QQ客服 官方微博 反馈留言

浏览/检索结果: 共5条，第1-5条 帮助 限定条件 专题：长春光学精密机械与物理研究所    第一署名单位    第一作者单位    通讯作者单位     已选(0)清除 条数/页：5101520253035404550556065707580859095100   排序方式：请选择作者升序作者降序题名升序题名降序发表日期升序发表日期降序提交时间升序提交时间降序 Fusing Appearance and Prior Cues for Road Detection 期刊论文 Applied Sciences-Basel, 2019, 卷号: 9, 期号: 5, 页码: 15 作者:  F.L.Ren;  X.He;  Z.H.Wei;  L.Zhang;  J.W.He 收藏  |  浏览/下载：1/0  |  提交时间：2020/08/24 road detection,appearance,geometric prior,superpixel,illuminant,invariant,GMM,Chemistry,Engineering,Materials Science,Physics   Background Subtraction Using Multiscale Fully Convolutional Network 期刊论文 Ieee Access, 2018, 卷号: 6, 页码: 16010-16021 作者:  Zeng, D. D.;  Zhu, M. 收藏  |  浏览/下载：5/0  |  提交时间：2019/09/17 Background subtraction  convolutional neural network  multiscale fully  convolutional network  video surveillance  density-estimation  Computer Science  Engineering  Telecommunications   功能磁共振成像温度觉刺激装置的设计与验证 学位论文 硕士: 中国科学院大学, 2015 作者:  董涛 收藏  |  浏览/下载：24/0  |  提交时间：2016/04/11 功能磁共振成像(fMRI)  刺激装置  LabVIEW  温度刺激   Quaternion moment and its invariants for color object classification 期刊论文 Information Sciences, 2014, 期号: 273, 页码: 132-143 Guo L. Q.; Dai M.; Zhu M. 收藏  |  浏览/下载：16/0  |  提交时间：2015/04/24 A novel starting-point-independent wavelet coefficient shape matching (EI CONFERENCE) 会议论文 ICO20: Optical Information Processing, August 21, 2005 - August 26, 2005, Changchun, China Hu S.; Zhu M.; Wu C.; Song H.-J. 收藏  |  浏览/下载：12/0  |  提交时间：2013/03/25 In many computer vision tasks  in order to improve the accuracy and robustness to the noise  wavelet analysis is preferred for the natural multi-resolution property. However  the wavelet representation suffers from the dependency of the starting point of the sampled contour. For overcoming the problem that the wavelet representation depends on the starting point of the sampled contour  the Zernike moments are introduced  and a novel Starting-Point-lndependent wavelet coefficient shape matching algorithm is presented. The proposed matching algorithm firstly gains the object contours  and give the translation and scale invariant object shape representation. The object shape representation is converted to the dyadic wavelet representation by the wavelet transform. And then calculate the Zernike moments of wavelet representation in different scales. With respect to property of rotation invariant of Zernike moments  consider the Zernike moments as the feature vector to calculate the dissimilarity between the object and template image  which overcoming the problem of dependency of starting point. The experimental results have proved the proposed algorithm to be efficient  precise  and robust.