中国科学院机构知识库网格系统: 检索

Dynamic multi-focus laser sculpting of freeform 3D glass microstructures 期刊论文 OAI收割
Optics and Lasers in Engineering, 2024, 卷号: 180
作者: Yao, Li; Xu, Kang; Huang, Lingyu; Huang, Peilin; Li, Zongyao

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收藏 | 浏览/下载：17/0 | 提交时间：2024/09/02

Dynamic multi-focus laser Three-dimensional glass microstructures Glass processing

AFCANet: An adaptive feature concatenate attention network for multi-focus image fusion 期刊论文 OAI收割
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES, 2023, 卷号: 35, 期号: 9, 页码: 22
作者: Liu, Shuaiqi; Peng, Weijian; Liu, Yali; Zhao, Jie; Su, Yonggang

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收藏 | 浏览/下载：22/0 | 提交时间：2023/11/15

Multi -focus image fusion Unsupervised training Adaptive feature concatenate Attention module

Multi-Perspective Long-Focus Camera Calibration Algorithm Based on Parallel Perspective Projection Model 会议论文 OAI收割
Beijing, China, 2023-07-25
作者: Ni, Siyu; Xue, Bin; Tao, Jinyou

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收藏 | 浏览/下载：5/0 | 提交时间：2024/02/07

Photogrammetry Multi-View Geometry Bundle Adjustment Affine Approximation Projection Model Long-Focus Camera Calibration

Multi-focus image fusion dataset and algorithm test in real environment 期刊论文 OAI收割
FRONTIERS IN NEUROROBOTICS, 2022, 卷号: 16, 页码: 8
作者: Liu, Shuaiqi; Peng, Weijian; Jiang, Wenjing; Yang, Yang; Zhao, Jie

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收藏 | 浏览/下载：35/0 | 提交时间：2022/12/27

image fusion multi-focus image fusion dataset image preprocessing multi-focus image fusion algorithm test real environment

Infrared and visual image fusion using LNSST and an adaptive dual-channel PCNN with triple-linking strength 期刊论文 OAI收割
Neurocomputing, 2018, 卷号: 310, 页码: 135-147
作者: Cheng, B. Y.; Jin, L. X.; Li, G. N.

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收藏 | 浏览/下载：31/0 | 提交时间：2019/09/17

LNSST ATD-PCNN Image fusion Singular value decomposition Auxiliary linking strength Triple-linking strength sparse representation shearlet transform multi-focus feature-extraction neural-network domain algorithm decomposition Computer Science

Multi-focus image fusion based on sparse feature matrix decomposition and morphological filtering 期刊论文 OAI收割
OPTICS COMMUNICATIONS, 2015, 卷号: 342, 页码: 1-11
作者: Li, Hui; Li, Li; Zhang, Jixiang

收藏 | 浏览/下载：30/0 | 提交时间：2016/03/30

Sparse matrix decomposition Multi-focus image fusion Feature extraction Morphological filtering

Data normalization of single camera visual measurement network system (EI CONFERENCE) 会议论文 OAI收割
2012 International Conference on Information Technology and Management Innovation, ICITMI 2012, November 10, 2012 - November 11, 2012, Guangzhou, China
作者: Zhang Y.-C.; Zhou J.

收藏 | 浏览/下载：22/0 | 提交时间：2013/03/25

With the development of visual measurement system Normalize the measured coordinates into the same world coordinate system Apply in the three coordinate measuring machine(CMM) to have the simulation experiment So the data normalization has the advantage of more high precision. (2013) Trans Tech Publications the visual measurement system of single camera which applied in the imaging theory of optical feature points then get the global data the result indicates that the maximum absolute tolerance between the normalization coordinates via the center of point set and the ones measured by CMM directly is 0.058mm Switzerland. it has been widespread used in the modern production. Due to the limit of the environment in scene and achieve the overall measurement the visual measurement system of single camera could not measure the shield between the measured objects each other. Focus on this problem But the one between the coordinate repeatedly measured at the position of one network control point is 0.066mm present a kind of the the knowledge of measurement network based on the visual measurement of single camera set up the measurement network system via the multi-control points. Measure the optical feature points in every network control point via the visual measurement system of single camera

Methods of Depth Measurement and Image Fusion Based on Multi-focus Micro-images 会议论文 OAI收割
Guiyang, 25-27 May 2013
作者: Yin YingJie; Wang, Xingang; Xu, De; Zhang, Zhengtao; Bai, Mingran

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收藏 | 浏览/下载：20/0 | 提交时间：2016/06/20

Depth Measurement Depth Of Field Image Fusion Micro-image Multi-focus Sharpness Evaluation Function

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：72/0 | 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 matching algorithm on statistical properties of Harris corner (EI CONFERENCE) 会议论文 OAI收割
2011 International Conference on Information and Automation, ICIA 2011, June 6, 2011 - June 8, 2011, Shenzhen, China
作者: He B.

收藏 | 浏览/下载：21/0 | 提交时间：2013/03/25

The fundamental goal of target recognition and video tracking is to match target template with source image. Most matching methods are based on image intensity or multi-feature points. And the latter method is more popular for its high accuracy and small calculation. Image Registration Based on Feature Points focus on effective feature extraction of image points and paradigm. Harris corner in the image rotation gray noise and viewpoint change conditions has an ideal match results is more recent application of one feature point. This paper extract the Harris corner deviation and covariance firstly experiments show that the two features exclusive then applied them to image registration for the first time. A set of actual images have shown this proposed method not only overcomes the complicated background gray uneven distribution problems but also pan and zoom the image has a good resistance. 2011 IEEE.