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长春光学精密机械与物... [3]
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会议论文 [3]
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2011 [1]
2010 [1]
2009 [1]
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内容类型:会议论文
专题:长春光学精密机械与物理研究所
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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.
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浏览/下载:34/0
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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.
Dynamic data fixing for IR target radiation characteristics (EI CONFERENCE)
会议论文
2010 International Conference on Computer, Mechatronics, Control and Electronic Engineering, CMCE 2010, August 24, 2010 - August 26, 2010, Changchun, China
Lihua C.
;
Juan C.
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浏览/下载:19/0
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提交时间:2013/03/25
The main methods to measure the target IR features are the following two ways: test and then measurement
or theoretical and simulation calculation. The former is direct measurement so the results are more accurate but the test is costly and complicated
so that it is limited in the application. Theoretical and simulation calculation are widely used in the target IR radiation calibration. Here we give the basic configuration of the calibration platform for measuring the IR radiation of the large area low temperature blackbody source
and discuss the calibration flowchart and key technologies for atmospheric transmission correction. The dynamic data are analyzed in real time to determine the IR radiation features in the data fusion subsystem
and the experimental results show that the calibration method is feasible and practical. 2010 IEEE.
Using bidirectional binary particle swarm optimization for feature selection in feature-level fusion recognition system (EI CONFERENCE)
会议论文
2009 4th IEEE Conference on Industrial Electronics and Applications, ICIEA 2009, May 25, 2009 - May 27, 2009, Xi'an, China
Wang D.
;
Ge W.
;
Wang Y.
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浏览/下载:16/0
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提交时间:2013/03/25
In feature-level fusion recognition system
the other is optimizing system sensor design to get outstanding cost performance. So feature selection become usually necessary to reduce dimensionality of the combination of multi-sensor features and improve system performance in system design. In general
there are two main missions. One is improving the recognition correct rate as soon as possible
the optimization is usually applied to feature selection because of its computational feasibility and validity. For further improving recognition accuracy and reducing selected feature dimensions
this paper presents a more rational and accurate optimization
Bidirectional Binary Particle Swarm Optimization (BBPSO) algorithm for feature selection in feature-level fusion target recognition system. In addition
we introduce a new evaluating function as criterion function in BBPSO feature selection method. At the last
we utilized Leave-One-Out method to validate the proposed method. The experiment results show that the proposed algorithm improves classification accuracy by two percentage points
while the selected feature dimensions are less one dimension than original Particle Swarm Optimization approach with 16 original feature dimensions. 2009 IEEE.
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