摘要
Sparse representation has recently been proved to be a powerful tool in image processing and object recognition.This paper proposes a novel small target detection algorithm based on this technique.By modelling a small target as a linear combination of certain target samples and then solving a sparse 0-minimization problem,the proposed apporach successfully improves and optimizes the small target representation with innovation.Furthermore,the sparsity concentration index（SCI） is creatively employed to evaluate the coefficients of each block representation and simpfy target identification.In the detection frame,target samples are firstly generated to constitute an over-complete dictionary matrix using Gaussian intensity model（GIM）,and then sparse model solvers are applied to finding sparse representation for each sub-image block.Finally,SCI lexicographical evalution of the entire image incorparates with a simple threshold locate target position.The effectiveness and robustness of the proposed algorithm are demonstrated by the exprimental results.
Sparse representation has recently been proved to be a powerful tool in image processing and object recognition.This paper proposes a novel small target detection algorithm based on this technique.By modelling a small target as a linear combination of certain target samples and then solving a sparse 0-minimization problem,the proposed apporach successfully improves and optimizes the small target representation with innovation.Furthermore,the sparsity concentration index（SCI） is creatively employed to evaluate the coefficients of each block representation and simpfy target identification.In the detection frame,target samples are firstly generated to constitute an over-complete dictionary matrix using Gaussian intensity model（GIM）,and then sparse model solvers are applied to finding sparse representation for each sub-image block.Finally,SCI lexicographical evalution of the entire image incorparates with a simple threshold locate target position.The effectiveness and robustness of the proposed algorithm are demonstrated by the exprimental results.
基金
supported by the Inter-governmental Science and Technology Cooperation Project (2009DFA12870)
作者简介
Corresponding author.Jiajia Zhao was born in 1982. He received the M.S. degree from Jilin University. Now he is a Ph.D. candidate of Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University. His current research interests are target detection and tracking. E-mail： zhaojiajia1982 @ gmail.comZhengyuan Tang was born in 1986. He received his B.E. degree from Zhejiang University in 2009. Now he is an M.S. candidate of Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University. His current research interests are target detection and image processing, E-mail： tangzhy 135 @gmail.comJie Yang was born in 1964. He received his Ph.D. degree in computer science from University of Hamburg, Germany. Now he is a professor and the Director of Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University. His current research interests include image processing, pattern analysis, and computational intelligence. E-mail： jieyang@sjtu.edu.cnErqi Liu works in China Aerospace Science and Industry Corporation, and he is the adjunct professor of Shanghai Jiaotong University. His current research interests include precision guidiance and engineering management. E-mail： everyth_ok@ yahoo.com.cn
