Proceedings of the 5th International Conference on Multimedia and Image Processing最新文献

Image recognition based on multi-scale dilated lightweight network model 基于多尺度扩展轻量级网络模型的图像识别

Proceedings of the 5th International Conference on Multimedia and Image Processing Pub Date : 2020-01-10 DOI: 10.1145/3381271.3381300

Yewei Shi, Xiao Yao, Ruixuan Chen, Lili Yuan, Ning Xu, Xiaofeng Liu

引用次数: 1

Defect detection in ID cards with accurately reconstructed reference image 基于精确重构参考图像的身份证缺陷检测

Proceedings of the 5th International Conference on Multimedia and Image Processing Pub Date : 2020-01-10 DOI: 10.1145/3381271.3381298

Xue Chen, Jianwen Cao, Yu-Peng Wang

{"title":"Defect detection in ID cards with accurately reconstructed reference image","authors":"Xue Chen, Jianwen Cao, Yu-Peng Wang","doi":"10.1145/3381271.3381298","DOIUrl":"https://doi.org/10.1145/3381271.3381298","url":null,"abstract":"ID card is made by hot-pressing a standard film with identifiable information onto a fixed baseboard with background of wavy lines. In this paper, we propose a defect detect algorithm by synthesising the film image and baseboard image to accurately reconstruct a reference image for a test card. First, to ensure the content consistency in position and scale, we align the card to a standard film image through perspective transformation(PT) based on AKAZE key-points. Besides, we use contrast limited adaptive histogram equalization(CLAHE) to enhance the background pattern of a baseboard image, and then align it to the rectified card. Second, we apply multiply algorithm to synthesise the aligned film image and baseboard image as a reconstructed reference image. Besides, we align the lightness histogram of the reference image to a test card so as to eliminate the lighting difference. Finally, we apply the difference image method based on canny edge detection to detect difference between a reference image with a card, and further extract the defect information. We experiment on cards with different types of defects and shooting disturbances. Results show high accuracy of our method.","PeriodicalId":124651,"journal":{"name":"Proceedings of the 5th International Conference on Multimedia and Image Processing","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122867154","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

Video dehazing based on CNN 视频去雾基于CNN

Proceedings of the 5th International Conference on Multimedia and Image Processing Pub Date : 2020-01-10 DOI: 10.1145/3381271.3381278

Xing Zhao, Ting Zhang, Xiang Zhan, Wenxin Chen

引用次数: 0

The design of an intelligent monitoring system for human hand behaviors 设计了一种智能的手部行为监测系统

Proceedings of the 5th International Conference on Multimedia and Image Processing Pub Date : 2020-01-10 DOI: 10.1145/3381271.3381290

Zhengliang Wu, Mingfeng Lu, Chenchen Ji

引用次数: 1

Information hiding in scanned binary image of chinese characters 汉字扫描二值图像的信息隐藏

Proceedings of the 5th International Conference on Multimedia and Image Processing Pub Date : 2020-01-10 DOI: 10.1145/3381271.3381301

Wen Wen

{"title":"Information hiding in scanned binary image of chinese characters","authors":"Wen Wen","doi":"10.1145/3381271.3381301","DOIUrl":"https://doi.org/10.1145/3381271.3381301","url":null,"abstract":"Because most existing algorithms rarely consider the inherent characteristics of Chinese characters, information hiding in binary image of Chinese characters causes large distortion to the original binary image. To solve this problem, this paper proposes an algorithm for hiding and extracting information on binary image of Chinese characters. For information hiding, the scanned image is firstly geometrically corrected, then Chinese characters in the image are segmented by projection method. We take each segmented character as the unit for one bit of information hiding. The parity of number of black pixels in each character represents bit hidden, \"1\" or \"0\". Then, the position of hidden information is determined according to the stroke trend of Chinese characters. For information extraction, after segmenting characters in the same way as at information hiding phase, the receiver calculates the number of black pixels in each character to obtain information. Experimental results show that the proposed algorithm has a great advantage in the imperceptibility of information hiding. In addition, the computational cost of the algorithms both in hiding information and extracting information is low.","PeriodicalId":124651,"journal":{"name":"Proceedings of the 5th International Conference on Multimedia and Image Processing","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128708436","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

The fault diagnosis of catenary system based on the deep learning method in the railway industry 基于深度学习方法的接触网系统故障诊断在铁路行业中的应用

Proceedings of the 5th International Conference on Multimedia and Image Processing Pub Date : 2020-01-10 DOI: 10.1145/3381271.3381293

Chenchen Huang, Yuan Zeng

{"title":"The fault diagnosis of catenary system based on the deep learning method in the railway industry","authors":"Chenchen Huang, Yuan Zeng","doi":"10.1145/3381271.3381293","DOIUrl":"https://doi.org/10.1145/3381271.3381293","url":null,"abstract":"The catenary system plays a vital role in the railway industry, which is associated with the security and efficiency of the train operation. The fault diagnosis and anomaly detection of the catenary system is of significance. The current carrying ring and dropper are important parts of catenary and attract attention in the inspection process. Based on the image processing technique and deep learning method, the fault diagnosis method of the catenary system is presented. The fault diagnosis of catenary system consists of three parts, top current carrying ring, dropper and bottom current carrying ring detection. The feature pyramid network is applied for the various scales units of catenary system in image from inspection vehicle. Based on the modified CenterNet, the current carrying ring is detected. The results of the located rings are chosen through specific selection. Then the selected top and bottom rings are matched further through the location relationship. Based on the matched rings, the dropper is located and then classified by the classification network. According to the experiments on the plenty of catenary image datasets, it shows that the method have efficient and satisfied performance on the fault diagnosis of the catenary system.","PeriodicalId":124651,"journal":{"name":"Proceedings of the 5th International Conference on Multimedia and Image Processing","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126567969","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: 5

Automatic bounding-box-labeling method of occluded objects in virtual image data 虚拟图像数据中遮挡物的自动边界框标记方法

Proceedings of the 5th International Conference on Multimedia and Image Processing Pub Date : 2020-01-10 DOI: 10.1145/3381271.3381292

Xinyue Wang, LingZhong Meng, Yunzhi Xue

{"title":"Automatic bounding-box-labeling method of occluded objects in virtual image data","authors":"Xinyue Wang, LingZhong Meng, Yunzhi Xue","doi":"10.1145/3381271.3381292","DOIUrl":"https://doi.org/10.1145/3381271.3381292","url":null,"abstract":"Computer vision technology is widely used based on its massive and correct data set, of which the bounding box labeling is a common method. Aimed at a large number of original image data set produced by virtual simulation, we proposed an automatic pixel-level bounding-box-labeling method to solve problem of accuracy and speed. The method starts by a fundamental algorithm based on targeted bounding box, which will be adopted to label the images produced by virtual simulation and learn from the bounding box of different objects; Next, the method will find consistent seed points and apply region growing algorithm to automatically produce binary images based on the seed points; Then, an occlusion-estimating algorithm can be used to evaluate the occluded conditions in the binary image; Finally, employ bounding-box-labeling algorithm to label targeted objects according to various occlusion. Apply the data set from 2019 Small Target Competition held by China Society of Images and Graphics to test and verify our method, the result turns out that this method can solve the occlusion problem especially the truncate occlusion and can label the objects' entire body precisely.","PeriodicalId":124651,"journal":{"name":"Proceedings of the 5th International Conference on Multimedia and Image Processing","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131038038","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

Pavement type recognition based on deep learning 基于深度学习的路面类型识别

Proceedings of the 5th International Conference on Multimedia and Image Processing Pub Date : 2020-01-10 DOI: 10.1145/3381271.3381286

Gaojian Cui, Fanghu Ning, Xiaoguang Ren

引用次数: 1

Flying point target tracking using infrared images 利用红外图像对飞行点目标进行跟踪

Proceedings of the 5th International Conference on Multimedia and Image Processing Pub Date : 2020-01-10 DOI: 10.1145/3381271.3381284

S. Cao, Hongyan He

引用次数: 0

Perception of gender-stereotype in films: a case study on "Captain Marvel" superhero movie 对电影中性别刻板印象的认知——以超级英雄电影《惊奇队长》为例

Proceedings of the 5th International Conference on Multimedia and Image Processing Pub Date : 2020-01-10 DOI: 10.1145/3381271.3381303

K. T. Chau, Pau Yen Ooi, Tania Amos

引用次数: 1