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浏览/检索结果: 共7条，第1-7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 Multi-scale analysis method of underwater polarization imaging 期刊论文 Acta Physica Sinica, 2018, 卷号: 67, 期号: 5, 页码: 10 作者:  Han, P. L.;  Liu, F.;  Zhang, G.;  Tao, Y.;  Shao, X. P. 收藏  |  浏览/下载：6/0  |  提交时间：2019/09/17 underwater imaging  polarization imaging  background scattering  target detection  recovery  visibility  system  Physics   Image motion velocity field model of space camera with large field and analysis on three-axis attitude stability of satellite 期刊论文 Guangxue Jingmi Gongcheng/Optics and Precision Engineering, 2016, 卷号: 24, 期号: 9 作者:  Lu, P.-L.;  Y.-C. Li;  L.-X. Jin;  G.-N. Li;  Y.-N. Wu and W.-H. Wang 收藏  |  浏览/下载：14/0  |  提交时间：2017/09/11 红外与可见光图像融合技术研究 学位论文 博士: 中国科学院大学, 2015 作者:  张蕾 收藏  |  浏览/下载：70/0  |  提交时间：2015/11/30 红外图像  可见光图像  图像融合  多尺度几何分析  结构相似度   大视场多光谱相机图像拼接与融合技术研究 学位论文 博士: 中国科学院大学, 2015 作者:  李新娥 收藏  |  浏览/下载：166/0  |  提交时间：2015/11/30 图像融合  图像配准  图像拼接  颜色传递   Fusion of infrared and visual images based on non-sampled Contourlet transform and region classification 期刊论文 Guangxue Jingmi Gongcheng/Optics and Precision Engineering, 2015, 卷号: 23, 期号: 3, 页码: 810-818 作者:  Zhang, L.;  L.-X. Jin;  S.-L. Han;  Z.-M. Lv and X.-E. Li 收藏  |  浏览/下载：12/0  |  提交时间：2016/08/24 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.   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：13/0  |  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.