中国开放科研知识云: 检索

Remarnet: Conjoint relation and margin learning for small-sample image classification 期刊论文
IEEE Transactions on Circuits and Systems for Video Technology, 2021, 卷号: 31, 期号: 4, 页码: 1569-1579
作者: Li, Xiaoxu; Yu, Liyun; Yang, Xiaochen; Ma, Zhanyu; Xue, Jing-Hao

收藏 | 浏览/下载：8/0 | 提交时间：2021/06/03

Deep learning Image classification Sampling Classification mechanism Competitive performance Discriminative features Discriminative power Fully connected networks Novel neural network State-of-the-art methods State-of-the-art performance

Deeply Supervised Depth Map Super-Resolution as Novel View Synthesis 期刊论文
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2019, 卷号: 29, 期号: 8, 页码: 2323-2336
作者: Song, Xibin; Dai, Yuchao; Qin, Xueying

收藏 | 浏览/下载：14/0 | 提交时间：2019/12/11

Convolutional neural network depth map super-resolution novel view synthesis

Deeply Supervised Depth Map Super-Resolution as Novel View Synthesis 期刊论文
IEEE Transactions on Circuits and Systems for Video Technology, 2018
作者: Song X.; Dai Y.; Qin X.

收藏 | 浏览/下载：4/0 | 提交时间：2019/12/11

Cameras Color Convolutional Neural Network Deconvolution Depth Map DH-HEMTs Novel View Synthesis Spatial resolution Super-Resolution Task analysis

A New RNN Model With a Modified Nonlinear Activation Function Applied to Complex-Valued Linear Equations 期刊论文
IEEE ACCESS, 2018, 卷号: Vol.6, 页码: 62954-62962
作者: Ding, L; Xiao, L; Zhou, KQ; Lan, YH; Zhang, YS

收藏 | 浏览/下载：10/0 | 提交时间：2019/12/26

Recurrent neural network convergence rate finite time complex-valued systems of linear equation novel nonlinear activation function

A New RNN Model With a Modified Nonlinear Activation Function Applied to Complex-Valued Linear Equations 期刊论文
IEEE ACCESS, 2018, 卷号: Vol.6, 页码: 62954-62962
作者: Ding, Lei; Xiao, Lin; Zhou, Kaiqing; Lan, Yonghong; Zhang, Yongsheng

收藏 | 浏览/下载：9/0 | 提交时间：2019/12/26

Recurrent neural network convergence rate finite time complex-valued systems of linear equation novel nonlinear activation function

A novel fusion algorithm for visible and infrared image using non-subsampled contourlet transform and pulse-coupled neural network 会议论文
作者: Ikuta, Chihiro; Zhang, Songjun; Uwate, Yoko; Yang, Guoan; Nishio, Yoshifumi

收藏 | 浏览/下载：5/0 | 提交时间：2019/12/02

Computer vision applications Image fusion algorithms Low and high frequencies Non subsampled contourlet transform (NSCT) Non-sub-sampled contourlet transforms Novel fusion algorithms Pulse coupled neural network Visible image

Multi-focus image fusion algorithm based on adaptive PCNN and wavelet transform (EI CONFERENCE) 会议论文
International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Imaging Detectors and Applications, May 24, 2011 - May 26, 2011, Beijing, China
Wu Z.-G.; Wang M.-J.; Han G.-L.

收藏 | 浏览/下载：34/0 | 提交时间：2013/03/25

Being an efficient method of information fusion image fusion has been used in many fields such as machine vision medical diagnosis military applications and remote sensing.In this paper Pulse Coupled Neural Network (PCNN) is introduced in this research field for its interesting properties in image processing including segmentation target recognition et al. and a novel algorithm based on PCNN and Wavelet Transform for Multi-focus image fusion is proposed. First the two original images are decomposed by wavelet transform. Then based on the PCNN a fusion rule in the Wavelet domain is given. This algorithm uses the wavelet coefficient in each frequency domain as the linking strength so that its value can be chosen adaptively. Wavelet coefficients map to the range of image gray-scale. The output threshold function attenuates to minimum gray over time. Then all pixels of image get the ignition. So the output of PCNN in each iteration time is ignition wavelet coefficients of threshold strength in different time. At this moment the sequences of ignition of wavelet coefficients represent ignition timing of each neuron. The ignition timing of PCNN in each neuron is mapped to corresponding image gray-scale range which is a picture of ignition timing mapping. Then it can judge the targets in the neuron are obvious features or not obvious. The fusion coefficients are decided by the compare-selection operator with the firing time gradient maps and the fusion image is reconstructed by wavelet inverse transform. Furthermore by this algorithm the threshold adjusting constant is estimated by appointed iteration number. Furthermore In order to sufficient reflect order of the firing time the threshold adjusting constant is estimated by appointed iteration number. So after the iteration achieved each of the wavelet coefficient is activated. In order to verify the effectiveness of proposed rules the experiments upon Multi-focus image are done. Moreover comparative results of evaluating fusion quality are listed. The experimental results show that the method can effectively enhance the edge details and improve the spatial resolution of the image. 2011 SPIE.

用人工神经网络分析Ni含量对新型空冷贝氏体钢CCT图的定量影响 期刊论文
2010, 2010
刘亚秀; 徐卫红; 由伟; 白秉哲; 方鸿生; LIU Ya-xiu; XU Wei-hong; YOU Wei; BAI Bing-zhe; FANG Hong-sheng

收藏 | 浏览/下载：2/0

Effect of chromium on CCT diagrams of novel air-cooled bainite steels analyzed by neural network 期刊论文
2010, 2010
You, Wei; Xu, Wei-hong; Liu, Ya-xiu; Bai, Bing-zhe; Fang, Hong-sheng

收藏 | 浏览/下载：1/0

Quantitative analysis of Ni effect on CCT diagrams of novel air-cooled bainite steels using artificial neural network models 期刊论文
2010, 2010, OCT
Xu, WH; You, W; Liu, YX; Bai, BZ; Fang, HS

收藏 | 浏览/下载：1/0 | 提交时间：2017/06/15

novel air-cooled bainite steels nickel CCT diagrams artificial neural network Materials Science, Multidisciplinary Metallurgy & Metallurgical Engineering Mining & Mineral Processing

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