中国科学院机构知识库网格系统: 检索

Multi-focus image fusion algorithm based on adaptive PCNN and wavelet transform (EI CONFERENCE) 会议论文 OAI收割
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：77/0 | 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.

Research on station mode for attitude determination of multiple targets (EI CONFERENCE) 会议论文 OAI收割
2010 International Conference on Computational and Information Sciences, ICCIS2010, December 17, 2010 - December 19, 2010, Chengdu, Sichuan, China
作者: Zhao L.; Wang J.; Li M.

收藏 | 浏览/下载：38/0 | 提交时间：2013/03/25

A key problem of attitude determination system is how to locate CMOS cameras and deal with relations among them. In this paper several kinds of multi-CMOS station mode are introduced. Fundamental principles and classification of station mode directly affect on measurement parameters of system. No matter what mode is designed the ultimate goal is to extend Effective Viewing Field (EVF) increase capture ratio of objects and increase measurement precision. Station mode introduced in this paper is made of CMOS cameras because of low cost high speed frame frequency and low power consumption CMOS cameras were putted near from object tested. So sequences of image are legible which are convenient for analysis of three dimensional(3D) attitude. 2010 IEEE.

CAPS satellite spread spectrum communication blind multi-user detecting system based on chaotic sequences 期刊论文 OAI收割
SCIENCE IN CHINA SERIES G-PHYSICS MECHANICS & ASTRONOMY, 2009, 卷号: 52, 期号: 3, 页码: 339-345
作者: Lei LiHua; Shi HuLi; Ma GuanYi

收藏 | 浏览/下载：21/0 | 提交时间：2017/04/18

Multiple Path Interference (MPI) Multiple Access Interference (MAI) Chebyshev map chaotic sequences blind multi-user detection

Classification and counting on multicontinued fractions and its application to multi-sequences 期刊论文 iSwitch采集
Science in china series f-information sciences, 2007, 卷号: 50, 期号: 3, 页码: 351-358
作者: Dai ZongDuo; Feng XiuTao

收藏 | 浏览/下载：18/0 | 提交时间：2019/05/10

Multi-strict continued fractions Multi-sequences Linear complexity distribution

Multi-continued fraction algorithm and generalized b-m algorithm over f-q 期刊论文 iSwitch采集
Finite fields and their applications, 2006, 卷号: 12, 期号: 3, 页码: 379-402
作者: Dai, Zongduo; Yang, Junhui

收藏 | 浏览/下载：31/0 | 提交时间：2019/05/10

Multi-continued fraction Generalized berlekamp-massey algorithm Multi-sequences over finite fields

L-p ([0,1])-characterizations of multi-knot piecewise linear spectral sequences 期刊论文 iSwitch采集
Progress in natural science, 2006, 卷号: 16, 期号: 7, 页码: 684-690
作者: Lian Qiaofang; Cheng Linfeng; Yan Dunyan

收藏 | 浏览/下载：32/0 | 提交时间：2019/05/10

Exponential bases Multi-knot piecewise linear spectral sequences Unconditional bases

multi-continued fraction algorithm and generalized b-m algorithm over f-q 期刊论文 OAI收割
FINITE FIELDS AND THEIR APPLICATIONS, 2006, 卷号: 12, 期号: 3, 页码: 379-402
Dai ZD; Yang JH

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收藏 | 浏览/下载：20/0 | 提交时间：2011/07/13

multi-continued fraction generalized Berlekamp-Massey algorithm multi-sequences over finite fields

multi-continued fraction algorithm and generalized b-m algorithm over f-q 期刊论文 OAI收割
FINITE FIELDS AND THEIR APPLICATIONS, 2006, 卷号: 12, 期号: 3, 页码: 379-402
Dai ZD; Yang JH

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收藏 | 浏览/下载：13/0 | 提交时间：2011/07/13

multi-continued fraction generalized Berlekamp-Massey algorithm multi-sequences over finite fields

multi-continued fraction algorithm and generalized b-m algorithm over f-q 期刊论文 OAI收割
FINITE FIELDS AND THEIR APPLICATIONS, 2006, 卷号: 12, 期号: 3, 页码: 379-402
Dai ZD; Yang JH

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收藏 | 浏览/下载：15/0 | 提交时间：2011/07/13

multi-continued fraction generalized Berlekamp-Massey algorithm multi-sequences over finite fields

Multi-sequences with d-perfect property 期刊论文 iSwitch采集
Journal of complexity, 2005, 卷号: 21, 期号: 2, 页码: 230-242
作者: Feng, XT; Wang, QL; Dal, ZD

收藏 | 浏览/下载：16/0 | 提交时间：2019/05/10

Multi-sequences Linear complexity profile D-perfect M-continued fraction