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IEEE Instrumentation and Measurement Technology Conference

作者： Jie Huang Satoshi Ishikawa Michiko Ebana Huakang Li Qunfei Zhao School of computer science and engineering University of Aizu Japan Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University China

In this paper, we proposed a robot self position identification method by active sound localization. This method can be used for autonomous security robots working in room environments. A system using a AIBO robot equipped with two microphones and wireless network is constructed and is used for position identification experiments. Arrival time differences to the microphones of robot are used as localization cues. To overcome the ambiguity of front-back confusion, a three-head position measurement method was proposed. The robot position can be identified by the intersection of circles restricted by the azimuth differences to different speaker pairs. By localizing three or four speakers as sound beacons positioned on known locations, the robot can identify its self position with an average error of about 7 cm in a 2.5 times 3.0 m 2 working space

关键词： Microphones Loudspeakers Humans Head Azimuth Orbital robotics Mobile robots Humanoid robots image processing Robot sensing systems

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Generalized stochastic tube model: Tracking 3D blood vessels in MR images 12

Generalized stochastic tube model: Tracking 3D blood vessels...

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12th IAPR International Conference on pattern recognition - Conference B: pattern recognition and Neural Networks, ICPR 1994

作者： Huang, Qian Stockman, George C. IBM Research Division Almaden Research Center 650 Harry Road San JoseCA95120-6099 United States Pattern Recognition and Image Processing Laboratory Department of Computer Science Michigan State University East LansingMI48824 United States

ISBN: (纸本)0818662700

This paper addresses the issue of tracking tubular objects, particularly blood vessels from MR images. A model-based approach is adopted. The generalized stochastic tube (GST) model is developed which is an extension of our previously proposed Generalized Tube (GT) model. Transitions among adjacent tubes are explicitly parameterized. Integrated with a bivariate Gaussian density function adopted to model the blood flow within cross sections, the GST model is applied to tracking blood vessels in MRA volumetric data. Experimental results on both synthetic data with different degrees of Gaussian noise and real MRA data demonstrated that simultaneously utilizing both models yields robust performance under noisy conditions. © 1994 institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Blood vessels

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Model-based automatic recognition of blood vessels from MR images and its 3D visualization

Model-based automatic recognition of blood vessels from MR i...

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The 1994 1st IEEE International Conference on image processing

作者： Huang, Qian Stockman, G.C. IBM Research Division Almaden Research Center 650 Harry Road San JoseCA95120-6099 United States Pattern Recognition and Image Processing Laboratory Department of Computer Science Michigan State University East LansingMI48824 United States

A model-based approach is used for recognizing arterial blood vessels from MRA volumetric data. The modeling includes (1) a generalized stochastic tube model characterizing the structural properties of the vessels, and (2) a bivariate Gaussian function, modeling the expected cross sectional blood flow. This integrated model renders the recognition problem as a parameter estimation problem which is subsequently solved in a hierarchical fashion. Due to the descriptive representation for objects, a new visualization scheme for blood vessels is proposed that allows the observation of the blood flow of each cross section along a recognized vessel. Some experimental results from both synthetic data and real MRA volumes are given. The visualization of interior blood flow patterns for some vessels are also shown. © 1994 IEEE.

关键词： Blood vessels

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Empirically validating software metrics for risk prediction based on intelligent methods

Journal of Digital Information Management

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Journal of Digital Information Management 2007年第3期5卷 99-106页

作者： Xu, Zhihong Zheng, Xin Guo, Ping Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing 100875 China School of Computer Science Beijing Institute of Technology Beijing 100081 China

The software systems which are related to national science and technology projects are very crucial. This kind of systems always involves high technical factors and has to spend a large amount of money, so the quality and reliability of the software deserve to be further studied. Hence, we propose to apply four intelligent classification techniques most used in data mining fields, including Bayesian belief networks (BBN), nearest neighbor (NN), rough set (RS) and decision tree (DT), to validate the usefulness of software metrics for risk prediction. Results show that comparing with metrics such as Lines of code (LOC) and Cyclomatic complexity (V(G)) which are traditionally used for risk prediction, Halstead program difficulty (D), Number of executable statements (EXEC) and Halstead program volume (V) are the more effective metrics as risk predictors. By analyzing obtained results we also found that BBN was more effective than the other three methods in risk prediction.

关键词： Bayesian networks

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LRTHR-Net: A Low-Resolution-to-High-Resolution Framework to Iteratively Refine the Segmentation of Thyroid Nodule in Ultrasound images 23rd

LRTHR-Net: A Low-Resolution-to-High-Resolution Framework to ...

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Anatomical Brain Barriers to Cancer Spread: Segmentation from CT and MR images Challenge, ABCs 2020, Learn2Reg Challenge, L2R 2020 and Thyroid Nodule Segmentation and Classification in Ultrasound images Challenge, TN-SCUI 2020 held in conjunction with 23rd International Conference on Medical image Computing and computer-Assisted Intervention, MICCAI 2020

作者： Chen, Huai Song, Shaoli Wang, Xiuying Wang, Renzhen Meng, Deyu Wang, Lisheng Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University Shanghai200240 China Department of Nuclear Medicine Fudan University Shanghai Cancer Center Shanghai China School of Computer Science The University of Sydney Sydney Australia School of Mathematics and Statistics Xi’an Jiaotong University Xi’an China

ISBN: (纸本)9783030718268

The thyroid nodule is quickly increasing worldwide and the thyroid ultrasound is the key tool for the diagnosis of it. For the subtle difference between malignant and benign nodules, segmenting lesions is the crucial preliminary step for diagnosis. In this paper, we propose a low-resolution-to-high-resolution segmentation framework for TN-SCUI2020 challenge to alleviate the workload of clinicians and improve the efficiency of diagnosis. Specifically speaking, in order to integrate multi-scale information, several low-resolution segmenting results are obtained firstly and combined with a high-resolution image to refine them and obtain high-resolution results. Secondly, iterative-transfer is proposed to effectively initialize network based on previous trained one on small-scale images. Finally, ensemble refinement is introduced to utilize multiple models to refine the segmentation again. Experimental results showed the effectiveness of the proposed framework. And we won the 2nd place in the segmentation task of TN-SCUI2020. © 2021, Springer Nature Switzerland AG.

关键词： Ultrasonics

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Multi-source remote sensing classification based on Mallat fusion and residual error feature selection

Journal of Digital Information Management

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Journal of Digital Information Management 2007年第3期5卷 130-137页

作者： Cao, Dongdong Yin, Qian Guo, Ping Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing 100875 China School of Computer Science Beijing Institute of Technology Beijing 100081 China

Classification of multi-source remote sensing images has been studied for decades, and many methods have been proposed or improved. Most of these studies focus on how to improve the classifiers in order to obtain higher classification accuracy. However, as we know, even if the most promising method such as neural network, its performance not only depends on the classifier itself, but also has relation with the training pattern (i.e. features). On consideration of this aspect, we propose an approach to feature selection and classification of multi-source remote sensing images based on Mallat fusion and residual error in this paper. Firstly, the fusion of multi-source images can provide a fused image which is more preferable for classification. And then a featureselection scheme approach based on fused image is proposed, which is to select effective subsets of features as inputs of a classifier by taking into account the residual error associated with each land-cover class. In addition, a classification technique base on selected features by using a feed-forward neural network is investigated. The results of computer experiments carried out on a multisource data set confirm the validity of the proposed approach.

关键词： Classification (of information)

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Error analysis and improvements of Spectral Angle Mapper (SAM) model

Error analysis and improvements of Spectral Angle Mapper (SA...

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MIPPR 2005: SAR and Multispectral image processing

作者： Du, Peijun Chen, Yunhao Fang, Tao Tang, Hong Department of RS and GISCi China University of Mining and Technology Xuzhou City Jiangsu Province 221008 China College of Resources Science Beijing Normal University Beijing 100875 Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai 200030 China

Spectral Angle Mapper (SAM) model has got wide applications in hyperspectral Remote Sensing (RS) information processing. But Spectral Angle couldn't achieve satisfied performance in some cases because of its sensitivity to noises and uncertainty. Based on the analysis to traditional SAM algorithm, four types of errors and their impacts to spectral angle are investigated. In order to reduce the impacts of above errors, some improved algorithms are proposed and experimented. The first improved algorithm is grouping spectral angle algorithm. In this new algorithm all bands are divided into two sets by odd and even bands, that means two additional sub-vectors are created in addition to the original spectral vector. So three spectral angles will be computed and the minimum of three indexes is used as final index. The second improved algorithm is normalized spectral angle. In this way spectral angle is computed to the normalized vectors of two original vectors. Two approaches are used to normalize the spectral vector, and spectral angle is computed to the normalized vectors. This algorithm is able to decrease the impacts of random errors. The third algorithm is intersected spectral angle. Spectral angle is calculated by a spectral displacement strategy in this approach. That means a given displacement to change the corresponding bands of two spectral vectors is used and a spectral angle to the displaced vectors will be got. By this displacement strategy the impacts of band offset is reduced. Finally some experiments are used to test those improved algorithms. It proves that those new approaches can reduce and control the errors and improve the precision and reliability of similarity measure.

关键词： Remote sensing

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Patching Method for Coverage Holes with Distance-Assistant in Wireless Sensor Networks

Patching Method for Coverage Holes with Distance-Assistant i...

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2013 International Conference on computer,Networks and Communication Engineering(ICCNCE 2013)

作者： Zhengwei Guo Lin Zhu Department of Computer and Information Engineering Institute of Image Processing and Pattern Recognition Henan University Department of Computer and Information Technology Shangqiu Normal University

ISBN: (纸本)9781629932170

An effective approach for coverage holes was proposed, called Distance-assistant Coverage Hole Parching (DACHP) strategy. DACHP strategy would achieve the purpose of patching coverage holes using redundant sensor nodes within the wireless sensor networks. Simulation results show, compared with DFNFP and random algorithm, for the same size and shape coverage hole, DACHP can awaken five redundant nodes to patch 92.19% of hole, yet DFNFP needs awaken nine redundant nodes and just patch 90.28%. It can be seen that DFNFP not only awaken four extra nodes, but also is lower 1.19% in patching rate of hole than DACHP. In addition, DACHP is more stable in utilization of redundant nodes and does not produce additional cost.

关键词： wireless sensor networks coverage holes patching holes nodes utiliza

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Data Classification on Multiple Manifolds

Data Classification on Multiple Manifolds

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International Conference on pattern recognition

作者： Rui Xiao Qijun Zhao David Zhang Pengfei Shi Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University China Department of Computer Science Hong Kong Polytechnic University Hong Kong China

ISBN: (纸本)9781424475421

Unlike most previous manifold-based data classification algorithms assume that all the data points are on a single manifold, we expect that data from different classes may reside on different manifolds of possible different dimensions. Therefore, better classification accuracy would be achieved by modeling the data by multiple manifolds each corresponding to a class. To this end, a general framework for data classification on multiple manifolds is presented. The manifolds are firstly learned for each class separately, and a stochastic optimization algorithm is then employed to get the near optimal dimensionality of each manifold from the classification viewpoint. Then, classification is performed under a newly defined minimum reconstruction error based classifier. Our method could be easily extended by involving various manifold learning methods and searching strategies. Experiments on both synthetic data and databases of facial expression images show the effectiveness of the proposed multiple manifold based approach.

关键词： Manifolds Classification algorithms Accuracy Databases Training Tuning image reconstruction

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A Services Selection Policy for ServiceBSP Model with QoS-aware in Grids

A Services Selection Policy for ServiceBSP Model with QoS-aw...

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International Conference on Convergence Information Technology

作者： Yan Jiang Weiqing Tong Wentao Zhao School of Computer Engineering and Science Shanghai University China Image Processing and Pattern Recognition Institute Shanghai Jiaotong University Shanghai China

ISBN: (纸本)9781424431779

ServiceBSP model is presented as an extension of BSP model with a view to the advantages of BSP model in grid environment where large-scale and geographically distributed resources (abstracted as services) are available. In this paper, we advocate a preferable service broker framework to support the services selection policy that could optimize the services selection for ServiceBSP model. It means that the services selection can increase the level of satisfaction measured by overall quality value and decrease execution time by considering services dependent relation and location in a subtask group. Meanwhile, the policy is preliminary work ensuring reasonable distribution of services and loading balance in ServiceBSP model. In an addition, we give a detailed process of services selection based on heuristic greedy algorithm. Finally, by comparing response time and price using proposed policy with other policy only considering QoS, the experimental simulation shows that our services selection policy possesses superior performance on response time and then provides satisfactory result for users.

关键词： Quality of service Ontologies Large-scale systems Delay Information technology Grid computing pattern recognition Costs Time measurement Greedy algorithms

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