2018 7th International Conference on Digital Home (ICDH)最新文献_第3页

2D Chebyshev-Sine Map for Image Encryption 用于图像加密的二维切比雪夫正弦映射

2018 7th International Conference on Digital Home (ICDH) Pub Date : 2018-11-01 DOI: 10.1109/ICDH.2018.00008

Y. Zhong, Huayi Liu, Rushi Lan, Ting Wang, Xiyan Sun, Xiaonan Luo

引用次数: 7

A Method of Feature Extraction for BCG Signal Based on Wavelet and Bispectrum 基于小波和双谱的BCG信号特征提取方法

2018 7th International Conference on Digital Home (ICDH) Pub Date : 2018-11-01 DOI: 10.1109/icdh.2018.00027

Zimin Wang, Li Zeng, Yumeng Wang, Man Wang

引用次数: 0

User Interface Layout Recommendation Based on Pairing Model 基于配对模型的用户界面布局推荐

2018 7th International Conference on Digital Home (ICDH) Pub Date : 2018-11-01 DOI: 10.1109/ICDH.2018.00041

Xiaohong Shi, Shuyi Huang, Yongsheng Rao, Xiangping Chen

引用次数: 0

Simulation and Circuit Realization of a Novel Symmetry Grid Multi-wing Chaotic System 一种新型对称网格多翼混沌系统的仿真与电路实现

2018 7th International Conference on Digital Home (ICDH) Pub Date : 2018-11-01 DOI: 10.1109/ICDH.2018.00031

Chaoxia Zhang, Jinxin Ruan, Qiang Chen

引用次数: 0

Multi-parameter Estimation and Its Cramer-Rao Lower Bound for Underwater Acoustic Signal 水声信号的多参数估计及其Cramer-Rao下界

2018 7th International Conference on Digital Home (ICDH) Pub Date : 2018-11-01 DOI: 10.1109/ICDH.2018.00026

Liying Xie, Jinxin Ruan, Yilin Wu, Qiang Chen

引用次数: 0

An Efficient Expansion Word Extraction Algorithm for Educational Video 一种有效的教育视频扩展词提取算法

2018 7th International Conference on Digital Home (ICDH) Pub Date : 2018-11-01 DOI: 10.1109/ICDH.2018.00032

Lijie Shao, Fuwei Zhang, Ruomei Wang, Fan Zhou, Shujin Lin

引用次数: 2

Research and Implementation of Path Recommendation Algorithm Based on Exercise Record 基于运动记录的路径推荐算法研究与实现

2018 7th International Conference on Digital Home (ICDH) Pub Date : 2018-11-01 DOI: 10.1109/ICDH.2018.00049

Qiuyuan Luo, Linfa Lu, Guifeng Zheng, Jinlu Xue

引用次数: 1

A Benchmark for Action Recognition of Large Animals 大型动物动作识别的基准

2018 7th International Conference on Digital Home (ICDH) Pub Date : 2018-11-01 DOI: 10.1109/ICDH.2018.00020

Yun Liang, Fuyou Xue, Xiaoming Chen, Zexin Wu, Xiang Chen

{"title":"A Benchmark for Action Recognition of Large Animals","authors":"Yun Liang, Fuyou Xue, Xiaoming Chen, Zexin Wu, Xiang Chen","doi":"10.1109/ICDH.2018.00020","DOIUrl":"https://doi.org/10.1109/ICDH.2018.00020","url":null,"abstract":"The action recognition of large animals plays an important role in the intelligent and modern farming. People often use the actions as the key factors to achieve scientific feeding and improve the animal welfare, and then the quality and productivity of animals are greatly promoted. However, most present action recognition methods focus on the actions of human (as pedestrian, athletes) or man-made objects (as cars, bikes). This paper proposes a benchmark to recognize and evaluate the actions of a kind of large animals namely the cows. First, we construct a dataset including 60 videos to describe the popular actions existing in the daily life of cows, and manually denote the target regions of cows on every frame in the dataset. Second, six famous trackers are evaluated on this dataset to compute the trajectory of cows which is the basis of actions recognition. Third, we define the method to recognize the actions of cows via the trajectories and validate the proposed method on our dataset. Many experiments demonstrate that our method of action recognition performs favorable in detecting the actions of cows, and the proposed dataset basically satisfies the action evaluations for farmers. The work in this paper provides an automatic and scientific method for famers to design a scheme to promote the quality and productivity of cows.","PeriodicalId":117854,"journal":{"name":"2018 7th International Conference on Digital Home (ICDH)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124482256","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: 4

Single Image Super-Resolution via Laplacian Information Distillation Network 基于拉普拉斯信息蒸馏网络的单幅图像超分辨率

2018 7th International Conference on Digital Home (ICDH) Pub Date : 2018-11-01 DOI: 10.1109/ICDH.2018.00012

M. Cheng, Zhan Shu, Jiapeng Hu, Y. Zhang, Zhuo Su

引用次数: 1

Sparse Representation-Based Face Object Generative via Deep Adversarial Network 基于深度对抗网络的稀疏表示人脸对象生成

2018 7th International Conference on Digital Home (ICDH) Pub Date : 2018-11-01 DOI: 10.1109/ICDH.2018.00019

Ye Yuan, Yong Zhang, Shaofan Wang, Baocai Yin

{"title":"Sparse Representation-Based Face Object Generative via Deep Adversarial Network","authors":"Ye Yuan, Yong Zhang, Shaofan Wang, Baocai Yin","doi":"10.1109/ICDH.2018.00019","DOIUrl":"https://doi.org/10.1109/ICDH.2018.00019","url":null,"abstract":"How to generate well quality faces objects of automated processes has always been the focus on researchers. Recently, due to the deep generative networks have achieved impressive successes in data generative fields, researchers have tried to introduce deep learning into the 3d objects generate field, such as text2scene, slice-based object generate. However, the generative ability in 3D object is limited by the size of the feature space, because of computational space limitations on hardware. In this paper, we address the problem by reducing amount of calculated on process of learning, and thus generative newly different objects. The problem is intractable, since first the limiting of compute space is so hard that object can't be process in deep network due to the process need to compute many matrix multiplications. To resolve the problem, we propose a sparse representation-based method of generating well-quality faces object. Our method consists of two parts: sparse reconstruction and object generative. First, we verified the possibility of using sparse representations of 3D data by reconstructing 3D object. Second, we design a network architecture of deep adversarial network of generating new sparse representation and combined with the previous reconstruction method of generating new face object. Experiments show that our method has the ability to generate very different and well quality faces objects that contain tens of thousands of points and meshes. Our findings show that sparse representation can be used in 3D object reconstruction and generate via deep generative adversarial model.","PeriodicalId":117854,"journal":{"name":"2018 7th International Conference on Digital Home (ICDH)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127321837","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