Proceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition最新文献_第4页

Vision-based 2D Vibration Displacement Measurement of Hoisting Vertical Rope in Mine Hoist 基于视觉的矿井提升垂直钢丝绳二维振动位移测量

Proceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2020-06-26 DOI: 10.1145/3430199.3430221

Ganggang Wu, Xingming Xiao, Chi Ma

引用次数: 1

A Novel Color Multi-Secret Visual Cryptography Scheme 一种新的彩色多秘密视觉密码方案

Proceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2020-06-26 DOI: 10.1145/3430199.3430235

Rui Sun, Zhengxin Fu, Bin Yu, Hangying Huang

引用次数: 1

Deep Hashing Network Based on Split Channels for Hybrid-Source Remote Sensing Image Retrieval 基于分割通道的深度哈希网络混合源遥感图像检索

Proceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2020-06-26 DOI: 10.1145/3430199.3430225

Salayidin Sirajidin, H. Huo, T. Fang

{"title":"Deep Hashing Network Based on Split Channels for Hybrid-Source Remote Sensing Image Retrieval","authors":"Salayidin Sirajidin, H. Huo, T. Fang","doi":"10.1145/3430199.3430225","DOIUrl":"https://doi.org/10.1145/3430199.3430225","url":null,"abstract":"Traditional remote sensing image retrieval (RSIR) methods are generally based on images from a specific single source. As different sources and huge volumes of remote sensing images have been easily available nowadays, RSIR is facing the challenge of retrieving remote sensing images with different spectral and spatial information from different sources. Benefited from compelling image feature extraction ability of deep neural networks and efficient computing power and effective retrieval ability of hashing, deep hashing networks has become prevalent for image retrieval researches. In this paper, a deep hashing network based on split channels is proposed for hybrid source RSIR called split-channels triplet deep hashing networks(SCTDHNs). It takes skillfully splitting channels as input, and is mainly composed of a hybrid source deep hashing subnetwork for cross source images retrieval and single-source deep hashing sub-network for a multi-spectral image retrieval, and each of them achieves high retrieval performance. Furthermore, a novel trick for loss function is proposed, called increased intervals between dissimilar pairs during training stage that dramatically improves the retrieval performance. Extensive experiments implement on dual-source remote sensing data set demonstrate that proposed method yields better performance than existing state-of-art hybrid source retrieval methods as far as is known.","PeriodicalId":371055,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128071862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Novel Method for Extracting Subtle Tremor Signal from Human Body 一种提取人体细微震颤信号的新方法

Proceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2020-06-26 DOI: 10.1145/3430199.3430205

Weiping Liu, Zhiyang Lin, Guannan Chen

{"title":"A Novel Method for Extracting Subtle Tremor Signal from Human Body","authors":"Weiping Liu, Zhiyang Lin, Guannan Chen","doi":"10.1145/3430199.3430205","DOIUrl":"https://doi.org/10.1145/3430199.3430205","url":null,"abstract":"Some common diseases (such as Parkinson's disease, stroke and epilepsy) could cause spontaneous tremors in patients, and doctors could make a preliminary diagnosis based on these tremor in different parts of the patient's body. In order to be more accurate to automatically obtain the tremor signal, we proposed a Novel method for extracting subtle tremor signal from human body. The scope of traditional video tremor extraction usually contained the whole video. In order to extract tremor signals of different body parts of patients separately, we adopted OpenPose to automatically divide different body parts, so as to obtain more detailed video of body parts. Due to some patients' tremor was not obvious, so we used Eulerian video magnification method to amplify the non-obvious tremor and then extracted the tremor signal from the amplified video. To obtain a better tremor signal, we used Butterworth band-pass filter to remove the noise from the initial signal. The experimental results showed that our method can automatically obtain the tremor signal of different body parts of the patient, and the tremor signal was relatively accurate.","PeriodicalId":371055,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133258380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automatic Differentiation Between Legitimate and Fake News Using Named Entity Recognition 使用命名实体识别技术自动区分真假新闻

Proceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2020-06-26 DOI: 10.1145/3430199.3430220

Bo Xu, C. Tsai

引用次数: 1

Dual-Precision Deep Neural Network 双精度深度神经网络

Proceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2020-06-26 DOI: 10.1145/3430199.3430228

J. Park, J. Choi, J. Ko

引用次数: 0

Application Research of Model-Free Reinforcement Learning under the Condition of Conditional Transfer Function with Coupling Factors 耦合因子条件传递函数条件下无模型强化学习的应用研究

Proceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2020-06-26 DOI: 10.1145/3430199.3430210

Xiaoya Yang, Youtian Guo, Rui Wang, Xiaohui Hu

引用次数: 0

Multi-view Learning for 3D LGE-MRI Left Atrial Cavity Segmentation 三维LGE-MRI左房腔分割的多视图学习

Proceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2020-06-26 DOI: 10.1145/3430199.3430203

Jingjing Xiao, Dongyue Si, Yanfang Wu, Meng Li, J. Yin, H. Ding

{"title":"Multi-view Learning for 3D LGE-MRI Left Atrial Cavity Segmentation","authors":"Jingjing Xiao, Dongyue Si, Yanfang Wu, Meng Li, J. Yin, H. Ding","doi":"10.1145/3430199.3430203","DOIUrl":"https://doi.org/10.1145/3430199.3430203","url":null,"abstract":"This paper presents a multi-view learning based method for left atrial cavity segmentation in 3D Late Gadolinium Enhanced Magnetic Resonance Imaging (LGE-MRI). Segmenting left atrium is challenging due to the low intensity contrast, motion artifacts, and extremely thin atrial walls. Since the spatial consistency of the atrium could help to alleviate the segmentation ambiguity caused by those problems, the proposed method consists of three deep convolutional streams which construct 3D segmentation likelihood maps from different views, i.e., axial view, coronal view, and sagittal view. Then, those likelihood maps will be fused and contribute to a final 3D segmentation map, where the method further inspects the 3D connectivity of the labeled pixels and discards the disconnected regions that don't belong to the atrium. The proposed method is tested on a publicly available dataset, where 80 scans are for training and 20 scans are for testing. Compared to the other state-of-the-art algorithms, the proposed method demonstrates a considerable improvement, which shows the advantages of using multi-view information.","PeriodicalId":371055,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114990151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3