李云松
博士 教授 博士生导师
西安电子科技大学 通信工程学院
图像视频压缩编码、图像处理、高性能计算、芯片设计。
个性化签名
- 姓名:李云松
- 目前身份:在职研究人员
- 担任导师情况:博士生导师
- 学位:博士
-
学术头衔:
博士生导师, 教育部“新世纪优秀人才支持计划”入选者
- 职称:高级-教授
-
学科领域:
通信技术
- 研究兴趣:图像视频压缩编码、图像处理、高性能计算、芯片设计。
李云松,西安电子科技大学教授,博导。分别于1996年和1999年在西安电子科技大学获得图像传输与处理专业学士学位和通信与信息系统专业硕士学位。并于2002年12月在西安电子科技大学获得信号与信息处理专业博士学位。2008年10月-2009年1月在美国威斯康辛大学麦迪逊分校作访问学者。2009年入选教育部新世纪优秀人才计划。2012年获国家优秀青年科学基金项目资助。2014年入选科技部中青年科技创新领军人才。目前是西安电子科技大学空天研究院副院长、通信与信息系统学术带头人、通信与信息系统博士生导师。
研究方向:1.图像/视频处理、编码及传输 2.芯片设计 3.高性能计算。以第一完成人获2010年度国防科学技术进步二等奖、2011年度教育部科技进步一等奖、2012年度国家科技进步二等奖。授权发明专利15项;发表SCI检索论文10篇、EI检索论文60篇。
兼任:绕月探测工程科学应用专家委员会委员;ISN国家重点实验室图像编码与处理研究中心主任;中国宇航学会深空探测技术专业委员会委员;中国仪器仪表学会空间仪器分会常务理事;陕西省图形图像学会常务理事;SPIE 国际会议卫星数据压缩分会联合主席。
-
主页访问
80
-
关注数
0
-
成果阅读
278
-
成果数
7
【期刊论文】Reconstruction of hyperspectral image using matting model for classification
Optical Engineering ,2016,55(5):053104
2016年05月09日
Although hyperspectral images (HSIs) captured by satellites provide much information in spectral regions, some bands are redundant or have large amounts of noise, which are not suitable for image analysis. To address this problem, we introduce a method for reconstructing the HSI with noise reduction and contrast enhancement using a matting model for the first time. The matting model refers to each spectral band of an HSI that can be decomposed into three components, i.e., alpha channel, spectral foreground, and spectral background. First, one spectral band of an HSI with more refined information than most other bands is selected, and is referred to as an alpha channel of the HSI to estimate the hyperspectral foreground and hyperspectral background. Finally, a combination operation is applied to reconstruct the HSI. In addition, the support vector machine (SVM) classifier and three sparsity-based classifiers, i.e., orthogonal matching pursuit (OMP), simultaneous OMP, and OMP based on first-order neighborhood system weighted classifiers, are utilized on the reconstructed HSI and the original HSI to verify the effectiveness of the proposed method. Specifically, using the reconstructed HSI, the average accuracy of the SVM classifier can be improved by as much as 19%.
无
0
-
55浏览
-
0点赞
-
0收藏
-
0分享
-
0下载
-
0评论
-
引用
【期刊论文】Optimizing extreme learning machine for hyperspectral image classification
Journal of Applied Remote Sensing ,2015,9(1):097296
2015年03月02日
Extreme learning machine (ELM) is of great interest to the machine learning society due to its extremely simple training step. Its performance sensitivity to the number of hidden neurons is studied under the context of hyperspectral remote sensing image classification. An empirical linear relationship between the number of training samples and the number of hidden neurons is proposed. Such a relationship can be easily estimated with two small training sets and extended to large training sets to greatly reduce computational cost. The kernel version of ELM (KELM) is also implemented with the radial basis function kernel, and such a linear relationship is still suitable. The experimental results demonstrated that when the number of hidden neurons is appropriate, the performance of ELM may be slightly lower than the linear SVM, but the performance of KELM can be comparable to the kernel version of SVM (KSVM). The computational cost of ELM and KELM is much lower than that of the linear SVM and KSVM, respectively.
无
0
-
34浏览
-
0点赞
-
0收藏
-
0分享
-
0下载
-
0评论
-
引用
【期刊论文】PCNN-based level set method of automatic mammographic image segmentation
Optik,2016,127(4):1644-1650
2016年02月01日
A novel approach to mammographic image segmentation, termed as PCNN-based level set algorithm, is presented in this paper. As well known, it is difficult to robustly achieve mammogram image segmentation due to low contrast between normal and lesion tissues. Therefore, Pulse Coupled Neural Network (PCNN) algorithm is firstly employed to achieve mammary-specific and mass edge detection for subsequently extracting contour as the initial zero level set. The proposed scheme accurately obtains the initial contour for level set evolution, which does not suffer from the drawback that level set method is sensitive to the initial contour. Especially, an improved level set evolution is performed to segment the images and get the final results. A preliminary evaluation of the proposed method performs on a known public database, namely MIAS, which demonstrates that the proposed framework in this paper can potentially obtain better masses detection results than traditional CV and VFC model in terms of accuracy.
Mammographic image Image segmentation Pulse coupled neural network Level set method
0
-
39浏览
-
0点赞
-
0收藏
-
0分享
-
0下载
-
0评论
-
引用
【期刊论文】Study of multi-feature fusion methods for distribution fields in object tracking
Xi'an Dianzi Keji Daxue Xuebao/Journal of Xidian University,2015,42(4):1-7
2015年08月26日
In order to improve the robustness of the distribution fields(DF)as an object model in object tracking,we propose a mutli-feature fusion framework for the distribution fields.In the original DF-based method,the density histogram was used to estimate the DF of a pixel,but the structural information was ignored.For effective representation of the structural information in the DFs,a special type of coding for the featured points which contain structural information is merged into the DFs.Experiments show that the new method outperforms the original method and four other state-of-the-art tracking algorithms for some challenging video clips.
object tracking multi-feature fusion distribution fields edge detection feature points object model
0
-
49浏览
-
0点赞
-
0收藏
-
0分享
-
0下载
-
0评论
-
引用
【期刊论文】Block-based two-dimensional wavelet transform running on graphics processing unit
IET Computers & Digital Techniques ,2014,8(5):229 – 236
2014年07月04日
This study explores the use of the graphics processing units (GPUs) for performing the two-dimensional discrete wavelet transform (DWT) of images. The study of fast wavelet transforms has been driven both by the enormous volumes of data produced by modern cameras and by the need for real-time processing of these data. With the emergence of general computing on GPUs, many time-consuming applications have started to reap the associated benefits. In the implementation of a GPU-based DWT, two approaches are used according to the published works, which are the row–column (RC) approach and the block-based (BB) approach. Most state-of-the-art techniques are based on the RC approach, which utilises the parallelism between different rows and columns; few works are based on the BB approach, which explores the parallelism between different blocks of the image. Although easy to implement, resource usage of the RC approach is usually related to the image size. Another shortcoming of the RC approach lies in the fact, according to the author's analysis, that more global memory access is required. The authors thus select the BB approach in this study. Experiment results show that the proposed BB approach outperforms the RC approach, being 99× faster than a native CPU implementation for 4096 × 4096 images.
无
0
-
40浏览
-
0点赞
-
0收藏
-
0分享
-
0下载
-
0评论
-
引用
【期刊论文】Hyperspectral image reconstruction by deep convolutional neural network for classification
Pattern Recognition,2017,63():371-383
2017年03月01日
Spatial features of hyperspectral imagery (HSI) have gained an increasing attention in the latest years. Considering deep convolutional neural network (CNN) can extract a hierarchy of increasingly spatial features, this paper proposes an HSI reconstruction model based on deep CNN to enhance spatial features. The framework proposes a new spatial features-based strategy for band selection to define training label with rich information for the first time. Then, hyperspectral data is trained by deep CNN to build a model with optimized parameters which is suitable for HSI reconstruction. Finally, the reconstructed image is classified by the efficient extreme learning machine (ELM) with a very simple structure. Experimental results indicate that framework built based on CNN and ELM provides competitive performance with small number of training samples. Specifically, by using the reconstructed image, the average accuracy of ELM can be improved as high as 30.04%, while performs tens to hundreds of times faster than those state-of-the-art classifiers.
Spatial features of hyperspectral imagery (, HSI), have gained an increasing attention in the latest years., Considering deep convolutional neural network (, CNN), can extract a hierarchy of increasingly spatial features,, this paper proposes an HSI reconstruction model based on deep CNN to enhance spatial features., The framework proposes a new spatial features-based strategy for band selection to define training label with rich information for the first time., Then,, h
0
-
32浏览
-
0点赞
-
0收藏
-
0分享
-
0下载
-
0评论
-
引用
【期刊论文】Breast mass classification in digital mammography based on extreme learning machine
Neurocomputing,2016,173(3):930-941
2016年01月15日
This paper presents a novel computer-aided diagnosis (CAD) system for the diagnosis of breast cancer based on extreme learning machine (ELM). In view of a mammographic image, it is first eliminated interference in the preprocessing stages. Then, the preprocessed images are segmented by the level set model we proposed. Subsequently, a model of multidimensional feature vectors is built. Since not every feature vector contributes to the improvement of performance, feature selection is done by the combination of support vector machine (SVM) and extreme learning machine (ELM). Finally, an optimal subset of feature vectors is inputted into the classifiers for distinguishing malignant masses from benign ones. We also compare our breast mass classification approach based on ELM with several state-of-the-art classification models, and the results show that the proposed CAD system not only has good performance in terms of specificity, sensitivity and accuracy, but also achieves a significant reduction in training time compared with SVM and particle swarm optimization-support vector machine (PSO-SVM). Ultimately, our system achieves the better performance with average accuracy of 96.02% which indicates that the proposed segmentation model, the utilization of selected feature vectors and the effective classifier ELM provide satisfactory system.
Mammography CAD Level set method Feature selection Extreme learning machine Support vector machine
0
-
29浏览
-
0点赞
-
0收藏
-
0分享
-
0下载
-
0评论
-
引用