Proceedings of the 3rd International Conference on Vision, Image and Signal Processing最新文献_第7页

Efficient Verification of Control Systems with Neural Network Controllers 神经网络控制器控制系统的有效验证

Proceedings of the 3rd International Conference on Vision, Image and Signal Processing Pub Date : 2019-08-26 DOI: 10.1145/3387168.3387244

Guoqing Yang, Guangyi Qian, Pan Lv, Hong Li

引用次数: 7

Training Encrypted Models with Privacy-preserved Data on Blockchain 在b区块链上训练具有隐私保护数据的加密模型

Proceedings of the 3rd International Conference on Vision, Image and Signal Processing Pub Date : 2019-08-26 DOI: 10.1145/3387168.3387211

Lifeng Liu, Yifan Hu, Jiawei Yu, Fengda Zhang, Gang Huang, Jun Xiao, Chao Wu

引用次数: 3

Using Geomatics Techniques to Produce a Geospatial Database System for Geological Hazards in the Al-Salt Area 利用地理信息技术建立铝盐区地质灾害地理空间数据库系统

Proceedings of the 3rd International Conference on Vision, Image and Signal Processing Pub Date : 2019-08-26 DOI: 10.1145/3387168.3387170

E. Fadda, A. Abulibdeh, Al Balushi

{"title":"Using Geomatics Techniques to Produce a Geospatial Database System for Geological Hazards in the Al-Salt Area","authors":"E. Fadda, A. Abulibdeh, Al Balushi","doi":"10.1145/3387168.3387170","DOIUrl":"https://doi.org/10.1145/3387168.3387170","url":null,"abstract":"In the Al-Salt area of Jordan, natural geological hazard events occur frequently because the area northeast of the Dead Sea is recognized as an active seismic zone. Therefore, using the cutting edge technology represented in this study by the geomatics techniques of remote sensing, GPS and GIS were become necessary and sufficient, that because there are many variables a several features to be mapped and store. This study was focused on faults and landslides because these are the dominant events in the study area. Satellite images, geological maps and topographic maps were used to produce GIS layers describing geological hazard elements. The mapped elements included faults, landslides and many other spatial features including lineaments, drainage patterns, road networks, vegetation, contour lines, slopes, aspects and residential areas. Faults, lineaments and landslide maps were extracted from the published geological map of the Al-Salt area (1:50 000 scale). Other features were extracted either from a topographic map or from satellite images of the study area. Digital image-processing techniques were performed on satellite images to enhance the required spatial features (i.e., faults and landslides). Several techniques were applied to the digital images, including false-color composite band ratio analysis, principal-component analysis and high-pass filtering. A Garmin hand-held GPS (model GPSMAP60CSx) was used to track and to map the city's recently constructed main ring road. The hand-held GPS was also used to locate positions of the faults and landslides in the study area. Finally, the produced GIS layers and their attributes were stored in a spatial GIS database using the Arc GIS software package. These layers could be used for retrieving data on the geological hazard elements and for producing a geological hazards map (or any other thematic map) for the Al-Salt area in Jordan.","PeriodicalId":346739,"journal":{"name":"Proceedings of the 3rd International Conference on Vision, Image and Signal Processing","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128083631","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: 4

Determining Lead-Lag Structure between Sentiment Index and Stock Price Returns 确定情绪指数与股价收益的超前-滞后结构

Proceedings of the 3rd International Conference on Vision, Image and Signal Processing Pub Date : 2019-08-26 DOI: 10.1145/3387168.3389115

Alex Momotov, Xianghua Xie

引用次数: 0

Development of Propulsion Systems for Exploration Missions towards Moon and Jupiter 月球和木星探测任务推进系统的发展

Proceedings of the 3rd International Conference on Vision, Image and Signal Processing Pub Date : 2019-08-26 DOI: 10.1145/3387168.3387225

Timo Krone, M. Abele, M. Riehle

引用次数: 1

Federated Object Detection: Optimizing Object Detection Model with Federated Learning 联邦对象检测:用联邦学习优化对象检测模型

Proceedings of the 3rd International Conference on Vision, Image and Signal Processing Pub Date : 2019-08-26 DOI: 10.1145/3387168.3387181

Peihua Yu, Yunfeng Liu

{"title":"Federated Object Detection: Optimizing Object Detection Model with Federated Learning","authors":"Peihua Yu, Yunfeng Liu","doi":"10.1145/3387168.3387181","DOIUrl":"https://doi.org/10.1145/3387168.3387181","url":null,"abstract":"Object detection with deep learning model has achieved good results in many fields, but in some fields that think highly of data privacy, such as medical care, its applications is greatly limited by data. And Federated Learning allows clients to train a model together, while leaving their data in the local, without sharing with the server or other clients. Using the methods of Federated Learning, such as Federated Averaging(FedAvg), to train models can provide privacy, security benefits. Nonetheless, there is little experiment applying Federated Learning algorithms to train the model with a large number of parameters, such as deep learning object detection model. With non-IID data, the accuracy of object detection model trained by FedAvg reduces significantly, and need more rounds to coverage. In this work, we use Kullback-Leibler divergence(KLD) measure the weights divergence between different model trained with non-IID data. And we propose a useful scheme to improve FedAvg based Abnormal Weights Supression, reducing the influence of the weights divergence caused by non-IID and unbalanced data. As a representative of object detection, we choose Single Shot MultiBox Detector(SSD) as the base model. The results of the experiments show that the Mean Average Precision(mAP) get obvious improvement in Pascal VOC 2007 test dataset.","PeriodicalId":346739,"journal":{"name":"Proceedings of the 3rd International Conference on Vision, Image and Signal Processing","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114173193","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: 23

Low-Dose CT Reconstruction with Non-Local Functionals 非局部功能的低剂量CT重建

Proceedings of the 3rd International Conference on Vision, Image and Signal Processing Pub Date : 2019-08-26 DOI: 10.1145/3387168.3387248

Ryosuke Ueda, H. Kudo

引用次数: 0

Curiosity-Driven Exploration Effectiveness on Various Environments 好奇心驱动的探索在不同环境下的有效性

Proceedings of the 3rd International Conference on Vision, Image and Signal Processing Pub Date : 2019-08-26 DOI: 10.1145/3387168.3387235

K. Charoenpitaks, Y. Limpiyakorn

引用次数: 2

Improved algorithm for Phase Unwrapping with Continuous Submodular Minimization 基于连续次模最小化的相位展开改进算法

Proceedings of the 3rd International Conference on Vision, Image and Signal Processing Pub Date : 2019-08-26 DOI: 10.1145/3387168.3387247

S. Lian, H. Kudo

{"title":"Improved algorithm for Phase Unwrapping with Continuous Submodular Minimization","authors":"S. Lian, H. Kudo","doi":"10.1145/3387168.3387247","DOIUrl":"https://doi.org/10.1145/3387168.3387247","url":null,"abstract":"The phase unwrapping is the process of attempting to reconstruct the true phase from modulo 2π phase values. This procedure requires that we have an important congruent constraint i.e. rewrapping unwrapped image should be identical to the original wrapped image. This constraint condition causes a discrete optimization problem. However, many methods have ignored this constraint condition to solve a continuous minimization problem directly, making it difficult to solve the solution correctly. We recently presented new continuous minimum norm method that is based on the Lovász extension. Our method can reach the optimal solution with the congruent constraint condition. Note that in our work, we have taken the subgradient method for minimization, but it is time consuming. In this paper, we introduce the incremental subgradient method to the minimization procedure, which is faster than the subgradient method. To solve the phase unwrapping, first, we also use the Lovász extension to transform the phase unwrapping problem to equivalent continuous minimization problem which consists of the sum of large numbers of component functions. Then we take the incremental subgradient method to solve the minimization problem in which operate on a single component at each iteration, rather than on the entire cost function. On the other hand, we also introduce new minimal function to deal with high lever noise. Several simulations show, compared with the subgradient method, the new algorithm only takes one quarter of (or less) the numbers of iteration for the convergence.","PeriodicalId":346739,"journal":{"name":"Proceedings of the 3rd International Conference on Vision, Image and Signal Processing","volume":"284 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115423230","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

A Brief Overview of Waveforms for UAV Air-to-Ground Communication Systems 无人机空对地通信系统波形概述

Proceedings of the 3rd International Conference on Vision, Image and Signal Processing Pub Date : 2019-08-26 DOI: 10.1145/3387168.3387203

Chaoxing Yan, Lingang Fu, Xiang Luo, Ming Chen

引用次数: 2