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| 基于稀疏表示的特定目标识别 | |
| 引用本文： | 查长军,孙南,张成,韦穗.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. |
| 作者姓名： | 查长军 孙南 张成 韦穗 |
| 作者单位： | 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;合肥学院机器视觉与智能控制技术重点实验室,合肥230601 2. 中国人民解放军73101部队,江苏徐州,221008 3. 安徽大学计算智能与信号处理教育部重点实验室,合肥,230039 |
| 基金项目： | 高等学校博士学科点专项科研基金项目(20113401130001) |
| 摘 要： | 针对轮廓检测系统输出采样信号的特点,结合稀疏表示及主成分分析理论,提出了一种基于稀疏表示的特定目标识别方法。该方法首先通过主成分分析提取采样信号的主要成分以消除冗余信息,同时将信号转换为相同维数的特征向量,然后将特征向量投影到低维空间构造出字典,通过该字典对测试信号进行稀疏表示、识别。数值仿真与现场实验结果表明:该方法在低维空间下具有很好的识别效果;并结合实际情况,对有损坏传感器的系统进行测试,结果表明本文方法具有较好的鲁棒性。 |
| 关 键 词： | 信息处理技术 稀疏表示 轮廓识别 特征提取 |
Special object recognition based on sparse representation |
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| ZHA Chang-jun,SUN Nan,ZHANG Cheng,WEI Sui.Special object recognition based on sparse representation[J].Journal of Jilin University:Eng and Technol Ed,2013,43(1):256-260. | |
| Authors: | ZHA Chang-jun SUN Nan ZHANG Cheng WEI Sui |
| Affiliation: | 1(1.Key Laboratory of Intelligent Computing & 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.Troops 73101 of PLA,Xuzhou 221008,China) |
| Abstract: | According to the output signal characteristics of the profile detecting system,special object recognition method based on sparse representation,combined with the theory of sparse representation and principal component analysis,is proposed.First,using principal component analysis,the method extracts the main components of the sample signal in order to eliminate redundant information.Second,the signal is transformed into the same size of the feature vectors,which is then projected to the lower dimensional space to construct a dictionary.Finally,the testing samples are sparsely represented and recognized by the dictionary.Numerical simulations and experiments show that the proposed method has good classification effect in lower dimensional space,and good robustness for the system with some damage sensors in the actual situation. |
| Keywords: | information processing sparse representation profiling recognition feature extraction |
| 本文献已被 CNKI 万方数据 等数据库收录！ | |