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Alignment, segmentation and 3-D reconstruction of serial sections based on automated algorithm

AIP Conference Proceedings 2012年第1期1504卷 1287-1292页

作者： Weiguo Bian Shaojie Tang Qiong Xu Qin Lian Jin Wang Dichen Li State Key Lab for Manufacturing System Engineering Xi 'an Jiaotong University Xi 'an 710049 P.R.China and The First Affiliated Hospital Medical College Xi 'an Jiaotong University Xi 'an 710049 China School of Automation Xi'an University of Posts and Telecommunications Xi'an 710121 P.R.China and Institute of Image Processing & Pattern Recognition Xi'an Jiaotong University Xi'an 710049 China Institute of Image Processing & Pattern Recognition Xi'an Jiaotong University Xi'an 710049 China State Key Lab for Manufacturing System Engineering Xi 'an Jiaotong University Xi 'an 710049 China The Second Affiliated Hospital Medical College Xi'an Jiaotong University Xi'an Shaanxi 710121 China

A well-defined three-dimensional (3-D) reconstruction of bone-cartilage transitional structures is crucial for the osteochondral restoration. This paper presents an accurate, computationally efficient and fully-automated algorithm for the alignment and segmentation of two-dimensional (2-D) serial to construct the 3-D model of bone-cartilage transitional structures. Entire system includes the following five components: (1) image harvest, (2) image registration, (3) image segmentation, (4) 3-D reconstruction and visualization, and (5) evaluation. A computer program was developed in the environment of Matlab for the automatic alignment and segmentation of serial sections. Automatic alignment algorithm based on the position's cross-correlation of the anatomical characteristic feature points of two sequential sections. A method combining an automatic segmentation and an image threshold processing was applied to capture the regions and structures of interest. SEM micrograph and 3-D model reconstructed directly in digital microscope were used to evaluate the reliability and accuracy of this strategy. The morphology of 3-D model constructed by serial sections is consistent with the results of SEM micrograph and 3-D model of digital microscope.

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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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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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Oriented Gradient Maps based automatic asymmetric 3D-2D face recognition

Oriented Gradient Maps based automatic asymmetric 3D-2D face...

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IAPR International Conference on Biometrics (ICB)

作者： Di Huang Mohsen Ardabilian Yunhong Wang Liming Chen CNRS Ecole Centrale de Lyon LIRIS UMR5205 Université de Lyon Lyon France Laboratory of Intelligent Recognition and Image Processing Beijing Key Laboratory of Digital Media School of Computer Science and Engineering Beihang University Beijing China

Recently, asymmetric 3D-2D face recognition has been paid increasing attention. It enrolls in textured 3D faces and performs identification using only 2D facial images, therefore it generally achieves a better result than 2D algorithms do, and avoids inconvenience of data acquisition and computation of 3D methods as well. In this paper, a biological vision-based facial representation, namely Oriented Gradient Maps (OGMs), is introduced for such an application. It simulates the response of complex neurons to gradient information within a pre-defined neighborhood, and thus can describe local texture changes of 2D faces and local geometry variations of 3D faces at the same time. Due to its property of being highly distinctive, these OGMs improve accuracies of both matching steps of asymmetric face recognition, i.e. (1) 3D-2D matching using Canonical Correlation Analysis (CCA); (2) 2D-2D matching using LBP histogram based features and Sparse Representation Classifier (SRC). Some comparative experiments are carried out on the complete FRGC v2.0 database, and the achieved results clearly highlight the effectiveness of the biological vision-based facial description and its successful application to asymmetric face recognition.

关键词： Face Face recognition Principal component analysis Three dimensional displays Histograms Neurons Probes

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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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Epileptic EEG signal classification with marching pursuit based on harmony search method

Epileptic EEG signal classification with marching pursuit ba...

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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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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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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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