中国开放科研知识云: 检索

The real-time Complex cruise scene motion detection system Based on DSP 会议论文
International Symposium on Optoelectronic Technology and Application 2014: Image Processing and Pattern Recognition, IPTA 2014, May 13, 2014 - May 15, 2014, Beijing, China
Wu Z.-G.; Wang M.-J.

收藏 | 浏览/下载：11/0 | 提交时间：2015/04/27

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

收藏 | 浏览/下载：9/0 | 提交时间：2014/05/15

Super resolution optical image restoration for ground-based large telescope 会议论文
8th Symposium on Multispectral Image Processing and Pattern Recognition, MIPPR 2013, October 26, 2013 - October 27, 2013, Wuhan, China
Zhang S.

收藏 | 浏览/下载：15/0 | 提交时间：2014/05/15

Precision laser emission technology based on fast steering mirror for space optical communication 会议论文
2013 2nd International Conference on Measurement, Instrumentation and Automation, ICMIA 2013, April 23, 2013 - April 24, 2013, Guilin, China
Jiang Z. H.; Wang T. F.; Sun T.

收藏 | 浏览/下载：11/0 | 提交时间：2014/05/15

Optical design of zoom hyper spectral system 会议论文
Asia Pacific Conference on Optics Manufacture 2012, APCOM 2012, August 26, 2012 - August 28, 2012, Changchun, China
Liu Z.; Cui C.; Song Y.

收藏 | 浏览/下载：10/0 | 提交时间：2014/05/15

A fast target recognition algorithm based on MSA and MSR (EI CONFERENCE) 会议论文
2012 International Conference on Industrial Control and Electronics Engineering, ICICEE 2012, August 23, 2012 - August 25, 2012, Xi'an, China
Huang J.; Wang Y.; Ji H.; Liu G.

收藏 | 浏览/下载：23/0 | 提交时间：2013/03/25

This paper presents a new fast target recognition algorithm the proposed method is based on Multi-scale Auto convolution(MSA) and Multi-scale Retinex(MSR). As shown by the comparison with original MSA it appears that this new technique solves the problem that MSA algorithm is sensitive to illumination and the computational load is significantly reduced to 1/8th of that of the original MSA algorithm it is also robust to affine transform light projective transform noise thin fog occlusion and illumination change. the performed experiments show that it has fast searching speed and can accurately recognize and locate target in real scenes. 2012 IEEE.

Efficient human action recognition using accumulated motion image and support vector machines (EI CONFERENCE) 会议论文
International Workshop on Advanced Computational Intelligence and Intelligent Informatics, IWACIII 2011, November 19, 2011 - November 23, 2011, Suzhou, China
Cao W.; Zhang X.; Cao S.; Zhang J.; Wang M.; Han G.

收藏 | 浏览/下载：66/0 | 提交时间：2013/03/25

Vision-based human action recognition provides an advanced interface and research in this field of human action recognition has been actively carried out. This paper describes a scheme for recognizing human actions from a video sequences. The proposed method is an extension of the Motion History Image(MHI) method based on the ordinal measure of accumulated motion which is robust to variations of appearances. We define the accumulated motion image(AMI) using image differences firstly. Then the AMI of the video sequencesis resized to a MN regulation following the standard of training phases. Finally we employ Support Vector Machine(SVM) as a classifier to distinguish the current activity in target video sequences. In a word our proposed algorithm not only outperforms the state of art on public available KTH data set and Weizmann data set but also proves practical to some real world applications in addition this method is computationally simple and able to achieve a satisfactory accuracy.

Design for target classifier based on semi-supervised learning (EI CONFERENCE) 会议论文
2011 International Conference on Electric Information and Control Engineering, ICEICE 2011, April 15, 2011 - April 17, 2011, Wuhan, China
Jiangrui K.

收藏 | 浏览/下载：18/0 | 提交时间：2013/03/25

The target classifier is an ingredient of the target recognition system. In order to achieve the automation and computerization of target recognition a method for training target classifier based on semi-supervised learning is provided. It adopts CFS algorithm for dada feature selection and uses semi-supervised learning algorithm Co-training to construct the target classifiers. The final classifier was produced through integration learning method. Experimental results show that the performance of the target classifier based on semi-supervised learning trained is superior to the traditional target classifier. 2011 IEEE.

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 matching algorithm on statistical properties of Harris corner (EI CONFERENCE) 会议论文
2011 International Conference on Information and Automation, ICIA 2011, June 6, 2011 - June 8, 2011, Shenzhen, China
He B.; Ming Z.; Wei Y.

收藏 | 浏览/下载：18/0 | 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.

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