中国科学院机构知识库网格系统: 检索

Infrared Small-Target Detection Based on Radiation Characteristics with a Multimodal Feature Fusion Network 期刊论文 OAI收割
Remote Sensing, 2022, 卷号: 14, 期号: 15, 页码: 31
作者: D. Wu; L. H. Cao; P. J. Zhou; N. Li; Y. Li and D. J. Wang

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收藏 | 浏览/下载：5/0 | 提交时间：2023/06/14

EACOFT: An energy-aware correlation filter for visual tracking 期刊论文 OAI收割
Pattern Recognition, 2021, 卷号: 112
作者: Q. Liu; J. Ren; Y. Wang; Y. Wu; H. Sun and H. Zhao

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收藏 | 浏览/下载：8/0 | 提交时间：2022/06/13

Fast contour torque features based recognition in laser active imaging system 期刊论文 OAI收割
Optik, 2015, 卷号: 126, 期号: 21, 页码: 3276-3282
作者: Wang, C. J.; T. Sun; T. F. Wang and J. Chen

收藏 | 浏览/下载：9/0 | 提交时间：2016/07/15

实时图像增强研究及其系统设计 学位论文 OAI收割
硕士: 中国科学院大学, 2015
作者: 张辽

收藏 | 浏览/下载：25/0 | 提交时间：2016/04/11

图像增强实时性抗噪性动态增强目标识别

The research of multi-frame target recognition based on laser active imaging 会议论文 OAI收割
5th International Symposium on Photoelectronic Detection and Imaging, ISPDI 2013, June 25, 2013 - June 27, 2013, Beijing, China, June 25, 2013 - June 27, 2013
作者: Wang T.-F.; Sun T.; Chen J.

收藏 | 浏览/下载：9/0 | 提交时间：2014/05/15

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：29/0 | 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.