Contents
2017
Volume: 13 Issue 6
18 Article(s)
Radiometric calibration of hyper-spectral imaging spectrometer based on optimizing multi-spectral band selection
Li-wei SUN, Xin YE, Wei FANG, Zhen-lei HE, Xiao-long YI, and Yu-peng WANG
Optoelectronics Letters
  • Publication Date: Jan. 01, 1900
  • Vol.13 Issue, 6 405 (2017)
Low-resolution expression recognition based on central oblique average CS-LBP with adaptive threshold
Sheng HAN, Shi-qiong XI, and Wei-dong GENG
Optoelectronics Letters
  • Publication Date: Jan. 01, 1900
  • Vol.13 Issue, 6 444 (2017)
Discriminatively learning for representing local image features with quadruplet model
Zhang Da-long, Zhao Lei, Xu Duan-qing, and Lu Dong-ming
Optoelectronics Letters
  • Publication Date: Jan. 01, 1900
  • Vol.13 Issue, 6 462 (2017)
Image aesthetic quality evaluation using convolution neural network embedded learning
Yu-xin LI, Yuan-yuan PU, Dan XU, Wen-hua QIAN, and Li-peng WANG
A way of embedded learning convolution neural network (ELCNN) based on the image content is proposed to evaluate the image aesthetic quality in this paper. Our approach can not only solve the problem of small-scale data but also score the image aesthetic quality. First, we chose Alexnet and VGG_S to compare for confirm
Optoelectronics Letters
  • Publication Date: Jan. 01, 1900
  • Vol.13 Issue, 6 471 (2017)
Traffic sign recognition based on deep convolutional neural network
Shi-hao YIN, Ji-cai DENG, Da-wei ZHANG, and Jing-yuan DU
Traffic sign recognition (TSR) is an important component of automated driving systems. It is a rather challenging task to design a high-performance classifier for the TSR system. In this paper, we propose a new method for TSR system based on deep convolutional neural network. In order to enhance the expression of the n
Optoelectronics Letters
  • Publication Date: Jan. 01, 1900
  • Vol.13 Issue, 6 476 (2017)