A new infrared small and dim target detection algorithm based on multi-directional composite window

Abstract This work presents a new method based on gray characteristic analysis for infrared dim small target detection under complex backgrounds. Firstly, an improved detection window with eight directions and three layers is introduced to investigate the gray distribution characteristic of different structure in an infrared image. Secondly, we adopt a pretreatment process based on morphology filter and mean filter to reduce the running time and propose a detection rule on characteristic analysis for infrared targets. Meanwhile a new parameter optimization algorithm based on fuzzy control theory is employed so that the detection rule could be independent of the initial parameters. Finally, experimental results indicate that the proposed method can effectively detect the dim small targets and has better tracking performance.

[1]  Lai Rui,et al.  Infrared Complex Background Suppression Based on Vision Perception Model , 2012, 2012 International Conference on Industrial Control and Electronics Engineering.

[2]  Hua Ji-zhao.  A Novel Approach to Edge Detection Based on PCA , 2009 .

[3]  Shen-yuan Yang,et al.  Weak and Small Infrared Target Automatic Detection Based on Wavelet Transform , 2008, 2008 Second International Symposium on Intelligent Information Technology Application.

[4]  Xiujie Qu,et al.  Novel detection method for infrared small targets using weighted information entropy , 2012 .

[5]  Rachid Harba,et al.  A New Adaptive Switching Median Filter , 2010, IEEE Signal Processing Letters.

[6]  陈冰 Chen Bing,et al.  A New Approach of Small and Dim Target Detection in Cloud Cluster Infrared Image Based on Classification , 2009 .

[7]  YE Guang-qiang.  Pre-detecting Weak and Small Infrared Target Based on Target Model , 2006 .

[8]  Yuan Zhang,et al.  Infrared target detection based on image entropy , 2011, 2011 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC).

[9]  Li Jingfu,et al.  A Modified Segmentation Algorithm for Infrared Image , 2010, 2010 International Conference on Optoelectronics and Image Processing.

[10]  Tianxu Zhang,et al.  New class of Grayscale Morphological Filter to enhance infrared building target , 2012, IEEE Aerospace and Electronic Systems Magazine.

[11]  Yiquan Wu,et al.  Infrared small target detection based on complex contourlet transform and principal component analysis , 2010, 2010 3rd International Congress on Image and Signal Processing.

[12]  Wang Qing-quan.  Adaptive algorithm for small target detection in infrared searching system , 2010 .

[13]  Yu Lei.  Small Infrared Moving Target Detection Based on Optical Flow Estimation , 2009 .

[14]  Xingqiao Qin,et al.  Research on IR small target detection and backgroud suppression , 2010, 2010 IEEE International Conference on Information Theory and Information Security.

[15]  Jie Zhao,et al.  An Algorithm of Dim and Small Target Detection Based on Wavelet Transform and Image Fusion , 2012, 2012 Fifth International Symposium on Computational Intelligence and Design.

[16]  Bin Wu,et al.  A novel algorithm for point-target detection based on third-order cumulant in infrared image , 2006, 2006 8th international Conference on Signal Processing.

[17]  Brian D. Wemett.  Automatic target detection using vector quantization error , 2008, SPIE Defense + Commercial Sensing.

[18]  M. Farajzadeh,et al.  Detection of small target based on morphological filters , 2012, 20th Iranian Conference on Electrical Engineering (ICEE2012).

[19]  Dong Yu-cui.  Infrared Small Target Detection Research Based on Genetic Algorithm , 2012 .

[20]  C.-H. Lin,et al.  IMAGE RETRIEVAL AND CLASSIFICATION USING ADAPTIVE LOCAL BINARY PATTERNS BASED ON TEXTURE FEATURES , 2012 .