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CORC

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Multi-Discriminator Generative Adversarial Network for High Resolution Gray-Scale Satellite Image Colorization 会议论文
Valencia, Spain, 22-27 July 2018
作者:  Li FM(李非墨);  Ma L(马雷);  Cai J(蔡健)
收藏  |  浏览/下载：38/0  |  提交时间：2019/10/03
Sea-land segmentation for infrared remote sensing images based on superpixels and multi-scale features 期刊论文
INFRARED PHYSICS & TECHNOLOGY, 2018, 卷号: 91, 页码: 12-17
作者:  Lei, Sen;  Zou, Zhengxia;  Liu, Dunge;  Xia, Zhenghuan;  Shi, Zhenwei
收藏  |  浏览/下载：7/0  |  提交时间：2019/12/30
A convolutional neural network based classifier for uncompressed malware samples 会议论文
Proceedings of the ACM Conference on Computer and Communications Security
作者:  Yang, C.;  Wen, Y.;  Guo, J.;  Song, H.;  Li, L.
收藏  |  浏览/下载：5/0  |  提交时间：2019/12/30
Improvement of Mura based on the maximum/minimum bilinear interpolation method 期刊论文
Guangzi Xuebao/Acta Photonica Sinica, 2016, 卷号: 45
作者:  Liang, Zhi-Hu;  Zhang, Xiao-Ning;  Yue, Jun-Feng;  Tu, Zhen-Tao;  Huang, Tai-Jun
收藏  |  浏览/下载：8/0  |  提交时间：2019/11/26
Apple tree branch segmentation from images with small gray-level difference for agricultural harvesting robot 期刊论文
OPTIK, 2016, 卷号: 127, 期号: 23, 页码: 11173-11182
作者:  Ji, Wei[1];  Qian, Zhijie[2];  Xu, Bo[3];  Tao, Yun[4];  Zhao, Dean[5]
收藏  |  浏览/下载：5/0  |  提交时间：2019/12/24
An information hiding algorithm of gray images based on suboptimum weighted matrix 期刊论文
Sichuan Daxue Xuebao (Gongcheng Kexue Ban)/Journal of Sichuan University (Engineering Science Edition), 2015, 卷号: 47, 期号: [db:dc_citation_issue], 页码: 139-143
作者:  Peng, Zhenlong;  Gui, Xiaolin;  An, Jian;  Ji, Yali;  Guo, Jianhong
收藏  |  浏览/下载：3/0  |  提交时间：2019/12/02
Expression recognition algorithm based on local directional binary pattern 期刊论文
Journal of Computational Information Systems, 2014, 卷号: 10, 期号: 8, 页码: 3221-3228
作者:  Wang, Yan;  He, Guoqing
收藏  |  浏览/下载：25/0  |  提交时间：2020/11/14
A Novel Visual Classification Method of Seabed Sediments 会议论文
OCEANS'14 MTS/IEEE St. John's, St. John's, Canada, September 14-19, 2014
作者:  Li Y(李岩);  Xia, Chunlei;  Zhu PQ(祝普强);  Huang Y(黄琰);  Ge LY(葛利亚)
收藏  |  浏览/下载：21/0  |  提交时间：2014/12/29
An improved differential box-counting method to estimate fractal dimensions of gray-level images 期刊论文
JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION, 2014, 卷号: 25, 页码: 1102-1111
作者:  Liu, Yu;  Chen, Lingyu;  Wang, Heming;  Jiang, Lanlan;  Zhang, Yi
收藏  |  浏览/下载：8/0  |  提交时间：2019/12/09
A line mapping based automatic registration algorithm of infrared and visible images 会议论文
5th International Symposium on Photoelectronic Detection and Imaging (ISPDI) - Infrared Imaging and Applications, Beijing, June 25-27, 2013
作者:  Ai R(艾锐);  Shi ZL(史泽林);  Xu DJ(徐德江);  Zhang CS(张程硕)
收藏  |  浏览/下载：25/0  |  提交时间：2013/12/26
There exist complex gray mapping relationships among infrared and visible images because of the different imaging mechanisms. The difficulty of infrared and visible image registration is to find a reasonable similarity definition. In this paper, we develop a novel image similarity called implicit linesegment similarity(ILS) and a registration algorithm of infrared and visible images based on ILS. Essentially, the algorithm achieves image registration by aligning the corresponding line segment features in two images. First, we extract line segment features and record their coordinate positions in one of the images, and map these line segments into the second image based on the geometric transformation model. Then we iteratively maximize the degree of similarity between the line segment features and correspondence regions in the second image to obtain the model parameters. The advantage of doing this is no need directly measuring the gray similarity between the two images. We adopt a multi-resolution analysis method to calculate the model parameters from coarse to fine on Gaussian scale space. The geometric transformation parameters are finally obtained by the improved Powell algorithm. Comparative experiments demonstrate that the proposed algorithm can effectively achieve the automatic registration for infrared and visible images, and under considerable accuracy it makes a more significant improvement on computational efficiency and anti-noise ability than previously proposed algorithms.  

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