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ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC

作者： Keren Fu Chen Gong Yu Qiao Jie Yang Irene Guy Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai China Chalmers tekniska hogskola Goteborg SE

Recently, tracking is regarded as a binary classification problem by discriminative tracking methods. However, such binary classification may not fully handle the outliers, which may cause drifting. In this paper, we argue that tracking may be regarded as one-class problem, which avoids gathering limited negative samples for background description. Inspired by the fact the positive feature space generated by One-Class SVM is bounded by a closed sphere, we propose a novel tracking method utilizing One-Class SVMs that adopt HOG and 2bit-BP as features, called One-Class SVM Tracker (OCST). Simultaneously an efficient initialization and online updating scheme is also proposed. Extensive experimental results prove that OCST outperforms some state-of-the-art discriminative tracking methods on providing accurate tracking and alleviating serious drifting.

关键词： Target tracking Support vector machines Training Feature extraction Face Boosting Semisupervised learning

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BP Neural Networks with Harmony Search Method-based Training for Epileptic EEG Signal Classification

BP Neural Networks with Harmony Search Method-based Training...

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International Conference on Computational Intelligence and Security

作者： Xiao-Zhi Gao Jing Wang Jarno M.A. Tanskanen Rongfang Bie Ping Guo College of Information Engineering Shanghai Maritime University China laboratory of Image Processing and Pattern Recognition Beijinz Normal University China Tampereen yliopisto - Hervannan kampus Tampere Pirkanmaa FI

In this paper, the Harmony Search (HS)-based BP neural networks are used for the classification of the epileptic electroencephalogram (EEG) signals. It is well known that the gradient descent-based learning method can result in local optima in the training of BP neural networks, which may significantly affect their approximation performances. Two HS methods, the original version and a new variation recently proposed by the authors of the present paper, are applied here to optimize the weights in the BP neural networks for the classification of the epileptic EEG signals. Simulations have demonstrated that the classification accuracy of the BP neural networks can be remarkably improved by the HS method-based training.

关键词： Electroencephalography Optimization Training Biological neural networks Feature extraction Epilepsy

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Classification of gothic and baroque architectural elements

Classification of gothic and baroque architectural elements

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International Conference on Systems, Signals and image processing, IWSSIP

作者： Gayane Shalunts Yll Haxhimusa Robert Sablatnig Institute of Computer Aided Automation Computer Vision Laboratory University of Technology Vienna Austria Institute of Computer Graphics and Algorithms Pattern Recognition and Image Processing Lab University of Technology Vienna Austria

Architectural elements are the components and details of buildings. Their unique set, combination, design, construction technique form the architectural style of buildings. Building facade classification by architectural styles is viewed as a task of classifying separate architectural structural elements. In the scope of building facade architectural style classification the current paper targets the problem of classification of Gothic and Baroque architectural elements called tracery, pediment and balustrade. Since certain gradient directions dominate on the shape of each architectural element, discrimination between dominating gradients means classification of architectural elements and thus architectural styles. We use local features to describe gradient directions. Our approach is based on clustering and learning of local features and yields a high classification rate.

关键词： Shape Three dimensional displays Visualization Computer vision Histograms Windows

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Iterative data adaptive anisotropic image filtering

Iterative data adaptive anisotropic image filtering

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2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012

作者： Yang, Chang-Cai Zheng, Xin-Yi Tian, Jin-Wen Shang, Ke Tian, Xin Hao, Wei Institute for Pattern Recognition and Artificial Intelligence Science and Technology on Multi-Spectral Information Processing Laboratory Huazhong University of Science and Technology Wuhan 430074 China Electrical Engineering and Renewable Energy School China Three Gorges University Yichang 443002 China Institute of Intelligent Vision and Image Information College of Computer and Information Technology China Three Gorges University Yichang 443002 China

ISBN: (纸本)9781467314855

This paper presents a novel data-adaptive anisotropic filtering technique built on top of an iterative scheme. This new technique can preserve the original significant structures while suppressing noises to the largest extent. To achieve this goal, dominant orientation information of all gradients is used to control the local kernel adaptively. This results in elongated and elliptical contours spread along the directions of the local edge structure. With these locally adapted kernels, the smoothing is mostly effective along the edges, rather than across them. Therefore details in the original input can be preserved. The performance of the proposed data-adaptive anisotropic filter together with its iterative scheme are compared with the conventional Gaussian filter, anisotropic diffusion filter, the structure-adaptive anisotropic filter, and the iterative kernel regression scheme. The experimental results on both simulated Gaussian and Poission noisy images indicate that the proposed method performs the best, and the iterative scheme can significantly improve the performance of the noise suppressing while delivering high computation efficiency. © 2012 IEEE.

关键词： Iterative methods

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Epileptic EEG Signal Classification with ANFIS Based on Harmony Search Method

Epileptic EEG Signal Classification with ANFIS Based on Harm...

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International Conference on Computational Intelligence and Security

作者： Jing Wang X.Z. Gao Jarno M.A. Tanskanen Ping Guo Laboratory of Image Processing and Pattern Recognition Beijing Normal University Beijing China Department of Automation and Systems Technology Aalto University School of Electrical Engineering Finland Tampereen yliopisto - Hervannan kampus Tampere Pirkanmaa FI

In this paper, the Adaptive Neuro-Fuzzy Inference System (ANFIS) is used for the classification of the epileptic electroencephalogram (EEG) signals. The ANFIS combines the adaptation capability of the neural networks and the fuzzy logic-based qualitative approach together. A given input/output data set is deployed to construct a fuzzy inference system, whose membership function parameters are trained using a back propagation algorithm in combination with a least squares method. However, the training method sometimes may lead to local optima. We here propose a new strategy of hybrid training algorithm based on the fusion of the ANFIS and Harmony Search (HS), HS-ANFIS, which is adopted to tune all the parameters of the ANFIS. The validity of our method is verified by numerical experiments.

关键词： Electroencephalography Epilepsy Feature extraction Training pattern classification Adaptive systems Expert systems

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Experimental Comparison of Geometric, Arithmetic and Harmonic Means for EEG Event Related Potential Detection

Experimental Comparison of Geometric, Arithmetic and Harmoni...

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International Conference on Computational Intelligence and Security

作者： Jarno M.A. Tanskanen X.Z. Gao Jing Wang Ping Guo Jari A.K. Hyttinen Vassil S. Dimitrov Department of Biomedical Engineering Tampere University of Technology and BioMediTech Finland College of Information Engineering Shanghai Maritime University China Laboratory of Image Processing and Pattern Recognition Beijing Normal University Beijing China ATIPS Laboratory University of Calgary AB Canada

In this paper, we experimentally evaluate three different averaging methods for processing of electroencephalogram (EEG) event related potentials (ERPs) measured from scalp in response to repeated stimulus. In ERP applications, arithmetic mean (AM) is normally employed in processing the ERPs prior to ERP detection, whereas also other averaging methods might have beneficial properties. Fast ERP detection is essential, for example, in brain computer interfaces and during spine surgery. Thus, it is of interest to search for methods to aid in detecting ERPs with as few stimulus repetitions as possible. Here, noise reduction properties of AM, geometric mean (GM), and harmonic mean (HM) are demonstrated with simulations, and ERP processing by the three methods is illustrated by processing real visual evoked potentials (VEPs).

关键词： Electroencephalography Gaussian noise Educational institutions Electrodes Visualization Harmonic analysis

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A hybrid method to segment common carotid arteries from 3D ultrasound images

A hybrid method to segment common carotid arteries from 3D u...

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IEEE EMBS International Conference on Information Technology Applications in Biomedicine (ITAB)

作者： Xin Yang Wanji He Jiaoying Jin Xuming Zhang Ming Yuchi Mingyue Ding State Key Laboratory for Multi-spectral Information Processing Technologies Institute for Pattern Recognition and Artificial Intelligence (IPRAI) Huazhong University of Science and Technology Wuhan Hubei China Image Processing and Intelligence Control Key Laboratory of Education Ministry of China S School of Life Science and Technology Huazhong University of Science and Technology Wuhan Hubei China

A hybrid method is used to evaluate atherosclerosis through a mathematical morphology approach and GVF-Snake method. Common carotid artery (CCA) segmentation requires outlining the intima and adventitia contours on the transverse view of B-mode ultrasound (US) images. The lumen and adventitia contours are segmented using a morphology and GVF-Snake methods, respectively. Upon analyzing ten separate patient data sets demonstrate that a comparison between the proposed method and the traditional approach (manual contouring) on 110 transverse images of the CCA showed a mean absolute distance (MAD) of 0.67±0.17mm for lumen and 0.64 ± 0.19mm for adventitia. Their Dice Similarity Coefficient (DSC) values are 92.7%±2.3% and 90.3%±3.5% for lumen and adventitia segmentation, respectively. These values are in good agreement with clinical standards.

关键词： image segmentation Three dimensional displays Measurement Biomedical imaging Ultrasonic imaging Sun

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Software fault prediction framework based on aiNet algorithm

Software fault prediction framework based on aiNet algorithm

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2011 7th International Conference on Computational Intelligence and Security, CIS 2011

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

ISBN: (纸本)9780769545844

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. © 2011 IEEE.

关键词： Forecasting

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Computation of the folkman number Fv(3,5;6)

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Journal of Combinatorial Mathematics and Combinatorial Computing 2012年 81卷 11-17页

作者： Shao, Zehui Liang, Meilian Pan, Linqiang Xu, Xiaodong School of Information Science and Technology Chengdu University Chengdu 610106 China Key Laboratory of Pattern Recognition and Intelligent Information Processing China School of Mathematics and Information Science Guangxi University Nanning 530004 China Key Laboratory of Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China Guangxi Academy of Sciences Nanning Guangxi 530007 China

We prove that Fv(3,5;6) = 16, which solves the smallest open case of vertex Folkman numbers of the form Fv(3,k;k + 1). The proof uses computer algorithms.

关键词： Mathematical techniques

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Sparse representation for multi-label image annotation

Sparse representation for multi-label image annotation

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2011 7th International Conference on Computational Intelligence and Security, CIS 2011

作者： Xu, Bingxin Guo, Ping Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing 100875 China

ISBN: (纸本)9780769545844

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. © 2011 IEEE.

关键词： image analysis

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