2020 8th International Conference on Digital Home (ICDH)最新文献

Underwater image enhancement based on dual U-net 基于双U-net的水下图像增强

2020 8th International Conference on Digital Home (ICDH) Pub Date : 2020-09-01 DOI: 10.1109/ICDH51081.2020.00032

Ziyan Wang, Xinwei Xue, Long Ma, Xin Fan

{"title":"Underwater image enhancement based on dual U-net","authors":"Ziyan Wang, Xinwei Xue, Long Ma, Xin Fan","doi":"10.1109/ICDH51081.2020.00032","DOIUrl":"https://doi.org/10.1109/ICDH51081.2020.00032","url":null,"abstract":"As land resources have continually decreased, ocean exploration by humans has steadily grown. Underwater imaging is one of the most intuitive means to reflect the internal conditions of the ocean. However, due to the complex imaging environment of the ocean and light scattering in the sea, underwater images exhibit severe degradation, making it difficult to distinguish effective information. Thus, underwater imaging must be enhanced. Compared with traditional methods (e.g. histogram equalization method) and modeling methods, deep learning has been well applied in the field of computer vision. The key points are the acquisition of the training set and the generalization ability of the convolution model. Because the model-based method often needs to measure prior data manually in advance, it will cause inevitable errors; and direct generalization of the neural network will also cause image blurring. In this paper, we design a double U-Net for underwater image enhancement with strong generalization ability, in combination with modeling and deep learning methods. The gray image of the input image is processed with the attention mechanism in advance, and the relevant transmittance information is obtained using a U-Net. Then, the input image is processed with the information output from each layer of the previous U-Net. The final result is obtained by dividing the two U-Net results by pixels. The proposed network is trained using a paired training set generated by CycleGAN. Through quantitative and qualitative analysis, our method is proved to be more effective than the methods in recent papers in the field of underwater image enhancement.","PeriodicalId":210502,"journal":{"name":"2020 8th International Conference on Digital Home (ICDH)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126622420","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 CDIO Oriented Curriculum for Division of Data Science and Big Data Technologies: The Content, Process of Derivation, and Levels of Proficiency 面向CDIO的数据科学与大数据技术部课程:内容、衍生过程和熟练程度

2020 8th International Conference on Digital Home (ICDH) Pub Date : 2020-09-01 DOI: 10.1109/ICDH51081.2020.00037

Teng Zhou, Dazhi Jiang, Fei Wang, Xin Li, Lin Zheng

引用次数: 0

A New Image Hashing based on Discrete Trinion Fourier Transform 一种基于离散三角傅里叶变换的图像哈希算法

2020 8th International Conference on Digital Home (ICDH) Pub Date : 2020-09-01 DOI: 10.1109/ICDH51081.2020.00020

Yongchang Chen, Qiang Chen, Guoming Chen, Wanyi Li, Feifei Zhang, Jinxin Ruan

引用次数: 1

Knowledge Graph for Power Grid Dispatching of Digital Homes based on Graph Convolutional Network 基于图卷积网络的数字家庭电网调度知识图谱

2020 8th International Conference on Digital Home (ICDH) Pub Date : 2020-09-01 DOI: 10.1109/ICDH51081.2020.00042

Fei Peng, Tianyu An, Dan Li, Hanjun Wang, Changyi Tian, Zhikui Chen

{"title":"Knowledge Graph for Power Grid Dispatching of Digital Homes based on Graph Convolutional Network","authors":"Fei Peng, Tianyu An, Dan Li, Hanjun Wang, Changyi Tian, Zhikui Chen","doi":"10.1109/ICDH51081.2020.00042","DOIUrl":"https://doi.org/10.1109/ICDH51081.2020.00042","url":null,"abstract":"With the wide deployment of numerous digital media equipment into home and life, power grid dispatching (PGD) systems, as the energy source of digital media equipment, generate a large amount of data. Those data are stored in large volumes, various types, separate features, and lacking unified data specifications, making it harder to organize and learn. In view of the above challenges, inspired by the knowledge graph models which hold strong power for knowledge representing and reasoning, this paper constructs a knowledge graph model for power grid dispatching regulations with graph convolutional network (KGPGD). Specifically, in the context of the massive growth of grid dispatching data, knowledge extraction methods are used to obtain the entity words and relation words in the specification from the smart grid dispatching control system. The dataset of technical specifications of the smart grid dispatching control system is the real-world provided by the Northeast Branch of State Grid Corporation of China. And then a domain knowledge graph is automatically constructed to study domain-oriented big data of PGD. At the same time, the graph convolutional neural network model is utilized to learn the power grid dispatching knowledge graph features. KGPGD provides a reference basis and decision support for the formulation of the technical specifications of the power grid dispatching system of digital homes.","PeriodicalId":210502,"journal":{"name":"2020 8th International Conference on Digital Home (ICDH)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129733975","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

[Title page iii] [标题页iii]

2020 8th International Conference on Digital Home (ICDH) Pub Date : 2020-09-01 DOI: 10.1109/icdh51081.2020.00002

引用次数: 0

Automatic Image Colorization via Weighted Sparse Representation Learning 基于加权稀疏表示学习的图像自动着色

2020 8th International Conference on Digital Home (ICDH) Pub Date : 2020-09-01 DOI: 10.1109/ICDH51081.2020.00009

Bo Li, Juncai Zhou

{"title":"Automatic Image Colorization via Weighted Sparse Representation Learning","authors":"Bo Li, Juncai Zhou","doi":"10.1109/ICDH51081.2020.00009","DOIUrl":"https://doi.org/10.1109/ICDH51081.2020.00009","url":null,"abstract":"Automatic image colorization is to generate a colorful image from a given gray image automatically. It is an ill-conditioned task and remains a challenging problem in computer vision. One main challenge in example-based image colorization is how to find the correct correspondence between the grayscale image and the reference color image. In this paper, we propose a novel automatic example-based image colorization method via weighted sparse matching. First, we segment the images into superpixels, and operates at the superpixel level rather than pixels. Then we extract intensity features and texture features for each superpixel, which are then concatenated to form its descriptor. The feature descriptors collected from the reference image composes the representation dictionary. Finally, the correspondence between target grayscale image and the colorful reference image is built by solving a weighted sparse representation learning problem, and the target superpixels are colorized based on the chrominance information from the corresponding reference superpixels. Experimental results demonstrate that our colorization method outperforms several state-of-the-art methods.","PeriodicalId":210502,"journal":{"name":"2020 8th International Conference on Digital Home (ICDH)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130231698","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

Densely Connected Feature Pyramid Network for Image Segmentation 用于图像分割的密集连接特征金字塔网络

2020 8th International Conference on Digital Home (ICDH) Pub Date : 2020-09-01 DOI: 10.1109/ICDH51081.2020.00024

Yuhang Jia, Jieqing Tan, Yan Xing, Peilin Hong, Li Zhang

{"title":"Densely Connected Feature Pyramid Network for Image Segmentation","authors":"Yuhang Jia, Jieqing Tan, Yan Xing, Peilin Hong, Li Zhang","doi":"10.1109/ICDH51081.2020.00024","DOIUrl":"https://doi.org/10.1109/ICDH51081.2020.00024","url":null,"abstract":"Image segmentation is a specific image processing technique used to segment a picture into two or more semantic regions. This paper proposes a densely connected feature pyramid segmentation network and applies it to the segmentation of images in real driving scenarios. The feature pyramid network is a kind of feature extractor, which is originally used for object detection. This paper applies it to image segmentation tasks. The densely connected network is used as a part of feature pyramid network to extract features from bottom to top. Through the lateral connection, the features of the bottom-up part and the top-down part are merged. In the final merge stage, the extracted features are transformed to the same size through upsampling and then concatenated together, and finally the segmentation map is output. The experiment is conducted on the CamVid dataset, which is a dataset in actual driving scenarios. In the experiment, the generalization ability of the segmentation model is improved through data enhancement. Based on the densely connected feature pyramid segmentation network, the F1-score on the test set is 0.8895, and the IoU-score is 0.8209.","PeriodicalId":210502,"journal":{"name":"2020 8th International Conference on Digital Home (ICDH)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130991112","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: 3

Research on Trajectory Clustering Optimization Algorithm Based on Sparse Representation 基于稀疏表示的轨迹聚类优化算法研究

2020 8th International Conference on Digital Home (ICDH) Pub Date : 2020-09-01 DOI: 10.1109/ICDH51081.2020.00047

Wenting Zhao, Jingkang Yang, Fang Li, Cheng Pang, Ji Li, Xiaonan Luo

引用次数: 0

Named Entity Recognition in Chinese Judicial Domain Based on Self-attention mechanism and IDCNN 基于自注意机制和IDCNN的中文司法领域命名实体识别

2020 8th International Conference on Digital Home (ICDH) Pub Date : 2020-09-01 DOI: 10.1109/icdh51081.2020.00017

Wenming Huang, Juan Zhang, Yannan Xiao, Zheng Han, Zhenrong Deng

{"title":"Named Entity Recognition in Chinese Judicial Domain Based on Self-attention mechanism and IDCNN","authors":"Wenming Huang, Juan Zhang, Yannan Xiao, Zheng Han, Zhenrong Deng","doi":"10.1109/icdh51081.2020.00017","DOIUrl":"https://doi.org/10.1109/icdh51081.2020.00017","url":null,"abstract":"Chinese named entity recognition (CNER) in the judicial domain is an important basic task for intelligent analysis and processing of massive documents. This domain entity has more complicated structure than the common named entity, and its entity category is more abundant. However, the general method can not solve the problem of domain specific identification. In this paper, we combine self-attention mechanism and iteration dilated convolution neural network (IDCNN) for CNER in judicial domain. The bidirectional gate recurrent unit (BiGRU) model is used to automatically learn the context semantic information of the text and solve the long-distance dependence of the sequence. The model introduce the IDCNN to extract the key features of context semantic information, and capture finer-grained semantic information in underlying texts. The self-attention mechanism is used to analyze the relationship between characters, and the problem of long sequence semantic dilution is effectively solved by means of dynamic weight, and the optimal tag sequence is calculated by integrating conditional random fields (CRF), which further improves the recognition ability of the model. Finally, by analyzing the characteristics of legal documents, the new data set is annotated and the fine-grained named entity recognition is realized. The experimental results on our corpus show that the proposed method can effectively identify the entities in legal documents, and improve performance in the judicial field.","PeriodicalId":210502,"journal":{"name":"2020 8th International Conference on Digital Home (ICDH)","volume":"601 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116306339","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: 3

An improved ARAIM algorithm based on feedback structure 基于反馈结构的改进ARAIM算法

2020 8th International Conference on Digital Home (ICDH) Pub Date : 2020-09-01 DOI: 10.1109/ICDH51081.2020.00034

Wentao Fu, Linzhu Xu, Xiaonan Luo, Xiyan Sun, Yuanfa Ji

引用次数: 0