2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication (CTISC)最新文献_第4页

Jointly Learning Spectral Sensitivity Functions and Demosaicking via Deep Networks 基于深度网络的光谱灵敏度函数联合学习与去马赛克

2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication (CTISC) Pub Date : 2021-04-01 DOI: 10.1109/CTISC52352.2021.00079

Fan Zhang, Chen-Yan Bai

{"title":"Jointly Learning Spectral Sensitivity Functions and Demosaicking via Deep Networks","authors":"Fan Zhang, Chen-Yan Bai","doi":"10.1109/CTISC52352.2021.00079","DOIUrl":"https://doi.org/10.1109/CTISC52352.2021.00079","url":null,"abstract":"Demosaicking is reconstructing a RGB image from the mosaicked image recorded by a single sensor with a color filter array (CFA), which has achieved great progress via deep learning. Some works on joint CFA design and demosaicking via deep learning have been presented. However, almost all existing approaches focus only on optimizing filter arrangements without considering the used spectral sensitivity functions (SSFs). The commonly used SSFs are to mimic human trichromatic perception, which are not optimal for deep-learning-based de-mosaicking. In this paper, we simultaneously learn the CFA and demosaicking using a deep convolutional neural network, where both the filter arrangement and used SSFs are optimized. By modeling the applying of SSFs as a convolutional layer with physical constraints, we formulate our joint learning approach as an end-to-end autoencoder, which can be trained as the standard convolutional neural networks. Our approach allows the designing of CFA patterns with arbitrary sizes, where the filter arrangements can be predefined or not. We demonstrate the effectiveness and advantages of our approach by comparing with fixed Gaussian functions and various predefined filter arrangements.","PeriodicalId":268378,"journal":{"name":"2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication (CTISC)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130953681","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

Collaborative Representation Based on the Constraint of Spectral Characteristics for Hyperspectral Anomaly Detection 基于光谱特征约束的高光谱异常检测协同表示

2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication (CTISC) Pub Date : 2021-04-01 DOI: 10.1109/CTISC52352.2021.00044

Peiying Shi, Q. Ling, Jing Wu, Zaiping Lin

引用次数: 1

Domain Feature Alignment Based on Complement Foreground for Cross-domain Pedestrian Re-identification 基于互补前景的域特征对齐跨域行人再识别

2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication (CTISC) Pub Date : 2021-04-01 DOI: 10.1109/CTISC52352.2021.00071

Jiajian Huang

{"title":"Domain Feature Alignment Based on Complement Foreground for Cross-domain Pedestrian Re-identification","authors":"Jiajian Huang","doi":"10.1109/CTISC52352.2021.00071","DOIUrl":"https://doi.org/10.1109/CTISC52352.2021.00071","url":null,"abstract":"Although significant progress has been made in supervised pedestrian re-identification (re-id), it is still challenging to extend the re-id model to new scenes due to the huge domain gap. Getting rid of domain-specific features and aligning domain features are the ideas to solve cross-domain problems. However, for the former, the existing schemes are often not thorough enough to get rid of the unique characteristics of the domain, and for the latter, domain feature alignment schemes try to align the background and other information that should not be aligned, so they fail to achieve ideal results. In this paper, we propose domain alignment based on complementary foreground. The scheme strips off background clutter thoroughly with the help of mask, and uses pedestrian attribute information and control factor to reduce the influence of background loss caused by masks. Finally, we measure the distribution difference between the two domains with maximum mean difference, and make the feature distribution farther or closer according to the different feature levels. Experiments on mainstream data show that our scheme is 13.9% higher than the benchmark, and extensive ablation experiments prove the effectiveness of our design.","PeriodicalId":268378,"journal":{"name":"2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication (CTISC)","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125301083","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

LiDAR and Camera Calibration Using Near-Far Dual Targets 使用近远双目标的激光雷达和相机校准

2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication (CTISC) Pub Date : 2021-04-01 DOI: 10.1109/CTISC52352.2021.00075

Jianfei Ge, Chao Liu, Li Lv, Gen Xu, Gang Xu, Ningbo Bu, Jiangjian Xiao

引用次数: 1

Research and Application of the Mapping Process from Excel Sheet to Dynamic Form 从Excel表格到动态表格的映射过程研究与应用

2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication (CTISC) Pub Date : 2021-04-01 DOI: 10.1109/CTISC52352.2021.00045

Y. Hairong, Zhang Nan

引用次数: 0

A Cloud-based Smart Wide-area Measurement System for Power Grid Fault Detection 基于云的电网故障检测智能广域测量系统

2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication (CTISC) Pub Date : 2021-04-01 DOI: 10.1109/CTISC52352.2021.00023

Jingsong Shan, Jianyang Zhao, Chengfu Sun, Chunhua Jin, Zhenxiu Zhang

引用次数: 0

3D refined reconstruction for brushes based on multiple images 基于多幅图像的画笔三维精细重建

2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication (CTISC) Pub Date : 2021-04-01 DOI: 10.1109/CTISC52352.2021.00063

ChengPeng Dong, W. Liang, Pengfei Xu

{"title":"3D refined reconstruction for brushes based on multiple images","authors":"ChengPeng Dong, W. Liang, Pengfei Xu","doi":"10.1109/CTISC52352.2021.00063","DOIUrl":"https://doi.org/10.1109/CTISC52352.2021.00063","url":null,"abstract":"Chinese Calligraphy is one of the most esteemed visual art all over the world, and ink brushes are the traditional writing tool for Chinese calligraphy. We want to combine the traditional culture with the latest computer vision techniques, and an exact 3D model of brushes is a necessary prerequisite. However, traditional ink brushes have elongated and thin bodies, which is tough to create dense point clouds and an exact 3D reconstruction model. In this paper, we represent a new approach to achieve a complete 3D reconstruction model of brushes within an acceptable time. Our approach utilizes special brushes and extracts interesting points by using both SIFT and AKAZE. Through the combination of two feature extraction algorithms, interesting points could be doubled and be more focused on brushes rather than backgrounds. Moreover, we compare computational costs in different parameters in our 3D reconstruction pipeline and summary some experience to accelerate the reconstruction process. We decrease the number of neighbor cameras when creating a depth map, and reduce the number of output points to the extent that naked eyes cannot recognize the differences. Experimental results show that our approach is able to create a complete and consistent 3D reconstruction model within three minutes.","PeriodicalId":268378,"journal":{"name":"2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication (CTISC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129306976","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: 1

WiPass: PIN-free and Device-free User Authentication Leveraging Behavioral Features via WiFi Channel State Information WiPass:通过WiFi通道状态信息利用行为特征的PIN-free和设备free用户认证

2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication (CTISC) Pub Date : 2021-04-01 DOI: 10.1109/CTISC52352.2021.00030

Yu Gu, Xiaoxiao Yu

{"title":"WiPass: PIN-free and Device-free User Authentication Leveraging Behavioral Features via WiFi Channel State Information","authors":"Yu Gu, Xiaoxiao Yu","doi":"10.1109/CTISC52352.2021.00030","DOIUrl":"https://doi.org/10.1109/CTISC52352.2021.00030","url":null,"abstract":"As an essential way of human-computer interaction, user authentication has attracted widespread attention in recent years. Traditional user authentication methods include Personal Identification Numbers (PINs) and biometric technology. However, the PINs are easily leaked to others, and biometric technology requires specialized equipment. Different from traditional user authentication methods, in this paper, we use widely deployed WiFi infrastructure to achieve user authentication, and propose WiPass, which is a PIN-free and device-free user authentication leveraging behavioral features via WiFi Channel State Information (CSI). The key idea is to explore personalized behavioral information captured by WiFi CSI to identify different users. Concretely, we first proposed a data visualization method to visualize CSI data as a set of time-series images to preserve the behavior information of keystrokes. Secondly, all these images jointly input into a Convolutional Neural Network (CNN) for feature learning, and the obtained 256-dimensional deep behavior features are used to learn a linear support vector machine classifier. To demonstrate the effectiveness of WiPass, we built a prototype of WiPass on low-cost commodity WiFi devices and verified its performance in three different real environments. The empirical results show that WiPass achieved an average of 90.5% authentication accuracy, 7.5% false acceptance rate, and 6% false rejection rate for 11 participants in three real environments.","PeriodicalId":268378,"journal":{"name":"2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication (CTISC)","volume":" 33","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120829673","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

An Intelligent Customer Service System for Securities Industry based on Compound Corpus and Text Vector 基于复合语料库和文本向量的证券行业智能客服系统

2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication (CTISC) Pub Date : 2021-04-01 DOI: 10.1109/CTISC52352.2021.00059

Runwei Guan, Xiaohui Zhu, Fei Pan, Yong Yue, Jieming Ma, Jie Zhang

引用次数: 0

Range-Doppler Imaging for Noise Radar via 2D Generalized Orthogonal Matching Pursuit 基于二维广义正交匹配追踪的噪声雷达距离-多普勒成像

2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication (CTISC) Pub Date : 2021-04-01 DOI: 10.1109/CTISC52352.2021.00017

Xingyu Lu, Ke Tan, W. Su, Hong Gu, Hailong Zhang, Chao Bo

引用次数: 1