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arXiv 2018年

作者： Nag, Sauradip Shivakumara, Palaiahnakote Yirui, Wu Pal, Umapada Lu, Tong Kalyani Government Engineering College Kalyani Kolkata India Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia College of Computer and Information Hohai University Nanjing China Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University Nanjing China

Identifying crime for forensic investigating teams when crimes involve people of different nationals is challenging. This paper proposes a new method for ethnicity (nationality) identification based on Cloud of Line Distribution (COLD) features of handwriting components. The proposed method, at first, explores tangent angle for the contour pixels in each row and the mean of intensity values of each row in an image for segmenting text lines. For segmented text lines, we use tangent angle and direction of base lines to remove rule lines in the image. We use polygonal approximation for finding dominant points for contours of edge components. Then the proposed method connects the nearest dominant points of every dominant point, which results in line segments of dominant point pairs. For each line segment, the proposed method estimates angle and length, which gives a point in polar domain. For all the line segments, the proposed method generates dense points in polar domain, which results in COLD distribution. As character component shapes change, according to nationals, the shape of the distribution changes. This observation is extracted based on distance from pixels of distribution to Principal Axis of the distribution. Then the features are subjected to an SVM classifier for identifying nationals. Experiments are conducted on a complex dataset, which show the proposed method is effective and outperforms the existing method. Copyright © 2018, The Authors. All rights reserved.

关键词： Crime

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-labeling for brick product graphs

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Journal of Combinatorial Optimization 2014年第2期31卷 447-462页

作者： Shao, Zehui Xu, Jin Yeh, Roger K. School of Information Science and Technology Chengdu University Chengdu China Key Laboratory of Pattern Recognition and Intelligent Information Processing Institutions of Higher Education of Sichuan Province Chengdu China School of Electronic Engineering and Computer Science Peking University Beijing China Department of Applied Mathematics Feng Chia University Taichung Taiwan

Let $$G=(V, E)$$ be a graph. Denote $$d_G(u, v)$$ the distance between two vertices $$u$$ and $$v$$ in $$G$$ . An $$L(2, 1)$$ -labeling of $$G$$ is a function $$f: V \rightarrow \{0,1,\cdots \}$$ such that for any two vertices $$u$$ and $$v$$ , $$|f(u)-f(v)| \ge 2$$ if $$d_G(u, v) = 1$$ and $$|f(u)-f(v)| \ge 1$$ if $$d_G(u, v) = 2$$ . The span of $$f$$ is the difference between the largest and the smallest number in $$f(V)$$ . The $$\lambda $$ -number of $$G$$ , denoted $$\lambda (G)$$ , is the minimum span over all $$L(2,1 )$$ -labelings of $$G$$ . In this article, we confirm Conjecture 6.1 stated in X. Li et al. (J Comb Optim 25:716–736, 2013) in the case when (i) $$\ell $$ is even, or (ii) $$\ell \ge 5$$ is odd and $$0 \le r \le 8$$ .

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Investigate indistinguishable points in semantic segmentation of 3D point cloud

arXiv

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arXiv 2021年

作者： Xu, Mingye Zhou, Zhipeng Zhang, Junhao Qiao, Yu ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences University of Chinese Academy of Sciences China Shanghai AI Lab Shanghai China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society

This paper investigates the indistinguishable points (difficult to predict label) in semantic segmentation for large-scale 3D point clouds. The indistinguishable points consist of those located in complex boundary, points with similar local textures but different categories, and points in isolate small hard areas, which largely harm the performance of 3D semantic segmentation. To address this challenge, we propose a novel Indistinguishable Area Focalization Network (IAF-Net), which selects indistinguishable points adaptively by utilizing the hierarchical semantic features and enhance fine-grained features for points especially those indistinguishable points. We also introduce multi-stage loss to improve the feature representation in a progressive way. Moreover, in order to analyze the segmentation performances of indistinguishable areas, we propose a new evaluation metric called Indistinguishable Points Based Metric (IPBM). Our IAF-Net achieves the comparable results with state-of-the-art performance on several popular 3D point cloud datasets e.g. S3DIS and ScanNet, and clearly outperforms other methods on IPBM. Our code will be available at https://***/MingyeXu/IAF-Net. Copyright © 2021, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Combined Trojan Y Chromosome strategy and sterile insect technique to eliminate mosquitoes: Modelling and analysis

arXiv

引用

arXiv 2021年

作者： Lyu, Jingjing Gu, Musong Wang, Sheng College Of Computer Science Chengdu University Chengdu China Key Laboratory of Pattern Recognition and Intelligent Information Processing Institutions of Higher Education of Sichuan Province Chengdu University Chengdu China Information Development and Management Center Chuzhou University Chuzhou China

Sterile insect technique has been successfully applied in the control of agricultural pests, however, it has a limited ability to control mosquitoes. A promising alternative approach is Trojan Y Chromosome strategy, which works by manipulating the sex ratio of a population through the introduction of feminized Y Y supermales that guarantee male offspring. A combined Trojan Y chromosome strategy and sterile insect technique (TYC-SIT) strategy is modeled with ordinary differential equations that allow the kinetics of the female population decline of mosquitoes to be evaluated under identical modeling conditions. The dynamical analysis leads to results on both local and global stabilities of this combined model. Optimal control analysis is also implemented to investigate the optimal mechanisms for extinction of mosquitoes. In particular, the numerical results affirm that the combined TYC-SIT enables near elimination of mosquitoes. The conclusion has great significance for pest controls. © 2021, CC BY.

关键词： Ordinary differential equations

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UMFA: A photorealistic style transfer method based on U-Net and multi-layer feature aggregation

arXiv

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arXiv 2021年

作者： Rao, Dongyu Wu, Xiao-Jun Li, Hui Kittler, Josef Xu, Tianyang Jiangnan University Jiangsu Provincial Engineerinig Laboratory of Pattern Recognition and Computational Intelligence School of Artificial Intelligence and Computer Science Lihu Avenue Wuxi214122 China University of Surrey Centre for Vision Speech and Signal Processing GuildfordGU2 7XH United Kingdom

In this paper, we propose a photorealistic style transfer network to emphasize the natural effect of photo realistic image stylization. In general, distortion of the image content and lacking of details are two typical issues in the style transfer field. To this end, we design a novel framework employing the U-Net structure to maintain the rich spatial clues, with a multi-layer feature aggregation (MFA) method to simultaneously provide the details obtained by the shallow layers in the stylization processing. In particular, an encoder based on the dense block and a decoder form a symmetrical structure of U-Net are jointly staked to realize an effective feature extraction and image reconstruc tion. Besides, a transfer module based on MFA and "adaptive instance normalization" (AdaIN) is inserted in the skip connection positions to achieve the stylization. Accordingly, the stylized image possesses the texture of a real photo and preserves rich content details without introducing any mask or post-processing steps. The experimental results on public datasets demonstrate that our method achieves a more faithful structural similarity with a lower style loss, reflecting the effectiveness and merit of our approach. © 2021, CC0.

关键词： Deep learning

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Boosted local classifiers for visual tracking

Boosted local classifiers for visual tracking

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Weijian Ruan Jun Chen Jinqiao Wang Bo Luo Wenjun Huang Ruimin Hu National Engineering Research Center for Multimedia Software Computer School of Wuhan Univ. China The Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China National Laboratory of Pattern Recognition Chinese Academy of Sciences China Collaborative Innovation Center of Geospatial Technology China

ISBN: (纸本)9781467372596

Most existing discriminative tracking methods model a target object as a whole and train a tracker based on holistic templates, which cannot effectively deal with partial occlusions. Instead, in this paper, by treating the target as a collection of local patches, we propose a novel tracking approach based on boosted local classifiers. Initially, a set of local patches are sampled to train a set of local classifiers, and the weight of each classifier is given based on the estimated error. In addition, the positive examples and negative examples are sampled for model update with two constraints during the tracking process, which helps obtain more negatives for updating the appearance model and improve the updating efficiency. With updating the weights of local classifiers based on the temporal stability, the tracker can effectively handle partial occlusions. Extensive experiments on various challenging image sequences demonstrate the superiority to several state-of-the-art methods.

关键词： Target tracking Training Testing Stability analysis Object tracking Visualization Multimedia communication

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An efficient algorithm and implementation for residue to binary number conversion

An efficient algorithm and implementation for residue to bin...

引用

International Conference on Communications, Circuits and Systems (ICCCAS)

作者： Chengyi Xiong Zhirong Gao Jinwen Tian Key Laboratory of Education Commission for Image Processing and Intelligent Control Institute of Pattern Recognition & Artificial Intelligence Huazhong University of Science and Technology Wuhan China Department of Computer Science Wuhan University of Science & Engineering Wuhan China Key Laboratory of Education Commission for Image Processing Huazhong University of Science & Technology China

The residue number system (RNS) has computational advantages in addition and multiplication compared with weighted number systems, such as the binary number system (BNS), since operations on residue digits are performed independently and these processes can be performed in parallel. Thus they are widely used in digital signal processing etc. Since residue to binary conversion is critical and difficult for the practicality of RNS, in this paper, a novel residue to binary (R/B) conversion algorithm for the restricted moduli set (2/sup n/ -1, 2/sup n/, 2n+1), based on exploring the periodicity of modulo (2/sup n/ /spl plusmn/ 1) operations is presented. A new 2n-bit adder based R/B converter is also proposed. The performance comparison results demonstrate that the new converter is faster and requires less area compared with the others reported in the previous literature.

关键词： Signal processing algorithms Image converters Educational institutions computer science Concurrent computing Digital signal processing Arithmetic Laboratories Image processing Intelligent control

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New COLD Feature Based Handwriting Analysis for Enthnicity/Nationality Identification

New COLD Feature Based Handwriting Analysis for Enthnicity/N...

引用

International Workshop on Frontiers in Handwriting recognition

作者： Sauradip Nag Palaiahnakote Shivakumara Yirui Wu Umapada Pal Tong Lu Kalyani Government Engineering College Kalyani Kolkata India Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia College of Computer and Information Hohai University Nanjing China Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University Nanjing China

Identifying crime for forensic investigating teams when crimes involve people of different nationals is challenging. This paper proposes a new method for ethnicity (nationality) identification based on Cloud of Line Distribution (COLD) features of handwriting components. The proposed method, at first, uses tangent angle of the contour pixels in each row and the mean of intensity values of each row for segmenting text lines. For segmented text lines, we use tangent angle and direction of base lines to remove rule lines in the image. We use polygonal approximation for finding dominant points for contours of edge components. Then the proposed method connects the nearest dominant points of every dominant point, which results in line segments of dominant point pairs. For each line segment, the proposed method estimates angle and length, which gives a point in polar domain. For all the line segments, the proposed method generates dense points in polar domain, which results in COLD distribution. As character component shapes change, according to nationals, the shape of the distribution changes. This observation is extracted based on distance from pixels of distribution to Principal Axis of the distribution. Then the features are subjected to an SVM classifier for identifying nationals. Experiments are conducted on a complex dataset, which show the proposed method is effective and outperforms the existing method.

关键词： Feature extraction Shape Image edge detection Writing Image segmentation Forensics Support vector machines

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Triplet Graph Convolutional Network for Multi-scale Analysis of Functional Connectivity Using Functional MRI 1

引用

1st International Workshop on Graph Learning in Medical Imaging, GLMI 2019 held in conjunction with the 22nd International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2019

作者： Yao, Dongren Liu, Mingxia Wang, Mingliang Lian, Chunfeng Wei, Jie Sun, Li Sui, Jing Shen, Dinggang Brainnetome Center & National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Department of Radiology and BRIC University of North Carolina at Chapel Hill Chapel HillNC27599 United States College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing210016 China School of Computer Science Northwestern Polytechnical University Xi’an710072 China National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health Ministry of Health Peking University Beijing100191 China

ISBN: (数字)9783030358174

ISBN: (纸本)9783030358167

Brain functional connectivity (FC) derived from resting-state functional MRI (rs-fMRI) data has become a powerful approach to measure and map brain activity. Using fMRI data, graph convolutional network (GCN) has recently shown its superiority in learning discriminative representations of brain FC networks. However, existing studies typically utilize one specific template to partition the brain into multiple regions-of-interest (ROIs) for constructing FCs, which may limit the analysis to a single spatial scale (i.e., a fixed graph) determined by the template. Also, previous methods usually ignore the underlying high-order (e.g., triplet) association among subjects. To this end, we propose a multi-scale triplet graph convolutional network (MTGCN) for brain functional connectivity analysis with rs-fMRI data. Specifically, we first employ multi-scale templates for coarse-to-fine ROI parcellation to construct multi-scale FCs for each subject. We then develop a triplet GCN (TGCN) model to learn multi-scale graph representations of brain FC networks, followed by a weighted fusion scheme for classification. Experimental results on 1,218 subjects suggest the efficacy or our method. © 2019, Springer Nature Switzerland AG.

关键词： Brain

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UniFormer: Unifying Convolution and Self-attention for Visual recognition

arXiv

引用

arXiv 2022年

作者： Li, Kunchang Wang, Yali Zhang, Junhao Gao, Peng Song, Guanglu Liu, Yu Li, Hongsheng Qiao, Yu ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China Shanghai Artificial Intelligence Laboratory Shanghai200232 China National University of Singapore Singapore Shanghai Artificial Intelligence Laboratory China SenseTime Research China The Chinese University of Hong Kong Hong Kong

It is a challenging task to learn discriminative representation from images and videos, due to large local redundancy and complex global dependency in these visual data. Convolution neural networks (CNNs) and vision transformers (ViTs) have been two dominant frameworks in the past few years. Though CNNs can efficiently decrease local redundancy by convolution within a small neighborhood, the limited receptive field makes it hard to capture global dependency. Alternatively, ViTs can effectively capture long-range dependency via self-attention, while blind similarity comparisons among all the tokens lead to high redundancy. To resolve these problems, we propose a novel Unified transFormer (UniFormer), which can seamlessly integrate the merits of convolution and self-attention in a concise transformer format. Different from the typical transformer blocks, the relation aggregators in our UniFormer block are equipped with local and global token affinity respectively in shallow and deep layers, allowing tackling both redundancy and dependency for efficient and effective representation learning. Finally, we flexibly stack our blocks into a new powerful backbone, and adopt it for various vision tasks from image to video domain, from classification to dense prediction. Without any extra training data, our UniFormer achieves 86.3 top-1 accuracy on ImageNet-1K classification task. With only ImageNet-1K pre-training, it can simply achieve state-of-the-art performance in a broad range of downstream tasks. It obtains 82.9/84.8 top-1 accuracy on Kinetics-400/600, 60.9/71.2 top-1 accuracy on Something-Something V1/V2 video classification tasks, 53.8 box AP and 46.4 mask AP on COCO object detection task, 50.8 mIoU on ADE20K semantic segmentation task, and 77.4 AP on COCO pose estimation task. Moreover, we build an efficient UniFormer with a concise hourglass design of token shrinking and recovering, which achieves 2-4× higher throughput than the recent lightweight models. Code is av

关键词： Convolution

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