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International Conference on Image, Vision and Computing (ICIVC)

作者： Yuanyun Wang Jun Wang Zhaoming Wu Wei Tian Chengzhi Deng Shengqian Wang Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing Nanchang Institute of Technology Nanchang China

ISBN: (纸本)9781728123264

Robust visual tracking is a challenging task due to factors motion blur, fast motion, partial occlusion and illumination variation. Existing tracking algorithms represent a target candidate by templates or a linear combination of them with some constraints such as sparse coding. While the high computational cost restricts the tracking speed with sparse constraint since many trivial templates are introduced. Due to affecting by complicated appearance variations, the relationship between a target candidate and the corresponding target templates is not linear. Maybe it is nonlinear or complex. In this paper, we present a kernelized target representation model with sparsity constraint for visual tracking. Namely, a target candidate is represented by a nonlinear combination of templates with sparsity constraint. The proposed appearance model have the advantages of sparse coding and kernel method, which is robust to outliers and represents a target candidate in a high feature space. A novel tracker is proposed upon the presented appearance model. Superior experimental results are achieved against state-of-the-art trackers on some challenging sequences.

关键词： Kernel Target representation Visual tracking Appearance model visual tracking jigs(templates) Sparse coding Targets Kernel Constraint candidate species

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SAR image classification method based on Gabor feature and K-NN

SAR image classification method based on Gabor feature and K...

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IET International Radar Conference 2018, IRC 2018

作者： Wang, Zhiru Chen, Liang Shi, Hao Qi, Baogui Wang, Guanqun Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing100081 China Department of Electronics Tsinghua University Beijing100084 China

Synthetic aperture radar (SAR) image target classification is a hot issue in remote-sensing image application. Fast and accurate target classification is important in both military and civilian fields. Consequently, this study proposes a novel SAR image target classification method based on Gabor feature extraction and K-NN classifier. First, the multi-scale Gabor features of SAR image are extracted. Then, a k-nearest neighbour (k-NN) classifier with principle component analysis is trained by the extracted Gabor features. Finally, the classifier is used to realise the multi-types SAR image targets classification. MSTAR database is used to validate the classification ability. Experimental results demonstrate that the proposed method has superior performance in term of efficiency and accuracy. © 2019 Institution of Engineering and technology. All rights reserved.

关键词： Image classification

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Earlier attention? Aspect-aware LSTM for aspect-based sentiment analysis

arXiv

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arXiv 2019年

作者： Xing, Bowen Liao, Lejian Song, Dandan Wang, Jingang Zhang, Fuzhen Wang, Zhongyuan Huang, Heyan Lab of High Volume Language Information Processing & Cloud Computing Beijing Lab of Intelligent Information Technology School of Computer Science & Technology Beijing Institute of Technology Meituan-Dianping Group

Aspect-based sentiment analysis (ABSA) aims to predict fine-grained sentiments of comments with respect to given aspect terms or categories. In previous ABSA methods, the importance of aspect has been realized and verified. Most existing LSTM-based models take aspect into account via the attention mechanism, where the attention weights are calculated after the context is modeled in the form of contextual vectors. However, aspect-related information may be already discarded and aspect-irrelevant information may be retained in classic LSTM cells in the context modeling process, which can be improved to generate more effective context representations. This paper proposes a novel variant of LSTM, termed as aspect-aware LSTM (AA-LSTM), which incorporates aspect information into LSTM cells in the context modeling stage before the attention mechanism. Therefore, our AA-LSTM can dynamically produce aspect-aware contextual representations. We experiment with several representative LSTM-based models by replacing the classic LSTM cells with the AA-LSTM cells. Experimental results on SemEval-2014 Datasets demonstrate the effectiveness of AA-LSTM. Copyright © 2019, The Authors. All rights reserved.

关键词： Sentiment analysis

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A computationally-efficient sandbox algorithm for multifractal analysis of large-scale complex networks with tens of millions of nodes

arXiv

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arXiv 2020年

作者： Ding, Yuemin Liu, Jin-Long Li, Xiaohui Tian, Yu-Chu Yu, Zu-Guo School of Computer Science and Engineering Tianjin University of Technology Tianjin300384 China School of Computer Science Queensland University of Technology BrisbaneQLD4000 Australia Key Laboratory of Intelligent Computing and Information Processing Ministry of Education Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan University Xiangtan Hunan411105 China School of Information Science and Engineering Wuhan University of Science and Technology Wuhan Hubei430081 China

The fractality of complex networks has attracted much attention with extensive investigations over the last 15 years. As a generalization of fractal analysis, multifractal analysis (MFA) is a useful tool to systematically describe the spatial heterogeneity of both theoretical and experimental fractal patterns. One of the widely used methods for fractal analysis is box-covering. It uses the minimum number of covering boxes to calculate the fractal dimension of complex networks, and is known to be NP-hard. More severely, in comparison with fractal analysis algorithms, MFA algorithms have much higher computational complexity. Among various MFA algorithms for complex networks, the sandbox MFA algorithm behaves with the best computational efficiency. However, the existing sandbox algorithm is still computationally expensive. Thus, so far it has only been applied to small-scale complex networks of the size of about tens of thousands of nodes. It becomes challenging to implement the MFA for large-scale networks with tens of millions of nodes. It is also not clear whether or not MFA results can be improved by a largely increased size of a theoretical network. To tackle these challenges, a computationally-efficient sandbox algorithm (CESA) is presented in this paper for MFA of large-scale networks. Distinct from the existing sandbox algorithm that uses the shortest-path distance matrix to obtain the required information for MFA of complex networks, our CESA employs the breadth-first search (BFS) technique to directly search the neighbor nodes of each layer of center nodes, and then to retrieve the required information. Our CESA's input is a sparse data structure derived from the compressed sparse row (CSR) format designed for compressed storage of the adjacency matrix of large-scale network. A theoretical analysis reveals that the CESA reduces the time complexity of the existing sandbox algorithm from cubic to quadratic, and also improves the space complexity from quadratic

关键词： Computational efficiency

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A Vessel-Focused 3D Convolutional Network for Automatic Segmentation and Classification of Coronary Artery Plaques in Cardiac CTA 9th

A Vessel-Focused 3D Convolutional Network for Automatic Segm...

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9th International Workshop on Statistical Atlases and Computational Models of the Heart: Atrial Segmentation and LV Quantification Challenges, STACOM 2018, held in conjunction with Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018

作者： Liu, Jiang Jin, Cheng Feng, Jianjiang Du, Yubo Lu, Jiwen Zhou, Jie Department of Automation Tsinghua University Beijing China State Key Lab of Intelligent Technologies and Systems Tsinghua University Beijing China Beijing National Research Center for Information Science and Technology Beijing China

ISBN: (纸本)9783030120283

The segmentation and classification of atherosclerotic plaque (AP) are of great importance in the diagnosis and treatment of coronary artery disease. Although the constitution of AP can be assessed through a contrast-enhanced coronary computed tomography angiography (CCTA), the interpretation of CCTA scans is time-consuming and tedious for radiologists. Automation of AP segmentation is highly desired for clinical applications and further researches. However, it is difficult due to the extreme unbalance of voxels, similar appearance between some plaques and background tissues, and artefacts. In this paper, we propose a vessel-focused 3D convolutional network for automatic segmentation of AP including three subtypes: calcified plaques (CAP), non-calcified plaques (NCAP) and mixed calcified plaques (MCAP). We first extract the coronary arteries from the CT volumes;then we reform the artery segments into straightened volumes;finally, a 3D vessel-focused convolutional neural network is employed for plaque segmentation. The proposed method is trained and tested on a dataset of multi-phase CCTA volumes of 25 patients. We further investigate the effect of artery straightening through a comparison experiment, in which the network is trained on original CT volumes. Results show that by artery extraction and straightening, the training time is reduced by 40% and the segmentation performance of non-calcified plaques and mixed calcified plaques gains significantly. The proposed method achieves dice scores of 0.83, 0.73 and 0.68 for CAP, NCAP and MCAP respectively on the test set, which shows potential value for clinical application. © 2019, Springer Nature Switzerland AG.

关键词： Computerized tomography

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Target vehicle detection and tracking in surveillance videos based on exponential forgetting and perceptual hash algorithm

Advances in Transportation Studies

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Advances in Transportation Studies 2018年第Special issue期2卷 97-106页

作者： Fan, H.H. Zhu, H.J. School of Computer Engineering Jiangsu University of Technology Changzhou213001 China Key Laboratory of Cloud Computing and Intelligent Information Processing of Changzhou City Changzhou213001 China

In order to obtain the stable background of a traffic surveillance video, the application scenarios, computational complexity, and results of the Gaussian background model were analyzed. In the background modeling process, many artifacts were generated, making it difficult to reconstruct a highly stable background image. In order to solve this problem, we proposed a background model based on exponential forgetting algorithm. This method satisfies the background reconstruction of complex environment, the computation is small and the background result is pure. To further improve the accuracy of the test, shadows of the vehicle were detected to reduce the error caused on vehicle detection. The perceptual hash algorithm was used to track the target vehicle. The proposed algorithm is verified by different video data. The experimental results show that a good vehicle tracking effect is achieved in a variety of weather conditions. © 2018, Gioacchino Onorati Editore. All rights reserved.

关键词： Vehicles

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New blind equalisation for non-constant modulus signals using a segment cost function

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International Journal of Wireless and Mobile Computing 2019年第4期16卷 295-304页

作者： Rao, Wei Xu, Huijun Zhang, Jianqiu Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing Nanchang Institute of Technology Nanchang330099 China School of Information Science and Technology Fudan University Shanghai200433 China

It is well known that for constant modulus (i.e., magnitude) signals the famous constant modulus (CM) blind equalisation algorithm implemented in a fractionally spaced equaliser can present a zero steady-state mean square error (MSE), which means completely eliminating the distortions introduced in transmitting signals through channels. But for non-constant modulus signals it suffers a large steady-state MSE. In order to overcome this defect, a segment cost function according to the CM criterion is suggested. The distinctive feature of the segment cost function is that the equalised signals (of the non-constant modulus signals) are divided into three segments to form a ∩ shape where the ideal signals have a constant modulus instead of non-constant. And then a new blind equalisation algorithm seeks to minimise this segment cost function by applying a stochastic gradient method is proposed. When employing the proposed algorithm to equalise the 4-PAM or 16-QAM non-constant modulus signals, just as using the CM blind equalisation algorithm to equalise the 4-QAM constant modulus signal, a zero steady-state MSE can be obtained, which is derived. Compared to the classical bind equalisation algorithms, such as CMA, MMA, MCMA, or CMA+SDD, the proposed algorithm yields improved performance, especially for higher SNR. © 2019 Inderscience Enterprises Ltd.

关键词： Blind equalization

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Context Aware 3D Fully Convolutional Networks for Coronary Artery Segmentation 9th

Context Aware 3D Fully Convolutional Networks for Coronary A...

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作者： Duan, Yongjie Feng, Jianjiang Lu, Jiwen Zhou, Jie Department of Automation Tsinghua University Beijing China State Key Lab of Intelligent Technologies and Systems Tsinghua University Beijing China Beijing National Research Center for Information Science and Technology Beijing China

ISBN: (纸本)9783030120283

Cardiovascular disease caused by coronary artery disease (CAD) is one of the most common causes of death worldwide. Coronary artery segmentation has attracted increasing attention since it is useful for better visualization and diagnosis. Conventional lumen segmentation methods basically describe vessels by a rough tubular model, thus presenting inferiority on abnormal vascular structures and failing to distinguish exact coronary arteries from vessel-like structures. In this paper, we propose a context aware 3D fully convolutional network (FCN) for vessel enhancement and segmentation in coronary computed tomography angiography (CTA) volumes. Combining the superior capacity of CNN in extracting discriminative features and satisfactory suppression of vessel-like structures by spatial prior knowledge embedded, the proposed approach significantly outperforms conventional Hessian vesselness based approach on a dataset of 50 coronary CTA volumes. © 2019, Springer Nature Switzerland AG.

关键词： Diseases

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Two-Dimensional Borophene:Properties,Fabrication,and Promising Applications

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Research 2020年第1期2020卷 412-434页

作者： Zhongjian Xie Xiangying Meng Xiangnan Li Weiyuan Liang Weichun Huang Keqiang Chen Jianming Chen Chenyang Xing Meng Qiu Bin Zhang Guohui Nie Ni Xie Xiaobing Yan Han Zhang Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province Institute of Microscale Optoelectronics and Otolaryngology Department and Biobank of the First Affiliated HospitalShenzhen Second People’s HospitalHealth Science CenterShenzhen UniversityShenzhen 518060China Shenzhen International Institute for Biomedical Research 518116 ShenzhenGuangdongChina National-Local Joint Engineering Laboratory of New Energy Photovoltaic Devices Key Laboratory of Digital Medical Engineering of Hebei ProvinceCollege of Electron and Information EngineeringHebei UniversityBaoding 071002China Nantong Key Lab of Intelligent and New Energy Materials College of Chemistry and Chemical EngineeringNantong UniversityNantong226019 JiangsuChina Center for Stretchable Electronics and Nanoscale Systems Key Laboratory of Optoelectronic Devices and Systems of Ministry of EducationCollege of Physics and Optoelectronic EngineeringShenzhen UniversityShenzhen 518060China Key Laboratory of Marine Chemistry Theory and Technology(Ocean University of China) Ministry of EducationQingdao 266100China

Monoelemental two-dimensional(2D)materials(Xenes)aroused a tremendous attention in 2D science owing to their unique properties and extensive ***,one emerging and typical Xene,has been regarded as a promising agent for energy,sensor,and biomedical ***,the production of borophene is still a challenge because bulk boron has rather intricate spatial structures and multiple chemical *** this review,we describe its excellent properties including the optical,electronic,metallic,semiconducting,photoacoustic,and photothermal *** fabrication methods of borophene are also presented including the bottom-up fabrication and the top-down *** the end,the challenges of borophene in the latest applications are presented and perspectives are discussed.

关键词： properties boron Bor

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Corrigendum to "Hybridization chain reaction circuit controller: CRISPR/Cas12a conversion amplifier for miRNA-21 sensitive detection" [Talanta 266 (2024) 125130]

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Talanta 2024年 269卷 125395页

作者： keyi Long Gaihua Cao Yue Qiu Nannan Yang Jian Che Mei Yang Changjun Hou Danqun Huo Key Laboratory for Biorheological Science and Technology of Ministry of Education Bioengineering College of Chongqing University Chongqing 400044 PR China. Chongqing University Three Gorges Hospital Chongqing 404000 PR China. Key Laboratory for Biorheological Science and Technology of Ministry of Education Bioengineering College of Chongqing University Chongqing 400044 PR China. Electronic address: yangmei@***. Key Laboratory for Biorheological Science and Technology of Ministry of Education Bioengineering College of Chongqing University Chongqing 400044 PR China Chongqing Key Laboratory of Bio-perception & Intelligent Information Processing School of Microelectronics and Communication Engineering Chongqing University Chongqing 400044 PR China. Electronic address: houcj@***. Key Laboratory for Biorheological Science and Technology of Ministry of Education Bioengineering College of Chongqing University Chongqing 400044 PR China. Electronic address: huodq@***.

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