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Corrigendum to Intrusion detection based on improved density peak clustering for imbalanced data on sensor-cloud systems Journal of Systems Architecture volume 118 (2021) 102212

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Journal of Systems Architecture 2021年 119卷

作者： Ming Yan Yewang Chen Xiaoliang Hu Dongdong Cheng Yi Chen Jixiang Du The College of Computer Science and Technology Huaqiao University Xiamen China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business University Beijing China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Soochow China Xiamen Key Laboratory of Computer Vision and Pattern Recognition Huaqiao University Fujian Key Laboratory of Big Data Intelligence and Security Huaqiao University Xiamen China College of Big Data and Intelligent Engineering Yangtze Normal University Chongqing China

Multi-View Partial (MVP) Point Cloud Challenge 2021 on Completion and Registration: Methods and Results

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arXiv 2021年

作者： Pan, Liang Wu, Tong Cai, Zhongang Liu, Ziwei Yu, Xumin Rao, Yongming Lu, Jiwen Zhou, Jie Xu, Mingye Luo, Xiaoyuan Fu, Kexue Gao, Peng Wang, Manning Wang, Yali Qiao, Yu Zhou, Junsheng Wen, Xin Xiang, Peng Liu, Yu-Shen Han, Zhizhong Yan, Yuanjie An, Junyi Zhu, Lifa Lin, Changwei Liu, Dongrui Li, Xin Gómez-Fernández, Francisco Wang, Qinlong Yang, Yang S-Lab Nanyang Technological University Singapore SenseTime-CUHK Joint Lab The Chinese University of Hong Kong Hong Kong Sensetime Research Shanghai AI Laboratory China Department of Automation Tsinghua University China University of Chinese Academy of Sciences China ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Digital Medical Research Center School of Basic Medical Science Fudan University China School of Software BNRist Tsinghua University China *** Wayne State University State Key Laboratory for Novel Software Technology Nanjing University China DeepGlint Shanghai Jiao Tong University China Sichuan University China Xi'an Jiaotong University China

As real-scanned point clouds are mostly partial due to occlusions and viewpoints, reconstructing complete 3D shapes based on incomplete observations becomes a fundamental problem for computer vision. With a single incomplete point cloud, it becomes the partial point cloud completion problem. Given multiple different observations, 3D reconstruction can be addressed by performing partial-to-partial point cloud registration. Recently, a large-scale Multi-View Partial (MVP) point cloud dataset has been released, which consists of over 100,000 high-quality virtual-scanned partial point clouds. Based on the MVP dataset, this paper reports methods and results in the Multi-View Partial Point Cloud Challenge 2021 on Completion and Registration. In total, 128 participants registered for the competition, and 31 teams made valid submissions. The top-ranked solutions will be analyzed, and then we will discuss future research directions. Copyright © 2021, The Authors. All rights reserved.

关键词： Surface measurement

Group shift pointwise convolution for volumetric medical image segmentation

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arXiv 2021年

作者： He, Junjun Ye, Jin Li, Cheng Song, Diping Chen, Wanli Wang, Shanshan Gu, Lixu Qiao, Yu School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China Shenzhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China Shanghai AI Lab Shanghai China Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China The Chinese University of Hong Kong Hong Kong Peng Cheng Laboratory Guangdong Shenzhen China Pazhou Lab Guangdong Guangzhou China

Recent studies have witnessed the effectiveness of 3D convolutions on segmenting volumetric medical images. Compared with the 2D counterparts, 3D convolutions can capture the spatial context in three dimensions. Nevertheless, models employing 3D convolutions introduce more trainable parameters and are more computationally complex, which may lead easily to model overfitting especially for medical applications with limited available training data. This paper aims to improve the effectiveness and efficiency of 3D convolutions by introducing a novel Group Shift Pointwise Convolution (GSP-Conv). GSP-Conv simplifies 3D convolutions into pointwise ones with 1 × 1 × 1 kernels, which dramatically reduces the number of model parameters and FLOPs (e.g. 27× fewer than 3D convolutions with 3 × 3 × 3 kernels). Naïve pointwise convolutions with limited receptive fields cannot make full use of the spatial image context. To address this problem, we propose a parameter-free operation, Group Shift (GS), which shifts the feature maps along different spatial directions in an elegant way. With GS, pointwise convolutions can access features from different spatial locations, and the limited receptive fields of pointwise convolutions can be compensated. We evaluate the proposed methods on two datasets, PROMISE12 and BraTS18. Results show that our method, with substantially decreased model complexity, achieves comparable or even better performance than models employing 3D convolutions. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolution

REFUGE2 CHALLENGE: A TREASURE TROVE FOR MULTI-DIMENSION ANALYSIS AND EVALUATION IN GLAUCOMA SCREENING

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arXiv 2022年

作者： Fang, Huihui Li, Fei Wu, Junde Fu, Huazhu Sun, Xu Son, Jaemin Yu, Shuang Zhang, Menglu Yuan, Chenglang Bian, Cheng Lei, Baiying Zhao, Benjian Xu, Xinxing Li, Shaohua Fumero, Francisco Sigut, José Almubarak, Haidar Bazi, Yakoub Guo, Yuanhao Zhou, Yating Baid, Ujjwal Innani, Shubham Guo, Tianjiao Yang, Jie Orlando, José Ignacio Bogunović, Hrvoje Zhang, Xiulan Xu, Yanwu The REFUGE2 Challenge Australia State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-Sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Intelligent Healthcare Unit Baidu Inc. Beijing China The Institute of High Performance Computing Agency for Science Technology and Research Singapore Yatiris Group PLADEMA Institute CONICET UNICEN Tandil Argentina Christian Doppler Lab for Artificial Intelligence in Retina Department of Ophthalmology and Optometry Medical University of Vienna Vienna Austria VUNO Inc Seoul Korea Republic of Tencent HealthCare Tencent Shenzhen China Computer Vision Institute College of Computer Science and Software Engineering of Shenzhen University Shenzhen China School of Biomedical Engineering Health Science Center Shenzhen University China Xiaohe Healthcare ByteDance Guangdong Guangzhou510000 China School of Biomedical Engineering Shenzhen University China College of Computer Science & Software Engineering Shenzhen University China Department of Computer Science and Systems Engineering Universidad de La Laguna Spain Saudi Electronic University Saudi Arabia King Saud University Saudi Arabia Institute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China SGGS Institute of Engineering and Technology India Institute of Medical Robotics Shanghai Jiao Tong University China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

With the rapid development of artificial intelligence (AI) in medical image processing, deep learning in color fundus photography (CFP) analysis is also evolving. Although there are some open-source, labeled datasets of CFPs in the ophthalmology community, large-scale datasets for screening only have labels of disease categories, and datasets with annotations of fundus structures are usually small in size. In addition, labeling standards are not uniform across datasets, and there is no clear information on the acquisition device. Here we release a multi-annotation, multi-quality, and multi-device color fundus image dataset for glaucoma analysis on an original challenge-Retinal Fundus Glaucoma Challenge 2nd Edition (REFUGE2). The REFUGE2 dataset contains 2000 color fundus images with annotations of glaucoma classification, optic disc/cup segmentation, as well as fovea localization. Meanwhile, the REFUGE2 challenge sets three sub-tasks of automatic glaucoma diagnosis and fundus structure analysis and provides an online evaluation framework. Based on the characteristics of multi-device and multi-quality data, some methods with strong generalizations are provided in the challenge to make the predictions more robust. This shows that REFUGE2 brings attention to the characteristics of real-world multi-domain data, bridging the gap between scientific research and clinical application. © 2022, CC BY-NC-ND.

关键词： Color

Deep convolution neural networks cascaded improved boosted forest for pedestrian detection

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Journal of computers (Taiwan) 2018年第5期29卷 15-28页

作者： Xu, Zhi-Tong Luo, Yan-Min Liu, Pei-Zhong Du, Yong-Zhao College of Computer Science and Technology Huaqiao University Xiamen361021 China Key Laboratory for Computer Vision and Pattern Recognition of Xiamen City Huaqiao University Xiamen361021 China College of Engineering Huaqiao University Quanzhou362000 China

Due to the resolution of small size pedestrian is relatively low, and the hard negative background is very similar to people, therefore, detecting small size pedestrian or detecting pedestrian from hard negative background still a challenging problem in computer vision. In order to effectively address these problem, we propose a novel deep convolution neural networks, and cascade an improved boosted forest classifier method to detect pedestrian. Firstly, by using selective search method to propose pedestrian candidate boxes with confidence scores for utmost retaining image resolution;then, based on these proposed confidence values, adopting convolution neural network model to extract candidate regions feature maps;finally, we improve the boosted forest classifier and cascade it to classify candidate boxes for achieving efficiently pedestrian detection. Extensive experiments on Caltech and KITTI benchmarks demonstrate the proposed method outperforms the state-of-the-art, achieves promising precision on KITTI and the lowest miss rate of 11.53% on Caltech, outperforming the second best method (CompACT-Deep) by 0.17%. © 2018 computer Society of the Republic of China. All rights reserved.

关键词： Image resolution

CNN-RNN based method for license plate recognition

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CAAI Transactions on Intelligence Technology 2018年第3期3卷 169-175页

作者： Palaiahnakote Shivakumara Dongqi Tang Maryam Asadzadehkaljahi Tong Lu Umapada Pal Mohammad Hossein Anisi Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia National Key Laboratory for Novel Software Technology Nanjing Universiry Nanjing People's Republic of China Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India School of Computer Science and Electronic Engineering University of Essex CO4 3SQ Colchester UK

Achieving good recognition results for License plates is challenging due to multiple adverse factors. For instance, in Malaysia, where private vehicle （e.g., cars） have numbers with dark background, while public vehicle （taxis/cabs） have numbers with white background. To reduce the complexity of the problem, we propose to classify the above two types of images such that one can choose an appropriate method to achieve better results. Therefore, in this work, we explore the combination of Convolutional Neural Networks （CNN） and Recurrent Neural Networks namely, BLSTM （Bi-Directional Long Short Term Memory）, for recognition. The CNN has been used for feature extraction as it has high discriminative ability, at the same time, BLSTM has the ability to extract context information based on the past information. For classification, we propose Dense Cluster based Voting （DCV）, which separates foreground and background for successful classification of private and public. Experimental results on live data given by MIMOS, which is funded by Malaysian Government and the standard dataset UCSD show that the proposed classification outperforms the existing methods. In addition, the recognition results show that the recognition performance improves significantly after classification compared to before classification.

关键词：车牌识别识别率发展现状人工智能

A novel hybrid convolutional neural network for accurate organ segmentation in 3d head and neck CT images

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arXiv 2021年

作者： Chen, Zijie Li, Cheng He, Junjun Ye, Jin Song, Diping Wang, Shanshan Gu, Lixu Qiao, Yu Shenzhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China Shanghai AI Lab Shanghai China Shenzhen Yino Intelligence Techonology Co. Ltd. Guangdong Shenzhen China Co. Ltd. Guangdong Shenzhen China Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China Peng Cheng Laboratory Guangdong Shenzhen China Pazhou Lab Guangdong Guangzhou China

Radiation therapy (RT) is widely employed in the clinic for the treatment of head and neck (HaN) cancers. An essential step of RT planning is the accurate segmentation of various organs-at-risks (OARs) in HaN CT images. Nevertheless, segmenting OARs manually is time-consuming, tedious, and error-prone considering that typical HaN CT images contain tens to hundreds of slices. Automated segmentation algorithms are urgently required. Recently, convolutional neural networks (CNNs) have been extensively investigated on this task. Particularly, 3D CNNs are frequently adopted to process 3D HaN CT images. There are two issues with naïve 3D CNNs. First, the depth resolution of 3D CT images is usually several times lower than the in-plane resolution. Direct employment of 3D CNNs without distinguishing this difference can lead to the extraction of distorted image features and influence the final segmentation performance. Second, a severe class imbalance problem exists, and large organs can be orders of times larger than small organs. It is difficult to simultaneously achieve accurate segmentation for all the organs. To address these issues, we propose a novel hybrid CNN that fuses 2D and 3D convolutions to combat the different spatial resolutions and extract effective edge and semantic features from 3D HaN CT images. To accommodate large and small organs, our final model, named Organ-Net2.5D, consists of only two instead of the classic four downsampling operations, and hybrid dilated convolutions are introduced to maintain the respective field. Experiments on the MICCAI 2015 challenge dataset demonstrate that OrganNet2.5D achieves promising performance compared to state-of-the-art methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolution

Efficient Audio-Visual Speaker recognition via Deep Heterogeneous Feature Fusion 12th

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Efficient Audio-Visual Speaker Recognition via Deep Heteroge...

12th Chinese Conference on Biometric recognition, CCBR 2017

作者： Liu, Yu-Hang Liu, Xin Fan, Wentao Zhong, Bineng Du, Ji-Xiang Department of Computer Science Huaqiao University Xiamen361021 China Xiamen Key Laboratory of Computer Vision and Pattern Recognition Huaqiao University Xiamen361021 China

ISBN: (纸本)9783319699226

Audio-visual speaker recognition (AVSR) has long been an active research area primarily due to its complementary information for reliable access control in biometric system, and it is a challenging problem mainly attributes to its multimodal nature. In this paper, we present an efficient audio-visual speaker recognition approach via deep heterogeneous feature fusion. First, we exploit a dual-branch deep convolutional neural networks (CNN) learning framework to extract and fuse the high-level semantic features of face and audio data. Further, by considering the temporal dependency of audio-visual data, we embed the fused features into a bidirectional Long Short-Term Memory (LSTM) networks to produce the recognition result, though which the speakers acquired under different challenging conditions can be well identified. The experimental results have demonstrated the efficiency of our proposed approach in both audio-visual feature fusion and speaker recognition. © 2017, Springer International Publishing AG.

关键词： Long short-term memory

Human motion correction and representation method from motion camera

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Journal of Engineering 2017年第1期1卷 370-375页

作者： Zhang, Hong-Bo Guo, Feng Zhang, Miaohui Lin, Ying Hsiao, Tsung-Chih Department of Computer Science and Technology Huaqiao University Xiamen China Xiamen Key Laboratory of Computer Vision and Pattern Recognition Huaqiao University Xiamen China School of Information Science and Engineering Xiamen University Xiamen China Institute of Energy Jiangxi Academy of Sciences Jiangxi Province China

Motion estimation is a basic issue for many computer vision tasks, such as human-computer interaction, motion objection detection and intelligent robot. In many practical scenes, the object movement goes with camera motion. Generally, motion descriptors directly based on optical flow are inaccurate and have low discrimination power. To this end, a novel motion correction method is proposed and a novel motion feature descriptor called the motion difference histogram (MDH) for recognising human action is proposed in this study. Motion estimation results are corrected by background motion estimation and MDH encodes the motion difference between the background and the objects. Experimental results on video shot with camera motion show that the proposed motion correction method is effective and the recognition accuracy of MDH is better than that of the state-of-the-art motion descriptor.

关键词： Motion estimation