Underwater Inherent Optical Properties Estimation Using a Depth Aided Deep Neural Network
作者: Zhibin YuYubo WangBing ZhengHaiyong ZhengNan WangZhaorui Gu
作者单位: 1epartment of Electronic Engineering, College of Information Science and Engineering, Ocean University of China, Qingdao, China
2School of Life Science and Technology, Xidian University, Xi’an, China
3Department of Electronic Engineering, College of Information Science and Engineering, Ocean University of China, Qingdao, China
4Department of Electronic Engineering, College of Infor
刊名: Computational Intelligence and Neuroscience, 2017, Vol.2017
来源数据库: Directory of Open Access Journals
DOI: 10.1155/2017/8351232
原始语种摘要: Underwater inherent optical properties (IOPs) are the fundamental clues to many research fields such as marine optics, marine biology, and underwater vision. Currently, beam transmissometers and optical sensors are considered as the ideal IOPs measuring methods. But these methods are inflexible and expensive to be deployed. To overcome this problem, we aim to develop a novel measuring method using only a single underwater image with the help of deep artificial neural network. The power of artificial neural network has been proved in image processing and computer vision fields with deep learning technology. However, image-based IOPs estimation is a quite different and challenging task. Unlike the traditional applications such as image classification or localization, IOP estimation looks at...
全文获取路径: DOAJ  (合作)
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关键词翻译
关键词翻译
  • image 
  • vision 视力
  • network 网络
  • optical 光学的
  • camera 摄影机
  • neural 神经系统的
  • artificial 人为的
  • measuring 测量
  • processing 加工
  • noisy 有噪声