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3rd Chinese conference on pattern recognition and Computer Vision, PRCV 2020

3rd Chinese Conference on Pattern Recognition and Computer V...

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3rd Chinese conference on pattern recognition and Computer Vision, PRCV 2020

ISBN: (纸本)9783030606350

The proceedings contain 158 papers. The special focus in this conference is on pattern recognition and Computer Vision. The topics include: Underwater Image Processing by an Adversarial Network with Feedback Control;inception Parallel Attention Network for Small Object Detection in Remote Sensing Images;hyperspectral Image Denoising Based on Graph-Structured Low Rank and Non-local Constraint;multi-human Parsing with Pose and Boundary Guidance;m2E-Net: Multiscale Morphological Enhancement Network for Retinal Vessel Segmentation;DUDA: Deep Unsupervised Domain Adaptation Learning for Multi-sequence Cardiac MR Image Segmentation;learning from Rankings with Multi-level Features for No-Reference Image Quality Assessment;reversible Data Hiding Based on Prediction-Error-Ordering;aggregating Spatio-temporal Context for Video Object Segmentation;Position and Orientation Detection of Insulators in Arbitrary Direction Based on YOLOv3;R-PFN: Towards Precise Object Detection by Recurrent Pyramidal Feature Fusion;image Super-Resolution Based on Non-local Convolutional Neural Network;VH3D-LSFM: Video-Based Human 3D Pose Estimation with Long-Term and Short-Term Pose Fusion Mechanism;automatic Tooth Segmentation and 3D Reconstruction from Panoramic and Lateral Radiographs;unregistered Hyperspectral and Multispectral Image Fusion with Synchronous Nonnegative Matrix Factorization;Cloud Detection Algorithm Using Advanced Fully Convolutional Neural Networks in FY3D-MERSI Imagery;multi-layer Pointpillars: Multi-layer Feature Abstraction for Object Detection from Point Cloud;building Detection via Complementary Convolutional Features of Remote Sensing Images;hyperspectral Image Super-Resolution via Self-projected Smooth Prior;3D Point Cloud Segmentation for Complex Structure Based on PointSIFT;completely Blind Image Quality Assessment with Visual Saliency Modulated Multi-feature Collaboration;blood Flow Velocity Detection of Nailfold Microcirculation Based on Spatiotemporal Analysis;blind

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3rd Chinese conference on pattern recognition and Computer Vision, PRCV 2020

3rd Chinese Conference on Pattern Recognition and Computer V...

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3rd Chinese conference on pattern recognition and Computer Vision, PRCV 2020

ISBN: (纸本)9783030606381

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3rd Chinese conference on pattern recognition and Computer Vision, PRCV 2020

3rd Chinese Conference on Pattern Recognition and Computer V...

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3rd Chinese conference on pattern recognition and Computer Vision, PRCV 2020

ISBN: (纸本)9783030606329

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Applying Social Network Extraction with Named Entity recognition to the Examination of Political Bias within Online News Articles 20

Applying Social Network Extraction with Named Entity Recogni...

引用

3rd international conference on Artificial Intelligence and pattern recognition, AIPR 2020

作者： Lin, Kuan Heng Tsai, Chih Ming Xiamen International School 262 Xingbei San Lu Jimei District Xinglin361022 China Hsing Wu University New Taipei City Taiwan

ISBN: (纸本)9781450375511

We aim to expand the application of social network extraction with NER tools, which to date is largely limited to fiction. With the premise that news articles resemble mini-stories, this study explores the extraction of social networks from online United States news articles to examine relationships between political bias and network features. We find statistical significance with most trends, and find no substantial differences between Liberal and Conservative bias, but bias and neutrality. Furthermore, this study identifies several issues with social network analysis, proposing a more rigorous examination of textual characteristics that affect network features. © 2020 ACM.

关键词： Extraction

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Face recognition based on LBP algorithm 3

Face recognition based on LBP algorithm

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3rd international conference on Computer Network, Electronic and Automation, ICCNEA 2020

作者： Tao, Yang He, Yuanzi Nanchang Institute of Science Technology Nanchang China Guangdong University of Science Technology Dongguan China

ISBN: (纸本)9781728170831

In this paper, face recognition technology is studied and explored, and the classical local binary pattern (LBP) algorithm is studied and improved. An improved LBP algorithm is proposed to solve the deficiency of LBP: a 'double circle' LBP algorithm is proposed, which improves the rotation invariability of LBP to a greater extent and has a good recognition effect on small-pose face images. On the basis of good recognition effect of LBP based on partitioning, the idea of 'multiple partitioning + intermediate partitioning' is put forward, which effectively makes up for the shortage of incomplete extraction of peripheral information of original segmentation line. Facial features are the key features of face recognition. Different organs contribute differently to face image recognition. The idea of 'key block' based on the original block is proposed. Finally, the application of an LBP algorithm for face recognition is taken as an example, and the experimental results verify the reliability of the face recognition method based on LBP for weak light and expression change images. © 2020 IEEE.

关键词： Local binary pattern

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Proceedings of 3rd international conference on Computer Vision and Image Processing 1

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丛书名： Advances in Intelligent Systems and Computing

2020年

作者： Bidyut B. Chaudhuri Masaki Nakagawa Pritee Khanna Sanjeev Kumar

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Tracked-Vehicle Retrieval by Natural Language Descriptions With Domain Adaptive Knowledge

Tracked-Vehicle Retrieval by Natural Language Descriptions W...

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IEEE Computer Society conference on Computer Vision and pattern recognition Workshops (CVPRW)

作者： Huy Dinh-Anh Le Quang Qui-Vinh Nguyen Vuong Ai Nguyen Thong Duy-Minh Nguyen Nhat Minh Chung Tin-Trung Thá i Synh Viet-Uyen Ha School of Computer Science and Engineering International University Ho Chi Minh City Vietnam Vietnam National University Ho Chi Minh City Vietnam

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

This paper introduces our solution for Track 2 in AI City Challenge 2022. The task is Tracked-Vehicle Retrieval by Natural Language Descriptions with a real-world dataset of various scenarios and cameras. We mainly focus on developing a robust natural language-based vehicle retrieval system to address the domain bias problem due to unseen scenarios and multi-view multi-camera vehicle tracks. Specifically, we apply CLIP [16] to effectively extract both visual and textual representations for contrastive representation learning. Furthermore, for new scenarios in the test set, we pro-pose a novel Domain Adaptive Training method that utilizes information from labeled data and transfers it to the unseen domain by generating pseudo labels. By using this simple and effective strategy, we not only bridge the domain gap between the training set and test set, but also require less computational cost and data compared to previous top performance methods. Finally, we employ a context-sensitive post-processing method to address model’s uncertainty and eliminate the wrong retrieved vehicle track. Taking one step further, we also investigate the impact of different text formats and the number of pseudo labels data for the fine-tuning process. Our proposed method has achieved 3rd place in the AI City Challenge 2022, yielding a competitive performance of 47.73% MRR accuracy on the private test set, which verified the effectiveness of the proposed solution.

关键词： Training Representation learning Visualization Adaptive systems Uncertainty Computational modeling Urban areas

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Do we need multiple organs for plant identification? 3

Do we need multiple organs for plant identification?

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3rd international conference on Multimedia Analysis and pattern recognition, MAPR 2020

作者： Nhan, Nguyen Thi Thanh Le, Thi-Lan Hai, Vu University of Information and Communication Technology Nguyen University Thai Nguyen Viet Nam School of Electronics and Telecommunications Hanoi University of Science and Technology Hanoi Viet Nam International Research Institute Mica Hanoi University of Science and Technology Hanoi Viet Nam

ISBN: (纸本)9781728165554

Plant identification that aims at determining the name of plant species from images has attracted a lot of research attentions. Previous studies have confirmed that the plant identification based on multiple organs outperforms that of single organ. However, majority of recent works mainly use transformation-based approach and focus on two organs. Inspired from the work [1] that proposed a fusion scheme named robust hybrid fusion method (RHF) for two organs-based plant identification, in this paper, we propose to extend this scheme for more than two organs plant identification. 57 experiments have been conducted on a dataset of 50 species extracted from ImageCLEF 2015 dataset. The experimental results have shown that the more organs used for plant identification are, the better the identification accuracy is. Using all 6 organs obtain 98.8% accuracy at rank-1. © 2020 IEEE.

关键词： Convolutional neural networks

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Radar Intra-pulse Modulation Signal Classification Using CNN Embedding and Relation Network under Small Sample Set

Radar Intra-pulse Modulation Signal Classification Using CNN...

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Science and Technology Innovation (IAECST), international Academic Exchange conference on

作者： Tao Chen Fuyue Liu Key Laboratory of Advanced Marine Communication and Information Technology Ministry of Industry and Information Technology College of Information and Communication Engineering Harbin Engineering University Harbin China

ISBN: (纸本)9781665402682

For the intra-pulse modulation classification of radar signal, traditional deep learning algorithms have poor recognition performance without numerous training samples. Meanwhile, the receiver may intercept few pulse radar signals in the real scenes of electronic reconnaissance. To solve this problem, a structure which is made up of signal pretreatment by Smooth Pseudo Wigner-Ville (SPWVD) analysis algorithm, convolution neural network (CNN) and relation network (RN) is proposed in this study. The experimental results show that its classification accuracy is 94.24% under 20 samples per class training and the signal-to-noise ratio (SNR) is -4dB. Moreover, it can classify the novel types without further updating the network.

关键词： Training Technological innovation Modulation pattern classification Receivers Reconnaissance Radar imaging

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An Enhanced Coarse-to-Fine Framework for the Segmentation of Clinical Target Volume 23rd

An Enhanced Coarse-to-Fine Framework for the Segmentation of...

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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 Qian, Dahong Liu, Weiping Li, Hui Wang, Lisheng Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University Shanghai200240 China School of Biomedical Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Algorithm and Research Shanghai Aitrox Technology Co. Ltd. Shanghai China National Key Laboratory of Science and Technology on Nano/Micro Fabrication Key Laboratory for Thin Film and Micro Fabrication of the Ministry of Education Institute of Micro-Nano Science and Technology Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9783030718268

In radiation therapy, obtaining accurate boundary of the clinical target volume (CTV) is the vital step to decrease the risk of treatment failures. However, it is a time-consuming and laborious task to obtain the delineation by hand. Therefore, an automatic algorithm is urgently needed to realize accurate segmentation. In this paper, we propose an enhanced coarse-to-fine frameworkto automatically fuse the information of CT, T1 and T2 images to get the target region. This framework includes a coarse-segmentation stage to identify the region of interest (ROI) of targets and a fine-segmentation stage to iteratively refine the segmentation. In the coarse-segmentation stage, the F-loss is proposed to keep the high recall rate of the ROI. In the fine segmentation, the ROI of target will be first cropped according to the ROI obtained by coarse-segmentation and be fed into a 3D-Unet to get the initial results. Then, the prediction and medium features will be set as additional information for the next one network to refine the results. When evaluated on the validation dataset of challenge of Anatomical Brain Barriers to Cancer Spread (ABCs), our method won the 3th place in the public leaderboard. © 2021, Springer Nature Switzerland AG.

关键词： Iterative methods

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