吉林大学学报(工学版) ›› 2014, Vol. 44 ›› Issue (3): 822-827.doi: 10.13229/j.cnki.jdxbgxb201403039

• 论文 • 上一篇    下一篇

基于稀疏表示的多类融合样本中特定目标识别

查长军1,2,韦穗1,杨海蓉3,丁大为1   

  1. 1.安徽大学 计算智能与信号处理教育部重点实验室,合肥 230039;
    2.合肥学院 机器视觉与智能控制技术重点实验室,合肥 230601;
    3.合肥师范学院 数学系,合肥 230061
  • 收稿日期:2013-02-26 出版日期:2014-03-01 发布日期:2014-03-01
  • 通讯作者: 韦穗(1946),女,教授,博士生导师.研究方向:压缩感知,图像处理与三维全息显示.E-mail:swei@ahu.edu.cn E-mail:zcj_longman@sina.com
  • 作者简介:查长军(1980),男,博士研究生.研究方向:光学成像与信号处理.E-mail:zcj_longman@sina.com
  • 基金资助:
    NSFC-广东联合基金项目(U1201255);高等学校博士学科点专项科研基金项目(20113401130001);国家自然科学青年基金项目(61201227);安徽省自然科学基金青年项目(1208085QF114);安徽省高校省级自然科学一般项目(KJ2011B131,KJ2013B224).

Special object recognition based on sparse representation in multiclass fusion sample

ZHA Chang-jun1,2,WEI Sui1,YANG Hai-rong3,DING Da-wei1   

  1. 1.Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education, Anhui University, Hefei 230039,China;
    2.Key Laboratory of Machine Vision and Intelligence Control Technology, Hefei University, Hefei 230601,China;
    3.Department of Mathematics, Hefei Normal University,Hefei 230061,China
  • Received:2013-02-26 Online:2014-03-01 Published:2014-03-01

RICH HTML

0   

PDF (PC)

573

摘要： 针对WSN及轮廓检测系统的特点,给出了一种基于WSN的数据处理方法。该方法首先通过主分量分析提取样本特征,然后采用叠加方式对不同样本的特征进行融合,给出数学模型;并以此模型为基础,提出一种新的基于稀疏表示的多类融合样本中特定目标识别算法,该算法根据超完备字典下主要非零系数的分布情况识别出特定目标;数值仿真与实验结果验证了本文算法的有效性,综合性能优于传统方法。

关键词: 信息处理技术, 无线传感器网络, 稀疏表示, 轮廓识别, 无人值守地面传感器

Abstract: According to the characteristics of Wireless Sensor Network (WSN) and the profile detecting system, a data processing method based on WSN is proposed. In this method, first, the sample features are extracted by principal component analysis; then, the features of different samples were fused using accumulate mode. A mathematical model was given and on the basis of this model, a novel algorithm of special object recognition based on sparse representation in multiclass fusion sample was proposed. This algorithm recognizes the special target according to distribution of the main non-zero coefficients under an over-complete dictionary. Numerical simulation and experimental results demonstrate the effectiveness of the proposed algorithm, the comprehensive performance is better than the traditional methods.

Key words: information processing, wireless sensor network, sparse representation, profiling recognition, unattended ground sensor

中图分类号: 

  • TN911.74
[1] Russomanno David J, Chari Srikan, Emmanuel Kenny, et al. Testing and evaluation of profiling sensors for perimeter security[J]. ITEA, 2010,31(1):121-130.
[2] Sartain Ronald B, Aliberti Keith, Alexander Troy, et al. Long-wave infrared profile feature extractor (PFx) sensor[C]∥Proc of SPIE, Unattended Ground, Sea, and Air Sensor Technologies and Applications XI,Orlando, FL,2009.
[3] Russomanno David J, Chari Srikant, Jacobs Eddie L,et al. Near-IR sparse detector sensor for intelligent electronic fence applications[J]. IEEE Sensors Journal, 2010, 10(6):1106-1107.
[4] Chari Srikant, Halford Carl, Jacobs Eddie,et al. Classification of humans and animals using an infrared profiling sensor[C]∥Proc of SPIE, Unattended Ground, Sea, and Air Sensor Technologies and Applications XI,Orlando, FL,2009.
[5] Russomanno David, Chari Srikan, Halford Carl. Sparse detector imaging sensor with two-class silhouette classification[J]. Sensors, 2008(8):7996-8015.
[6] Reynolds R K, Chari S, Russomanno D J. Embedded real-time classifier for profiling sensors and custom detector configuration[C]∥Proc of SPIE, Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR II,2011.
[7] 查长军,孙南,张成,等. 基于稀疏表示的特定目标识别[J].吉林大学学报:工学版,2013,43(1):256-260.
Zha Chang-jun, Sun Nan, Zhang Cheng,et al. Special object recognition based on sparse representation[J].Journal of Jilin University (Engineering and Technology Edition), 2013,43(1):256-260.
[8] Chen S, Donoho D, Saunders M. Atomic decomposition by basis pursuit[J]. SIAM Review, 2001, 43(1):129-159.
[9] Li Yu-long, Feng Ju-fu. Sparse represention shape model[C]∥Proceedings of 2010 IEEE 17th ICIP, 2010:2733-2736.
[10] Wright John, Yang Allen Y, Ganesh Arvind, et al. Robust face recognition via sparse representation[J]. IEEE Transactions, PAMI, 2009, 31(2):1-18.
[11] Yang Jian,Zhang David,Frangi Alejandro F, et al. Two-dimensional PCA: a new approach to appearance-based face representation and recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004,26(1):131-137.
[12] Russomanno D, Yeasin M, Jacobs E, et al. Sparse detector sensor: profiling experiments for broad-scale classification[C]∥Proc of SPIE, Unattended Ground, Sea, and Air Sensor Technologies and Applications XI,Orlando, FL,2008.
[13] Donoho D L, Elad M, Temlyakov V N. Stable recovery of sparse overcomplete representations in the presence of noise[J]. IEEE Transactions on Information Theory, 2006,52(1):6-18.
[14] Rubinstein R, Bruckstein A M, Elad M. Dictionaries for sparse representation modeling[J]. Proceedings of the IEEE, 2010, 98(6): 1045-1057.
[15] Smith Leslie N, Elad Michael. Improving dictionary learning: multiple dictionary updates and coefficient reuse[J]. IEEE Signal Processing Letters, 2013, 20(1):79-82.
[16] Hastie Trevor, Tibshirani Robert, Friedman Jerome. The Elements of Statistical Learning[M]. New York:Springer-Verlag, 2001:14-18.
[1] 苏寒松,代志涛,刘高华,张倩芳. 结合吸收Markov链和流行排序的显著性区域检测[J]. 吉林大学学报(工学版), 2018, 48(6): 1887-1894.
[2] 徐岩,孙美双. 基于卷积神经网络的水下图像增强方法[J]. 吉林大学学报(工学版), 2018, 48(6): 1895-1903.
[3] 黄勇,杨德运,乔赛,慕振国. 高分辨合成孔径雷达图像的耦合传统恒虚警目标检测[J]. 吉林大学学报(工学版), 2018, 48(6): 1904-1909.
[4] 李居朋,张祖成,李墨羽,缪德芳. 基于Kalman滤波的电容屏触控轨迹平滑算法[J]. 吉林大学学报(工学版), 2018, 48(6): 1910-1916.
[5] 应欢,刘松华,唐博文,韩丽芳,周亮. 基于自适应释放策略的低开销确定性重放方法[J]. 吉林大学学报(工学版), 2018, 48(6): 1917-1924.
[6] 陆智俊,钟超,吴敬玉. 星载合成孔径雷达图像小特征的准确分割方法[J]. 吉林大学学报(工学版), 2018, 48(6): 1925-1930.
[7] 刘仲民,王阳,李战明,胡文瑾. 基于简单线性迭代聚类和快速最近邻区域合并的图像分割算法[J]. 吉林大学学报(工学版), 2018, 48(6): 1931-1937.
[8] 单泽彪,刘小松,史红伟,王春阳,石要武. 动态压缩感知波达方向跟踪算法[J]. 吉林大学学报(工学版), 2018, 48(6): 1938-1944.
[9] 董颖, 崔梦瑶, 吴昊, 王雨后. 基于能量预测的分簇可充电无线传感器网络充电调度[J]. 吉林大学学报(工学版), 2018, 48(4): 1265-1273.
[10] 姚海洋, 王海燕, 张之琛, 申晓红. 双Duffing振子逆向联合信号检测模型[J]. 吉林大学学报(工学版), 2018, 48(4): 1282-1290.
[11] 全薇, 郝晓明, 孙雅东, 柏葆华, 王禹亭. 基于实际眼结构的个性化投影式头盔物镜研制[J]. 吉林大学学报(工学版), 2018, 48(4): 1291-1297.
[12] 陈绵书, 苏越, 桑爱军, 李培鹏. 基于空间矢量模型的图像分类方法[J]. 吉林大学学报(工学版), 2018, 48(3): 943-951.
[13] 陈涛, 崔岳寒, 郭立民. 适用于单快拍的多重信号分类改进算法[J]. 吉林大学学报(工学版), 2018, 48(3): 952-956.
[14] 孟广伟, 李荣佳, 王欣, 周立明, 顾帅. 压电双材料界面裂纹的强度因子分析[J]. 吉林大学学报(工学版), 2018, 48(2): 500-506.
[15] 刘洲洲, 彭寒. 基于节点可靠度的无线传感器网络拓扑控制算法[J]. 吉林大学学报(工学版), 2018, 48(2): 571-577.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
版权所有 © 《吉林大学学报(工学版)》编辑部
地址：长春市人民大街5988号　电话：+86-431-85095297　传真：+86-431-85094074　E-mail: xbgxb@jlu.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发