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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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Target recognition method based on template matching in downward-looking infrared image

Target recognition method based on template matching in down...

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International Conference on Audio, Language and Image processing, ICALIP

作者： Hu Zhu Lizhen Deng Gang Zhou Xiaodong Bai Gaihua Wang National Key Laboratory of Science and Technology on Multi-spectral Information Processing Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan China College of Electrical & Electronic Engineering Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781467301732

Automatic ground target recognition technology in downward-looking infrared imagery is challenging problems due to the complexity of real-world. A robust ground target recognition method is proposed for downward-looking infrared (DLIR) image sequences in this paper. A complete model of the proposed method is designed firstly and the principles of some key technologies are introduced as following. Perspective transformation is used to solve the projective problems of landmark translation, rotation and scale variance in real-time image sequence. The size of landmark is estimated real-timely by using flight parameters and imaging parameters for getting the model with an appropriate scale. Based on the matching results of landmark and flight parameters, target position technology is proposed to identify the position of target by using the position relation between landmark and target. Experimental results using real-world image data with complicated background showed that the proposed method not only causes high precisely locating results, but also has good robustness for target occlusion.

关键词： Target recognition Satellites Image sequences Real-time systems Robustness Imaging Aircraft

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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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The investigation of metamaterials by measuring the power of transmission in the terahertz regime

The investigation of metamaterials by measuring the power of...

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2012 International Meeting on Opto-Electronics Engineering and Materials Research, OEMR 2012

作者： Luo, Jun Chen, Ying Kang, Shengwu Zhang, Xinyu Xie, Changsheng Zhang, Tianxu Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan Hubei Province 430074 China College of Sciences Huazhong Agricultural University Wuhan Hubei Province 430070 China Hubei Province Fiber Inspection Bureau Wuhan Hubei Province 430060 China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan Hubei Province 430074 China National Key Laboratory of Science and Technology on Multispectral Information Processing Huazhong University of Science and Technology Wuhan Hubei Province 430074 China

ISBN: (纸本)9783037854884

We present the design and fabrication of metamaterials, and investigated the power transmission properties of the metamaterials in the frequency ranging from 1.04THz to 4.25THz. The measured results reveal a global maximum transmission peak at 2.52THz. We compared measured power transmission performances of four types of metamaterials microstructures, and analyzed their transmission differences. © (2012) Trans Tech Publications, Switzerland.

关键词： Metamaterials

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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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Discriminant analysis with Gabor phase for robust face recognition

Discriminant analysis with Gabor phase for robust face recog...

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IAPR International Conference on Biometrics (ICB)

作者： Jianfei Zhu Dong Cao Sifei Liu Zhen Lei Stan Z. Li Institute of Intelligent Information Processing Xidian University Xi' an China Hohai University Nanjing Jiangsu Province China Center for Biometrics and Security Research & National Laboratory of Pattern Recognition Chinese Academy of Sciences Institute of Automation Chinese Academy of Sciences Beijing Beijing CN

This paper presents an occlusion robust image representation method and apply it to face recognition. Inspired from the recent work [15], we propose a Gabor phase difference representation for occlusion robust face recognition. Based on the good ability of Gabor filters to capture image structure and the robustness to image occlusion shown in this paper, Gabor phase features are expected to be discriminative and robust for face representation in occlusion case. Besides, we adopt spectral regression based discriminant analysis with the extracted Gabor phase features to find the most discriminant subspace to classify different faces. In this way, an occlusion robust face image discriminant subspace is derived. Extensive experiments with various occlusion cases show the efficacy of the proposed method.

关键词： Face Robustness Face recognition Databases Kernel Training Principal component analysis

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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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The Investigation of Terahertz Metamaterials by Normalized Power Transmission

The Investigation of Terahertz Metamaterials by Normalized P...

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2012 3rd International Conference on Applied Mechanics and Mechanical Engineering(ICAMME 2012)

作者： Jun Luo Ying Chen Shengwu Kang Xinyu Zhang Changsheng Xie Tianxu Zhang Institute for Pattern Recognition & Artificial Intelligence Huazhong University of Science & Technology College of Sciences Huazhong Agricultural University Hubei Province Fiber Inspection Bureau Wuhan National Laboratory for Optoelectronics Huazhong University of Science & Technology National Key Laboratory of Science & Technology on Multispectral Information Processing Huazhong University of Science & Technology

We present the design and fabrication of metamaterials, and investigated the normalized power transmission properties of four metamaterials at different frequency in terahertz regime. We compared the power transmissio... 详细信息

关键词： Metamaterials Terahertz Normalized Transmission Power

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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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Multi-level kernel machine for scene image classification

Multi-level kernel machine for scene image classification

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

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

ISBN: (纸本)9780769545844

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. © 2011 IEEE.

关键词： Principal component analysis

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