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IEEE International Conference on Systems, Man and Cybernetics

作者： Ping Guo Jing Wang X. Z. Gao Jarno M. A. Tanskanen Laboratory of Image Processing and Pattern Recognition Beijing Normal University Beijing China School of Foundational Education Peking University Health Science Center China Department of Automation and Systems Technology Aalto University School of Electrical Engineering Finland Department of Biomedical Engineering Tampere University of Technology Finland

In Epilepsy EEG signal classification, the main time-frequency features can be extracted by using sparse representation with marching pursuit (MP) algorithm. However, the computational burden is so heavy that it is almost impossible to apply MP to real time signal processing. To reduce complexity of sparse representation, we propose to adopt harmony search method in searching the best atoms. Because harmony search method can find the best atoms in continuous time-frequency dictionary, the performance of epilepsy EEG signal classification is enhanced. The validity of this method is proved by experimental results.

关键词： Feature extraction Electroencephalography Dictionaries Classification algorithms Training Matching pursuit algorithms Accuracy

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Cost reference particle filter based on adaptive particle swarm optimization in observation uncertainty

Cost reference particle filter based on adaptive particle sw...

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作者： Hu, Zhen-Tao Liu, Xian-Xing Jin, Yong Laboratory of Image Processing and Pattern Recognition Henan University Kaifeng 475001 China

ISBN: (纸本)9789881725592

Aiming at the effective approximation of sampling particle set relative to system state in observation uncertainty, a novel cost reference particle filter based on adaptive particle swarm optimization is proposed. In the new algorithm, the cost function and the risk function are firstly introduced to realize reasonable utilization of the latest observation. In addition, according to the prior modeling information, a new adaptive method is given to solve the selection of limit velocity. And then the movement of particle set towards the region of high weight particle is completed by particle swarm optimization strategy. The algorithm realizes the dynamic combination of the cost reference particle filter and the adaptive particle swarm optimization, and the reliability and stabilize of sampling particle set relative to system state are improved. The theoretical analysis and experimental results show the efficiency of the proposed algorithm. © 2011 Chinese Assoc of Automati.

关键词： Costs

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Maintaining imbalance highly dependent medical data using dirichlet process data generation

Maintaining imbalance highly dependent medical data using di...

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2011 6th International Conference on Digital Information Management, ICDIM 2011

作者： Antaresti, Tieta Fanany, Mohamad Ivan Arymurthy, Aniati Murni Pattern Recognition Image Processing and Content-Based Image Retrieval Laboratory Faculty of ComputerScience Universitas Indonesia Depok 16424 Indonesia

ISBN: (纸本)9781457715389

The existence of imbalanced data between one class and another class is an important issue to be considered in a classification problem. One of the well-known data balancing technique is the artificial oversampling, which increase the size of datasets. In this research, multinomial classification was applied to classify some recorded features obtained from a single ECG (electrocardiograph) sensor. Therefore, a Dirichlet process, a dirichlet distribution of cumulative distribution function of each data partition, was needed to model the distribution of the new generated data by also considering the statistical properties of the previous data. Data balancing process had given the result of 77.21% classification accuracy (CA), and 90.9% area under ROC curve (AUC). © 2011 IEEE.

关键词： Distribution functions

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The influence of hubness on nearest-neighbor methods in object recognition

The influence of hubness on nearest-neighbor methods in obje...

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2011 IEEE 7th International Conference on Intelligent Computer Communication and processing, ICCP 2011

作者： Tomašev, Nenad Brehar, Raluca Mladenić, Dunja Nedevschi, Sergiu Artificial Intelligence Laboratory Jožef Stefan Institute Ljubljana Slovenia Image Processing and Pattern Recognition Group Technical University of Cluj-Napoca Romania

ISBN: (纸本)9781457714788

Object recognition from images is one of the essential problems in automatic image processing. In this paper we focus specifically on nearest neighbor methods, which are widely used in many practical applications, not necessarily related to image data. It has recently come to attention that high dimensional data also exhibit high hubness, which essentially means that some very influential data points appear and these points are referred to as hubs. Unsurprisingly, hubs play a very important role in the nearest neighbor classification. We examine the hubness of various image data sets, under several different feature representations. We also show that it is possible to exploit the observed hubness and improve the recognition accuracy. © 2011 IEEE.

关键词： Object recognition

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A novel probabilistic data association algorithm based on multiple model particle filter

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Journal of Information and Computational Science 2011年第13期8卷 2759-2766页

作者： Hu, Zhentao Liu, Xianxing Jin, Yong Hou, Yandong Laboratory of Image Processing and Pattern Recognition Henan University Kaifeng 475004 China

Aiming at the problem of strong nonlinear and motion model switching of maneuvering target tracking system in clutters environment, a novel probabilistic data association algorithm based on multiple model particle filter is proposed in this paper. Firstly, the evolution of system state model is introduced in the sampling process of particle, and the joint sampling is completed. Secondly, one-step prediction of measurement and the covariance matrix of residual is constructed by the calculating the mean and variance of particle set with model information. Finally, the confirmation principle of echo from the probabilistic data association algorithm is used to realize the reasonable utilization. The theoretical analysis and experimental results show the filter precision is significantly superior to IMMUKF-PDA and close to IMMPF-PDA, and the computational complexity is almost one-half of IMMPF-PDA under the same condition. 1548-7741/Copyright © 2011 Binary Information Press.

关键词： Covariance matrix

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3D multipath planning for UAV based on network graph

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Journal of Systems Engineering and Electronics 2011年第4期22卷 640-646页

作者： Xin Liu Chengping Zhou Mingyue Ding Institute for Pattern Recognition and Artificial Intelligence Key Laboratory of National Defense Science and Technology forMulti-spectral Information Processing Technologies Huazhong University of Science and Technology Wuhan 430074 R R. China School of Life Science and Technology Image Processing and Intelligence Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan 430072 P. R. China

According to the characteristic and the requirement of multipath planning, a new multipath planning method is proposed based on network. This method includes two steps： the construction of network and multipath searching. The construction of network proceeds in three phases： the skeleton extraction of the configuration space, the judgment of the cross points in the skeleton and how to link the cross points to form a network. Multipath searching makes use of the network and iterative penalty method （IPM） to plan multi-paths, and adjusts the planar paths to satisfy the requirement of maneuverability of unmanned aerial vehicle （UAV）. In addition, a new height planning method is proposed to deal with the height planning of 3D route. The proposed algorithm can find multiple paths automatically according to distribution of terrain and threat areas with high efficiency. The height planning can make 3D route following the terrain. The simulation experiment illustrates the feasibility of the proposed method.

关键词： path planning skeleton graph iterative penaltymethod （IPM） network graph.

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An effective document image deblurring algorithm

An effective document image deblurring algorithm

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作者： Chen, Xiaogang He, Xiangjian Yang, Jie Wu, Qiang Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China Centre for Innovation in IT Services and Applications University of Technology Sydney Australia Key Laboratory of System Control and Information Processing Ministry of Education China

ISBN: (纸本)9781457703942

Deblurring camera-based document image is an important task in digital document processing, since it can improve both the accuracy of optical character recognition systems and the visual quality of document images. Traditional deblurring algorithms have been proposed to work for natural-scene images. However the natural-scene images are not consistent with document images. In this paper, the distinct characteristics of document images are investigated. We propose a content-aware prior for document image deblurring. It is based on document image foreground segmentation. Besides, an upper-bound constraint combined with total variation based method is proposed to suppress the rings in the deblurred image. Comparing with the traditional general purpose deblurring methods, the proposed deblurring algorithm can produce more pleasing results on document images. Encouraging experimental results demonstrate the efficacy of the proposed method. © 2011 IEEE.

关键词： image enhancement

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Multi-Level Kernel Machine for Scene image Classification

Multi-Level Kernel Machine for Scene Image Classification

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International Conference on Computational Intelligence and Security

作者： Junlin Hu Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China

ISBN: (纸本)9781457720086

Recently, a new representation for recognizing instances and categories of scenes called spatial Principal component analysis of Census Transform histograms (PACT) has shown its excellent performance in the scene image classification task. PACT captures local structures of an image through the Census Transform (CT), meanwhile, large scale structures are captured by the strong correlation between neighboring CT values and the histogram. However, the original spatial PACT only simply concatenates all levels compact histograms together, and discards the difference between various levels. In order to improve this problem, we propose a multi-level kernel machine method, which computes a set of base kernels at each level of pyramid of PACT, and finds optimal weights for best fusing all these base kernels for scene recognition. Experiments on two popular benchmark datasets demonstrate that our proposed multi-level kernel machine method outperforms the spatial PACT on scene recognition. Besides, our method is easy to be implemented comparing with spatial PACT.

关键词： Kernel Computed tomography Transforms Computer vision Histograms Conferences Support vector machines

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Software Fault Prediction Framework Based on aiNet Algorithm

Software Fault Prediction Framework Based on aiNet Algorithm

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International Conference on Computational Intelligence and Security

作者： Qian Yin Ruiyi Luo Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China

Software fault prediction techniques are helpful in developing dependable software. In this paper, we proposed a novel framework that integrates testing and prediction process for unit testing prediction. Because high fault prone metrical data are much scattered and multi-centers can represent the whole dataset better, we used artificial immune network (aiNet) algorithm to extract and simplify data from the modules that have been tested, then generated multi-centers for each network by Hierarchical Clustering. The proposed framework acquires information along with the testing process timely and adjusts the network generated by aiNet algorithm dynamically. Experimental results show that higher accuracy can be obtained by using the proposed framework.

关键词： Software Prediction algorithms Testing Software algorithms Measurement Clustering algorithms Accuracy

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Sparse Representation for Multi-Label image Annotation

Sparse Representation for Multi-Label Image Annotation

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International Conference on Computational Intelligence and Security

作者： Bingxin Xu Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China

ISBN: (纸本)9781457720086

image annotation is the process of assigning proper keywords to describe the content of a given image, which can be regarded as a problem of multi-object image classification. In this paper, a general multi-label annotation algorithm is proposed, which is based on sparse representation theory and employs a multi-level decision method to deal with the multi-object classification problem. The experimental results show that the proposed algorithm can provide more promising results compared with the traditional classification based image annotation methods.

关键词： Training Vectors Support vector machines Databases Feature extraction Roads Training data

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