Proceedings of the 12th International Symposium on Visual Information Communication and Interaction最新文献_第3页

Parsing Strategies for Context-Sensitive Graph Grammars 上下文敏感图语法的解析策略

Proceedings of the 12th International Symposium on Visual Information Communication and Interaction Pub Date : 2019-09-20 DOI: 10.1145/3356422.3356443

Yang Zou, Xiaoqin Zeng, Yufeng Liu

引用次数: 0

TEAMwISE: Synchronised Immersive Environments for Exploration and Analysis of Movement Data TEAMwISE:用于探索和分析运动数据的同步沉浸式环境

Proceedings of the 12th International Symposium on Visual Information Communication and Interaction Pub Date : 2019-09-20 DOI: 10.1145/3356422.3356450

Karsten Klein, Michael Aichem, B. Sommer, Stefan Erk, Ying Zhang, F. Schreiber

引用次数: 2

VIStory VIStory

Proceedings of the 12th International Symposium on Visual Information Communication and Interaction Pub Date : 2019-09-20 DOI: 10.1145/3356422.3356430

Ao Dong, Wei Zeng, Xi Chen, Zhanglin Cheng

{"title":"VIStory","authors":"Ao Dong, Wei Zeng, Xi Chen, Zhanglin Cheng","doi":"10.1145/3356422.3356430","DOIUrl":"https://doi.org/10.1145/3356422.3356430","url":null,"abstract":"Abstract Many visual analytics have been developed for examining scientific publications comprising wealthy data such as authors and citations. The studies provide unprecedented insights on a variety of applications, e.g., literature review and collaboration analysis. However, visual information (e.g., figures) that is widely employed for storytelling and methods description are often neglected. We present VIStory , an interactive storyboard for exploring visual information in scientific publications. We harvest a new dataset of a large corpora of figures, using an automatic figure extraction method. Each figure contains various attributes such as dominant color and width/height ratio, together with faceted metadata of the publication including venues, authors, and keywords. To depict these information, we develop an intuitive interface consisting of three components: (1) Faceted View enables efficient query by publication metadata, benefiting from a nested table structure, (2) Storyboard View arranges paper rings—a well-designed glyph for depicting figure attributes, in a themeriver layout to reveal temporal trends, and (3) Endgame View presents a highlighted figure together with the publication metadata. We illustrate the applicability of VIStory with case studies on two datasets, i.e., 10-year IEEE VIS publications, and publications by a research team at CVPR, ICCV, and ECCV conferences. Quantitative and qualitative results from a formal user study demonstrate the efficiency of VIStory in exploring visual information in scientific publications. Graphical abstract","PeriodicalId":197051,"journal":{"name":"Proceedings of the 12th International Symposium on Visual Information Communication and Interaction","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121852153","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: 17

Automatic Image Segmentation for Microwave Tomography (MWT): From Implementation to Comparative Evaluation 微波断层成像(MWT)的自动图像分割:从实现到比较评价

Proceedings of the 12th International Symposium on Visual Information Communication and Interaction Pub Date : 2019-09-20 DOI: 10.1145/3356422.3356437

Yuchong Zhang, Yong Ma, Adel Omrani, Rahul Yadav, M. Fjeld, M. Fratarcangeli

引用次数: 5

How to Improve Semantics Understanding of Word Clouds 如何提高词云的语义理解

Proceedings of the 12th International Symposium on Visual Information Communication and Interaction Pub Date : 2019-09-20 DOI: 10.1145/3356422.3356449

Lu-Biao Yang, Jie Li, Wenhuan Lu, Yi Chen, Kang Zhang, Yan Li

引用次数: 1

Digital Simulation and Reinterpretation for Pattern from Art Heritages 艺术遗产中图案的数字模拟与再解读

Proceedings of the 12th International Symposium on Visual Information Communication and Interaction Pub Date : 2019-09-20 DOI: 10.1145/3356422.3356455

X. Yu, Zeshi Chen, Shiwen Wang

引用次数: 1

Features Extraction and Classification of Wood Defect Based on Hu Invariant Moment and Wavelet Moment and BP Neural Network 基于Hu不变矩、小波矩和BP神经网络的木材缺陷特征提取与分类

Proceedings of the 12th International Symposium on Visual Information Communication and Interaction Pub Date : 2019-09-20 DOI: 10.1145/3356422.3356459

Xuyuan Ji, Hui Guo, Minghong Hu

{"title":"Features Extraction and Classification of Wood Defect Based on Hu Invariant Moment and Wavelet Moment and BP Neural Network","authors":"Xuyuan Ji, Hui Guo, Minghong Hu","doi":"10.1145/3356422.3356459","DOIUrl":"https://doi.org/10.1145/3356422.3356459","url":null,"abstract":"Wood defect will reduce wood properties, wood quality and use value, so it is of great practical significance to detect wood defect[1]. The key to feature extraction of target defect image is target recognition and classification. Moment feature is a common feature descriptor in defect extraction algorithm. Aiming at the problem that the seven feature components of Hu moments differ greatly in magnitude and are affected by scale factor, based on the principle and characteristics of invariant moments and wavelet energy, a feature extraction algorithm based on wavelet moments is proposed and applied to the feature extraction of wood defects. Finally, the experiment collects the actual wood defect image, decomposes the preprocessed image into three sub-images by wavelet transform, calculates the modified Hu moment invariants for the sub-images, takes the moment invariants as the feature variables, and obtains the recognition results by using the minimum neighborhood distance classification. The experimental results show that the feature extracted by this method has the invariance of translation, rotation and scale, and can reflect the important and original attributes of the target image. Compared with the traditional Hu moment, the recognition rate is significantly improved, and the expected goal is achieved.","PeriodicalId":197051,"journal":{"name":"Proceedings of the 12th International Symposium on Visual Information Communication and Interaction","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123910551","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: 11

Object Inflation for Artistic Augmentation in Images and Animations 用于图像和动画艺术增强的对象膨胀

Proceedings of the 12th International Symposium on Visual Information Communication and Interaction Pub Date : 2019-09-20 DOI: 10.1145/3356422.3356427

Xiaoqiang Ding, Ren Peng, Jinhui Yu

引用次数: 0

Proceedings of the 12th International Symposium on Visual Information Communication and Interaction 第十二届视觉信息传播与互动国际学术研讨会论文集

Proceedings of the 12th International Symposium on Visual Information Communication and Interaction Pub Date : 2019-09-20 DOI: 10.1145/3356422

引用次数: 3

Research on Traffic Sign Detection Based on Convolutional Neural Network 基于卷积神经网络的交通标志检测研究

Proceedings of the 12th International Symposium on Visual Information Communication and Interaction Pub Date : 2019-09-20 DOI: 10.1145/3356422.3356457

Zhong-yu Wang, Hui Guo

{"title":"Research on Traffic Sign Detection Based on Convolutional Neural Network","authors":"Zhong-yu Wang, Hui Guo","doi":"10.1145/3356422.3356457","DOIUrl":"https://doi.org/10.1145/3356422.3356457","url":null,"abstract":"TSD (Traffic Sign Detection) is a hotspot in autonomous driving and assisted driving research. TSD research is of great significance for improving road traffic safety. In recent years, CNN (Convolutional Neural Networks) have achieved great success in object detecting tasks. It shows better accuracy or faster execution speed than traditional method. However, the execution speed and the detection accuracy of the existing CNN methods cannot be obtained at the same time. What's more, the hardware requirements are also higher than before, resulting in a larger detection cost. In order to solve these problems, this paper proposes an improved CNN model based on YOLO model, darknet 53 construction. By introducing batch normalization and RPN networks and improving the network structure for traffic sign detection tasks, the YOLO neural network detection model is optimized. The accuracy of the model in the traffic sign detection task is greatly improved, and the detection speed becomes faster. The results show that the method in this paper is of great help to improve the accuracy and detection speed of traffic sign detection and reduce the hardware requirements of the detection system as well.","PeriodicalId":197051,"journal":{"name":"Proceedings of the 12th International Symposium on Visual Information Communication and Interaction","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134551264","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: 6