Proceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition最新文献

Robust Image Watermarking Method in Wavelet Domain Based on SIFT Features 基于SIFT特征的小波域鲁棒图像水印方法

Proceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2020-06-26 DOI: 10.1145/3430199.3430243

R. Ahmad, Xiaoming Yao, S. Nawaz, U. Bhatti, Anum Mehmood, M. Bhatti, Mohammad Usman Shaukat

{"title":"Robust Image Watermarking Method in Wavelet Domain Based on SIFT Features","authors":"R. Ahmad, Xiaoming Yao, S. Nawaz, U. Bhatti, Anum Mehmood, M. Bhatti, Mohammad Usman Shaukat","doi":"10.1145/3430199.3430243","DOIUrl":"https://doi.org/10.1145/3430199.3430243","url":null,"abstract":"To protect the ownership of the digital content, the robustness of the watermarking algorithm is the most important metric to assess its affectiveness. However few state-of-the-art watermarking algorithms can resist the combinations of the conventional attacks such as jpeg compression and geometric transformation. In this paper, an improved robust image watermarking algorithm is thus proposed to address this issue. The watermark information is embedded in the low frequency domain of the wavelet transform by a quantization modulation method. When using watermark detection, use matching the position information of the SIFT key points is used to calculate the affine transformation parameters and the edge point parameters, and then inversely transform and reposition the detected image to recover the watermark synchronization information. Theoretical analysis and experimental results show that the proposed algorithm has high correlation accuracy and stable performance, and can effectively recover the watermark synchronization of watermark images subjected to rotation, scaling and translation attacks, so that the watermark algorithm can correctly detect or extract watermarks.","PeriodicalId":371055,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127269898","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

Proceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition 2020年第三届人工智能与模式识别国际会议论文集

Proceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2020-06-26 DOI: 10.1145/3430199

引用次数: 0

Applying Social Network Extraction With Named Entity Recognition to the Examination of Political Bias Within Online News Articles 基于命名实体识别的社会网络提取在网络新闻文章政治偏见检测中的应用

Proceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2020-06-26 DOI: 10.1145/3430199.3430219

K. Lin, C. Tsai

引用次数: 0

People Counting Based on Multi-scale Region Adaptive Segmentation and Depth Neural Network 基于多尺度区域自适应分割和深度神经网络的人口计数

Proceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2020-06-26 DOI: 10.1145/3430199.3430201

Feng Min, Yansong Wang, Sicheng Zhu

引用次数: 1

Improving Single Shot Detector for Industrial Cracks by Feature Resolution Analysis 利用特征分辨率分析改进工业裂纹单针探测器

Proceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2020-06-26 DOI: 10.1145/3430199.3430204

Shengxiang Qi, Yaming Dong, Qing Mao

{"title":"Improving Single Shot Detector for Industrial Cracks by Feature Resolution Analysis","authors":"Shengxiang Qi, Yaming Dong, Qing Mao","doi":"10.1145/3430199.3430204","DOIUrl":"https://doi.org/10.1145/3430199.3430204","url":null,"abstract":"Although the single shot detector (SSD) is effective for object detection in natural images, it is not suitable for special tasks such as the industrial crack detection. The difficulty lies in the wide diversity of crack sizes and shapes that is usually unpredictable. To solve this problem, we improve the SSD model by feature resolution analysis. The classical SSD network extracts several convolutional feature layers with degressive scales, and then classifies and locates targets by a series of prior boxes with default sizes and aspect ratios regarding to each scale. Therefore, the key is whether the design of these prior boxes is consistent with the real target characteristics. In this paper, we improve the architecture of SSD network via statistically analyzing the distribution of sizes and shapes from our collected crack samples. According to the resolution analysis of the targets at each feature scale, only a fewer number of valid feature layers are carefully extracted, and some more accurate prior boxes are designed relative to each scale. Finally, experimental results demonstrate that the proposed method could not only achieve significantly better prediction accuracy, but also acquire higher computational efficiency, which outperform the state-of-the-art methods.","PeriodicalId":371055,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127067583","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

An LSTM-based Traffic Prediction Algorithm with Attention Mechanism for Satellite Network 基于lstm的卫星网络注意机制流量预测算法

Proceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2020-06-26 DOI: 10.1145/3430199.3430208

Feiyu Zhu, Lixiang Liu, Teng Lin

引用次数: 5

Mammography Registration for Unsupervised Learning Based on CC and MLO Views 基于CC和MLO视图的无监督学习乳房x线摄影注册

Proceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2020-06-26 DOI: 10.1145/3430199.3430238

Jiyun Li, Xiaomeng Wang, Chen Qian

引用次数: 0

Offline Handwritten Chinese Character Recognition Based on Improved Googlenet 基于改进Googlenet的离线手写汉字识别

Proceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2020-06-26 DOI: 10.1145/3430199.3430202

Feng Min, Sicheng Zhu, Yansong Wang

引用次数: 7

A Survey of Research on Image Data Sources Forensics 图像数据源取证研究综述

Proceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2020-06-26 DOI: 10.1145/3430199.3430241

Xu Meng, Kun Meng, Wenbao Qiao

{"title":"A Survey of Research on Image Data Sources Forensics","authors":"Xu Meng, Kun Meng, Wenbao Qiao","doi":"10.1145/3430199.3430241","DOIUrl":"https://doi.org/10.1145/3430199.3430241","url":null,"abstract":"The development of technologies such as smart terminals and mobile Internet has made image data one of the most important forms of data in the Internet and personal storage media, and has grown at an alarming rate. As the most effective expression of information, image data can record various information when image content appears, and it can play an unparalleled role in restoring the truth of things. Therefore, the aim of efficiently and accurately identify the source of image is to determine the device that generated the data. It is an effective means of clustering data from the same device, and become a key step in helping to understand the full content. It is one of the core technologies for conducting electronic data forensic evidence. On the basis of summarizing and analyzing the image generation process, this paper analyzes the data shape and acquisition steps of the potential image generation device information, and then obtains the method of image data source identification. It also summarizes the existing related technologies and methods, comparative analysis of their applicability and potential development direction.","PeriodicalId":371055,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117218873","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

Image Segmentation Based on Finite IBL Mixture Model with a Dirichlet Compound Multinomial Prior 基于Dirichlet复合多项式先验有限IBL混合模型的图像分割

Proceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2020-06-26 DOI: 10.1145/3430199.3430207

Z. Guo, Wentao Fan

引用次数: 0