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2004 International Conference on Image Processing, ICIP 2004

作者： Tianxu, Zhang Jin, Liu Institute for Pattern Recognition and Artifical Intelligence State Key Laboratory of Image Processing and Intelligent Control HuaZhong University of Science and Technology China Institute for Pattern Recognition and Artifical Intelligence HuaZhong University of Science and Technology China

ISBN: (纸本)0780385543

An important class of radiometric degradations we are faced with often in practice is image blurring. Special attention is paid to the recognition of the blurred image by moment invariant approach. Some important rules of complex moments for the blurred image are presented. Based on these rules, a useful subset of moment invariants is introduced, that are not affected by the blur, rotation, scale, and translation of the images. The experiments have shown that these invariants can be successfully used in recognition of the blurred image. ©2004 IEEE.

关键词： pattern recognition

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BUPt-MCPRL at TRECVID 2019: AcTEV and INS

BUPt-MCPRL at TRECVID 2019: AcTEV and INS

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2019 TREC Video Retrieval Evaluation, TRECVID 2019

作者： Li, Ya Chen, Guanyu Cheng, Xiangqian Chen, Chong Xu, Shaoqiang Li, Xinyu Xiang, Xuanlu Zhao, Yanyun Zhao, Zhicheng Su, Fei Multimedia Communication and Pattern Recognition Lab Beijing Key Laboratory of Network System Network Culture Beijing University of Posts and Telecommunications China

In this paper, we describe BUPT-MCPRL systems and evaluation results for TRECVID 2019 [14]. We join two tasks: activities in extended video and instance search. Activities in Extended Video (ActEV): p_baseline_2: the baseline of our method. In this submission, we use 3D-Faster-RCNN with Focal Loss to generate activity tubes, then we use tracking algorithm to link the tubes and build efficient temporal localization systems to detect events (activity instances). p_baseline_14: add OTW dataset to train 3D-Faster-RCNN. p_baseline_22: split long events. p_baseline_23: fuse similar events. Instance Search (INS): F_M_E_E_BUPT_MCPRL_1: retrieve persons and using pose-based action detection F_M_E_E_BUPT_MCPRL_2: retrieve persons and detect their actions F_P_E_E_BUPT_MCPRL_3: the same method as in F_M_E_E_BUPT_MCPRL_2I_M_E_E_BUPT_MCPRL_4: first retrieve target persons and detect their actions with partial correction manually. Copyright © TRECVID *** rights reserved.

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Applications of artificial intelligence to lipid nanoparticle delivery

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Particuology 2024年第7期90卷 88-97页

作者： Ye Yuan Yuqi Wu Jiabei Cheng Kaiyuan Yang Yilin Xia Hongguang Wu Xiaoyong Pan Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong UniversityKey Laboratory of System Control and Information ProcessingMinistry of Education of ChinaChina Songjiang Research Institute Songjiang District Central HospitalShanghai Jiao Tong University School of MedicineShanghaiChina

Lipid nanoparticles(LNPs)are nanocarriers composed of four lipid components and can be used for gene therapy,protein replacement,and vaccine ***,LNPs also face several challenges,such as toxicity,immune activation,and low delivery *** overcome these challenges,artificial intelligence can be used to optimize the design and formulation of LNPs,as well as to predict their properties and ***,antibody-targeted conjugation can be used to enhance the specificity and selectivity of LNPs by attaching an antibody that recognizes a specific antigen on the cell surface to LNPs.

关键词： Artificial intelligence Lipid nanoparticles Antibody Targeted conjugation

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Misclassification and margin based SVM active learning algorithm for audio event detection

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International Journal of Advancements in Computing Technology 2011年第9期3卷 90-97页

作者： Leng, Yan Liu, Gang Qi, Guanghui Guo, Jun Pattern Recognition and Intelligent System Laboratory Beijing University of Posts and Telecommunications Beijing 100876 China College of Physics and Electronics Shandong Normal University Jinan 250014 China Department of Construction Machinery Shandong Jiaotong University Jinan 250023 China

Audio event detection has become a hot research due to its wide applications in many fields, such as multimedia retrieval etc., the detection needs large amounts of labeled samples to train the audio event models, but in real life, the labeled samples are expensive to obtain, the shortage of such labeled samples is a big obstacle. Active learning is an efficient way to deal with the problem of insufficient labeled samples. The most popular support vector machines active learning is the margin based sampling (MBS), which is to query the sample closest to the current hyperplane, but when the current hyperplane is far away from the true hyperplane, the sample closest to the current hyperplane is not so informative, querying such samples would have a much slower adjustment of the hyperplane. In order to accelerate the adjustment, this paper proposes the misclassification and margin based sampling (MMBS) active learning algorithm. In order to query more informative samples, MMBS selects samples based on misclassified samples' KL divergence in the first few iterations, after that, considering the lower misclassification confidence and the outlier problem, it switches to MBS. Experiments show that compared to MBS and representative sampling (RepS), MMBS can get the highest detection performance under the same human annotation workload.

关键词： Support vector machines

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A Novel Wideband CPW-Fed 5.8GHz RFID Tag Antenna

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电子学报(英文版) 2012年第2期21卷 202-208页

作者： LI Huihui MOU Xuanqin JI Zhen YU Hang LI Yan Institute of Image Processing and Pattern Recognition Xi'an Jiaotong University Xi'an 710049 China College of Computer Science and Software Engineering Shenzhen University Shenzhen 518060 China Shenzhen Key Laboratory of Embedded System Design Shenzhen 518060 China

A novel wideband 5.8GHz CPW-fed an- tenna is presented for Radio frequency identification (RFID) tag. Four U-shaped and four L-shaped branches are used as additional resonators to achieve wideband operation. The proposed antenna was analyzed numeri- cally using the Method of moment (MOM) and the Fi- nite element method (FEM). With the antenna size lim- ited to 30 × 30mm2, the ?10dB bandwidth obtained by MOM is 3.235GHz (5.765～9GHz) and the ?9.5dB band- width obtained by FEM is 2.74GHz (5.32～8.06GHz), cor- responding to 55.7% and 47.2% of the center frequency 5.8GHz respectively. Moreover, the simulated results show that the proposed antenna has gain of more than 4.8dBi and the radiation pattern is nearly omnidirectional in the H-plane. The measured ?10dB bandwidth is 2.68GHz (5.63GHz～8.31GHz), 46.2% of the 5.8GHz frequency. Fur- thermore, there are three measured resonant frequencies at 1.34GHz, 3.23GHz and 5.8GHz with lower than ?10dB return loss respectively. The measurement result achieves a wideband RFID tag antenna performance and is in good agreement with the calculated results.

关键词： RFID标签天线尺寸共面波导宽带无线电频率识别有限元分析网络连接数值分析

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N-LBP BASED VEHICLE MONITORING system

N-LBP BASED VEHICLE MONITORING SYSTEM

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2009 IEEE International Conference on Network Infrastructure and Digital Content(2009年IEEE网络基础设施与数字内容国际会议 IEEE IC-NIDC2009)

作者： Kunyan Lan Jun Guo Wenting Lu Honggang Zhang Laboratory of Pattern Recognition and Intelligent System Beijing University of Posts and Telecommun Laboratory of Pattern Recognition and Intelligent System Beijing University of Posts and Telecommuni

ISBN: (纸本)9781424448999

In recent years, feature based object detection has attracted increasing attention in computer vision research community. However, to our best knowledge, no previous work has focused on utilizing local binary pattern (LBP) for vehicle detection in intelligent Transportation system（ITS） domain. In this paper, we develop a novel traffic monitoring system based on N-LBP algorithm, which is the new LBP texture descriptor proposed. The approach includes three steps: firstly the general critical ingredients (GCI for short) are selected from LBP features through training to indicate vehicles. Then GCI are extracted from region of interest (ROI) in the new image for object detection and identification. Linear Kalman filter is employed for feature based tracking finally. Experimental results demonstrate the superiority of N-LBP feature over basic LBP feature, and performance of the new system is more stable and reliable.

关键词： ITS vehicle detection vehicle tracking N-LBP Kalman filter

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NOVEL ACTIVE LEARNING SAMPLE EVALUATION METHOD BASED ON MULTI-LEVEL CONFUSION NETWORKS

NOVEL ACTIVE LEARNING SAMPLE EVALUATION METHOD BASED ON MULT...

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2010 2nd IEEE International Conference on Network Infrasturcture & Digital Content(2010年第二届IEEE网络基础设施与数字内容国际会议 IC-NIDC 2010)

作者： Wei Chen Jun Guo Gang Liu Pattern Recognition and Intelligent System LaboratoryBeijing University of Posts and Telecommunicati Pattern Recognition and Intelligent System Laboratory Beijing University of Posts and Telecommunicat

Active Learning (AL) is designed to aid the laborintensive process of training acoustic model for speech recognition. In AL, only the most informative training samples are selected for manual annotation. Thus, how to evaluate the unlabeled samples is worth researching. In this paper, we propose a unified framework to generate confusion networks of multiple levels including character, syllable and phone, and present a novel active learning sample evaluation method for Chinese acoustic modeling, posterior probabilities obtained from multi-level confusion networks are respectively adopted to evaluate the unlabeled samples. Our experiments show that compared with the widely used sample evaluation method using word posterior probability obtained from word confusion network, our proposed method can achieve satisfying performances.

关键词： Active learning confusion network acoustic model speech recognition

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A Garbage Classification Method Based on Improved YOLOv5

A Garbage Classification Method Based on Improved YOLOv5

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2022 International Conference on Networks, Communications and Information Technology, CNCIT 2022

作者： Yan, Xiaobo Yang, Yuan Feng, Le Wang, Lin Tan, Mian Guizhou Minzu Unversity Key Laboratory of Pattern Recognition and Intelligent System of Guizhou Province. Guiyang China College of Date Science and Information Engineering. Guizhou Minzu Unversity Guiyang China

ISBN: (数字)9781665452960

ISBN: (纸本)9781665452960

With the development of national economy, people's daily garbage is increasing day by day. Relying on manpower to sort garbage is a heavy workload and low efficiency. In this paper, an automatic garbage classification system model based on computer vision is proposed to solve the above problems. Firstly, the YOLOv5 object detection algorithm is improved, and abandoning small object detection to obtain faster detection speed. Secondly, shortcut the complex network layer in the YOLOv5 algorithm framework to speed up the recognition speed while ensuring the recognition accuracy. Finally, the traditional loss function is used to improve the detection speed of large objects in raspberry pie. We collect various types of garbage data set, trains the model on the improved algorithm and tests it by embedding raspberry pie. The experimental results show that the proposed algorithm framework has the characteristics of fast speed and high precision, which can complete the garbage classification at the source of garbage. © 2022 IEEE.

关键词： Network layers

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An efficient duplicate image detection method based on Affine-SIFT feature

An efficient duplicate image detection method based on Affin...

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2010 3rd IEEE International Conference on Broadband Network and Multimedia Technology, IC-BNMT2010

作者： Cao, Yudong Zhang, Honggang Gao, Yanyan Guo, Jun Pattern Recognition and Intelligent System Laboratory Beijing University of Posts and Telecommunications Beijing China College of Electrical and Information Engineering Liaoning University of Technology Jinzhou China

ISBN: (纸本)9781424467709

In this paper, we present a novel scheme to tackle the task of near-duplicate image detection. Given two input images, the algorithm based on the refined similarity measure can judge rightly whether two input image are duplicate images or not. The two images are represented with local feature (i.e, Affine-SIFT) in bag of features model. The Affine-SIFT can undergo larger affine distortions than Hessian-Affine and MSER (Maximally Stable Extremal Region). The refined similarity measure exploits the spatial information between two images. The algorithm is demonstrated on some image pairs with scale change, viewpoint change, blur, noise and spatial deformation. The experimental results show that proposed algorithm is more effective than other state-of-the-art duplicate Image detection algorithm. © 2010 IEEE.

关键词： Affine-SIFT Duplicate detection Image matching

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Salient object detection via color contrast and color distribution

Salient object detection via color contrast and color distri...

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11th Asian Conference on Computer Vision, ACCV 2012

作者： Fu, Keren Gong, Chen Yang, Jie Zhou, Yue Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Shanghai 200240 China

ISBN: (纸本)9783642373305

In this paper, we take the advantages of color contrast and color distribution to get high quality saliency maps. The overall procedure flow of our unified framework contains superpixel pre-segmentation, color contrast and color distribution computation, combination, final refinement and then object segmentation. During color contrast saliency computation, we combine two color systems and then introduce the using of distribution prior before saliency smoothing. It works to select correct color components. In addition, we propose a novel saliency smoothing procedure that is based on superpixel regions and is realized in color space. This processing step leads to total object being highlighted evenly, contributing to high quality color contrast saliency maps. Finally, a new refinement approach is utilized to eliminate artifacts and recover unconnected parts in the combined saliency maps. In visual comparison, our method produces higher quality saliency maps which stress out the total object meanwhile suppress background clutters. Both qualitative and quantitative experiments show our approach outperforms 8 state-of-the-art methods, achieving the highest precision rate 96% (3% improvement from the current highest), when evaluated via one of the most popular data sets [1]. Excellent content-aware image resizing also can be achieved with our saliency maps. © 2013 Springer-Verlag.

关键词： Superpixels

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