2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC)最新文献_第5页

Facial Expression Recognition Based on Graph Neural Network 基于图神经网络的面部表情识别

2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2020-07-01 DOI: 10.1109/ICIVC50857.2020.9177430

Xuchou Xu, Zhou Ruan, Lei Yang

引用次数: 12

High Temperature Deformation Field Measurement Using 3D Digital Image Correlation Method 三维数字图像相关法测量高温变形场

2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2020-07-01 DOI: 10.1109/ICIVC50857.2020.9177479

Hongtao Deng, D. Jiang, Kai Wang, Q. Fei

引用次数: 1

Color Image Filtering in Bessel-Fourier Moments Domain 贝塞尔-傅里叶矩域彩色图像滤波

2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2020-07-01 DOI: 10.1109/ICIVC50857.2020.9177478

Tianpeng Xia, S. Liao

引用次数: 0

Clone Chaotic Niche Evolutionary Algorithm for Duty Cycle Control Optimization in Wireless Multimedia Sensor Networks 无线多媒体传感器网络占空比控制优化的克隆混沌小生境进化算法

2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2020-07-01 DOI: 10.1109/ICIVC50857.2020.9177435

Jie Zhou, Mengying Xu, Rui Yang

{"title":"Clone Chaotic Niche Evolutionary Algorithm for Duty Cycle Control Optimization in Wireless Multimedia Sensor Networks","authors":"Jie Zhou, Mengying Xu, Rui Yang","doi":"10.1109/ICIVC50857.2020.9177435","DOIUrl":"https://doi.org/10.1109/ICIVC50857.2020.9177435","url":null,"abstract":"One of the most interesting issue regarding to wireless multimedia sensor networks (WMSNs) is to maximizing the network lifetime. Because sensor nodes are constrained in energy, it is very important and necessary to exploit novel duty cycle design algorithms. Such a problem is important in improving network lifetime in WMSNs. The new contribution of our paper is that we propose a clone chaotic niche evolutionary algorithm (CCNEA) for duty cycle design problem in WMSNs. Novel clone operator and chaotic operator have been designed to develop solutions randomly. The strategy merges the merits of clone selection, chaotic generation, and niche operator. CCNEA is a style of swarm algorithm, which has strong global exploit ability. CCNEA utilizes chaotic generation approach which targets to avoid local optima. Then, simulations are performed to verify the robust and efficacy performance of CCNEA compared to methods according to particle swarm optimization (PSO) and quantum genetic algorithm (QGA) under an WMSNs conditions. Simulation experiments denote that the presented CCNEA outperforms PSO and QGA under different conditions, especially for WMSNs that has large number of sensors.","PeriodicalId":6806,"journal":{"name":"2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC)","volume":"15 1","pages":"278-282"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90767734","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

Dual Stream Segmentation Network for Real-Time Semantic Segmentation 实时语义分割的双流分割网络

2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2020-07-01 DOI: 10.1109/ICIVC50857.2020.9177439

Changyuan Zhong, Zelin Hu, Miao Li, Hualong Li, Xuanjiang Yang, Fei Liu

引用次数: 1

A Foreground Mask Network for Cell Counting 一种用于细胞计数的前景掩码网络

2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2020-07-01 DOI: 10.1109/ICIVC50857.2020.9177433

Ni Jiang, Fei-hong Yu

引用次数: 3

A Craik-O'Brien Effect Based Lightness Modification Method Considering Color Distance for Dichromats 基于Craik-O'Brien效应的二色图颜色距离明度修正方法

2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2020-07-01 DOI: 10.1109/ICIVC50857.2020.9177463

Yujun Liu, Shi Bao, Chuanying Yang, Shaoying Ma

{"title":"A Craik-O'Brien Effect Based Lightness Modification Method Considering Color Distance for Dichromats","authors":"Yujun Liu, Shi Bao, Chuanying Yang, Shaoying Ma","doi":"10.1109/ICIVC50857.2020.9177463","DOIUrl":"https://doi.org/10.1109/ICIVC50857.2020.9177463","url":null,"abstract":"Dichromacy, also called as visual impairment, is an inherited disease of defective or abnormal color vision characterized by an inability to recognize certain colors. In this paper, a new lightness modification method based on Craik-O'Brien (C-O) effect was proposed in order to improve the color recognition ability of dichromats. The main idea is to modify the lightness values of the contour parts of the regions which are easy to be confused for dichromats by establishing the objective function of considering color distance, and find the optimal lightness modification value by using the steepest descent method. The modified images will generate C-O effect, which will make the observers produce a visual lightness difference, thus improving the color recognition of the images. The proposed method can retain the information details and overall naturalness of the original color images, making it easier to obtain information and perceive color variations and overall characteristics in the original color images for dichromats. The effectiveness and feasibility of the proposed method is shown in the experimental part by means of the comparison and analysis among the test image, the dichromacy simulation images and the result images obtained by this method.","PeriodicalId":6806,"journal":{"name":"2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC)","volume":"1 1","pages":"86-91"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84663772","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

Design of Face Recognition Attendance 人脸识别考勤系统的设计

2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2020-07-01 DOI: 10.1109/ICIVC50857.2020.9177492

Hai-Wu Lee, Wen-Tan Gu, Yuan-yuan Wang

引用次数: 3

Gain-Pixel Visualization Algorithm Designed for Computational Color Constancy Scheme 基于计算色彩常数方案的增益-像素可视化算法

2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2020-07-01 DOI: 10.1109/ICIVC50857.2020.9177464

S. Teng

引用次数: 0

AI Illustrator: Art Illustration Generation Based on Generative Adversarial Network AI Illustrator:基于生成对抗网络的艺术插图生成

2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2020-07-01 DOI: 10.1109/ICIVC50857.2020.9177494

Zihan Chen, Lianghong Chen, Zhiyuan Zhao, Yue Wang

{"title":"AI Illustrator: Art Illustration Generation Based on Generative Adversarial Network","authors":"Zihan Chen, Lianghong Chen, Zhiyuan Zhao, Yue Wang","doi":"10.1109/ICIVC50857.2020.9177494","DOIUrl":"https://doi.org/10.1109/ICIVC50857.2020.9177494","url":null,"abstract":"In recent years, people's pursuit of art has been on the rise. People want computers to be able to create artistic paintings based on descriptions. In this paper, we proposed a novel project, Painting Creator, which uses deep learning technology to enable the computer to generate artistic illustrations from a short piece of text. Our scheme includes two models, image generation model and style transfer model. In the real image generation model, inspired by the application of stack generative adversarial networks in text to image generation, we proposed an improved model, IStackGAN, to solve the problem of image generation. We added a classifier based on the original model and added image structure loss and feature extraction loss to improve the performance of the generator. The generator network can get additional hidden information from the classification information to produce better pictures. The loss of image structure can force the generator to restore the real image, and the loss of feature extraction can verify whether the generator network has extracted the features of the real image set. For the style transfer model, we improved the generator based on the original cycle generative adversarial networks and used the residual block to improve the stability and performance of the u-net generator. To improve the performance of the generator, we also added the cycle consistent loss with MS-SSIM. The experimental results show that our model is improved significantly based on the original paper, and the generated pictures are more vivid in detail, and pictures after the style transfer are more artistic to watch.","PeriodicalId":6806,"journal":{"name":"2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC)","volume":"69 1","pages":"155-159"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84354177","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: 2