraction Support vector machines Support vector machine classification Principal component analysis Face detection image processing pattern recognition Performance evaluation

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Incremental sparse saliency detection

Incremental sparse saliency detection

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IEEE International Conference on image processing

作者： Yin Li Yue Zhou Lei Xu Xiaochao Yang Jie Yang Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University China

By the guidance of attention, human visual system is able to locate objects of interest in complex scene. We propose a new visual saliency detection model for both image and video. Inspired by biological vision, saliency is defined locally. Lossy compression is adopted, where the saliency of a location is measured by the Incremental Coding Length(ICL). The ICL is computed by presenting the center patch as the sparsest linear representation of its surroundings. The final saliency map is generated by accumulating the coding length. The model is tested on both images and videos. The results indicate a reliable and robust saliency of our method.

关键词： Humans Visual system Layout Biological system modeling Video compression image coding Length measurement Loss measurement Biology computing Testing

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Multichannel image restoration based on optimization of the structural similarity index

Multichannel image restoration based on optimization of the ...

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43rd Asilomar Conference on Signals, Systems and Computers

作者： Temerinac-Ott, Maja Burkhardt, Hans Department of Pattern Recognition and Image Processing Institute of Computer Science University of Freiburg Georges-Köhler-Allee Geb. 052 79110 Freiburg Germany University of Freiburg Germany

ISBN: (纸本)9781424458271

In this paper a framework for multichannel image restoration based on optimization of the structural similarity (SSIM) index is presented. The SSIM index describes the similarity of images more appropriately for the human visual system than the mean square error (MSE). It has not yet been explored for the multi channel restoration task. The construction of an optimization algorithm is difficult due to the non-linearity of the SSIM measure. The existing solution based on a quasi-convex problem formulation is successfully extended for the multichannel image restoration. The correctness of the algorithm is verified on sample images and it is shown that multi-view information can significantly improve the restoration results. © 2009 IEEE.

关键词： Convex optimization

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Comparison of parametric and nonparametric techniques for non-peak traffic forecasting

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World Academy of Science, Engineering and Technology 2009年 39卷 242-248页

作者： Zhang, Yang Liu, Yuncai Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai 200240 China

Accurately predicting non-peak traffic is crucial to daily traffic for all forecasting models. In the paper, least squares support vector machines (LS-SVMs) are investigated to solve such a practical problem. It is the first time to apply the approach and analyze the forecast performance in the domain. For comparison purpose, two parametric and two non-parametric techniques are selected because of their effectiveness proved in past research. Having good generalization ability and guaranteeing global minima, LS-SVMs perform better than the others. Providing sufficient improvement in stability and robustness reveals that the approach is practically promising. © 2009 ***.

关键词： Forecasting

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A Linear Approach to Camera Calibration Using Concentric Circles

A Linear Approach to Camera Calibration Using Concentric Cir...

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International Conference on Information Technology and Computer Science, ITCS

作者： Wenjuan Ma Yinqiang Zheng Yuncai Liu Image Processing and Pattern Recognition Institute Shanghai Jiaotong University Shanghai China

In this paper, we present a new algorithm for camera calibration using concentric circles, which is a linear approach. Different from previous methods, we take the projective equations of three-dimensional circles, which include the intrinsic parameter matrix of the camera, as the basis of our calibration approach. According to the special structure of the projective equations in algebra, we can get a linear equation system about the intrinsic parameters. After enough equations constructed, the calibration can be easily finished. Our method needs three images of the two concentric circles at least, and all the five intrinsic parameters can be recovered. Experiments using computer simulated data demonstrate the robustness and accuracy of our method.

关键词： Cameras Calibration Equations Transmission line matrix methods Matrix decomposition Algebra Robustness Information technology Computer science image processing

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Missing traffic flow data prediction using least squares support vector machines in urban arterial streets

Missing traffic flow data prediction using least squares sup...

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IEEE Symposium on Computational Intelligence and Data Mining (CIDM)

作者： Yang Zhang Yuncai Liu Institute of Image Processing and Pattern Recognition Shanghai JiaoTong University Shanghai China

Accurate traffic parameters such as traffic flow, travel speeds and occupancies, are crucial to effective management of intelligent transportation systems (ITS). Some traffic data from loop detectors settled in arterial streets are incomplete, and the importance of effectively imputing the missing values emerges. In this paper, a technique called least squares support vector machines (LS-SVMs) is introduced to predict missing traffic flow based on spatio-temporal analysis in urban arterial streets. To the best of our knowledge, it is the first time to apply the rising computational intelligence (CI) technique incorporating with state space approach in missing traffic data imputation. Having good generalization ability and guaranteeing global minima ensure its well performance in the area. A baseline imputation technique, expectation maximization/data augmentation (EM/DA), is selected for comparison because of its proved effectiveness in missing data recovery. Through persuasive comparisons of the techniques, the proposed model is proved to be more applicable and performs better in stability and adaptability, which reveals that it is a promising approach in missing data prediction.

关键词： Least squares methods Support vector machines Intelligent transportation systems Machine intelligence Competitive intelligence Detectors Computational intelligence State-space methods Predictive models Stability

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Robust 3D modeling from silhouette cues

Robust 3D modeling from silhouette cues

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Enliang Zheng Qiang Chen Xiaochao Yang Yuncai Liu Institute of Image Processing and Pattern Recognition Shanghai JiaoTong University Shanghai China

We consider the problem of 3D modeling under the environments where colors of the foreground objects are similar to the background, which poses a difficult problem of foreground and background classification. A purely image-based algorithm is adopted in this paper, with no prior information about the foreground objects. We classify foreground and background by fusing the information at the pixel and region levels to obtain the similarity probability map, followed by a Bayesian sensor fusion framework to infer the space occupancy grid. The estimation of the occupancy allows incremental updating once a new observation is available, and the contribution of each observation can be adjusted according to its reliability. Finally, three parameters in the algorithm are analyzed in detail and experiments show the effectiveness of this method.

关键词： Robustness Bayesian methods image reconstruction Cameras Probability Pixel image processing pattern recognition Color Sensor fusion

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A Unified Framework for Kernelization: The Empirical Kernel Feature Space

A Unified Framework for Kernelization: The Empirical Kernel ...

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Chinese Conference on pattern recognition (CCPR)

作者： Huilin Xiong Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai China

In this paper, we propose to kernelize linear learning machines, e.g., PCA and LDA, in the empirical kernel feature space, a finite-dimensional embedding space, in which the distances of the data in the kernel feature space are preserved. The empirical kernel feature space provides a unified framework for the kernelization of all kinds of linear machines: performing a linear machine in the finite-dimensional empirical feature space, its nonlinear kernel machine is then established in the original input data space. This method is different from the conventional kernel-trick based kernelization, and more importantly, the final nonlinear kernel machines, called empirical kernel machines, are shown to be more efficient in many real-world applications, such as face recognition and facial expression recognition, than the kernel-trick based kernel machines.

关键词： Kernel Face recognition pattern analysis pattern recognition Machine learning Linear discriminant analysis Training data Principal component analysis image processing Support vector machines

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Deformable Surface 3D Tracking Using Linear Programming

Deformable Surface 3D Tracking Using Linear Programming

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International Workshop on Computer Science and Engineering (WCSE)

作者： Chenhao Wang Shuhan Shen Yuncai Liu Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai China

A method for 3D shape reconstruction of deformable surfaces from consecutive frames was presented. In our method, the model of the surface is represented by a triangulated mesh. The constraints for the model, including keypoint correspondences and disallowing large changes of edge orientation between consecutive frames, are formulated as Linear Programming (LP) constraints. Therefore the deformable surface 3D tracking method turns into an LP problem that can be effectively solved. The robustness and efficiency of our approach are validated on synthetic and real data.

关键词： Linear programming Surface reconstruction Shape image reconstruction Cameras Deformable models Computer science image processing pattern recognition Robustness

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