An Improved Algorithm for Facet-based Infrared Small Target Detection
Infrared small target detection is an important research area of computer vision and often a key technique in Infrared Search and Track (IRST) systems. Many algorithms have been reported for this purpose. The facet-based method is one of novel algorithms and is shown as robust and efficient, but it does not perform well in target preservation. The method cannot detect peripheral pixel of target, which causes information loss of target intensity distribution and affects post processing of detection, such as target tracking and recognition. In this paper an improved algorithm is developed for solving this shortcoming. The detection behavior of the facet model is further analyzed. Small target is surrounded by background, so local image edge that indicates target contour can be represented by zero-crossings of the second partial derivatives. The improved algorithm uses facet model to fit local intensity surface and detect potential targets using extremum theory, then the zero-crossings of the second partial derivatives of the fitting function in each potential target’s neighborhood are found and the pixels inside the zero-crossing contour are restored to the potential target. In experiments involving typical infrared images target intensity distribution information is well preserved by proposed algorithm and its execution time is also acceptable.
Small target detection Target preservation Cubic facet model Extremum theory Zero-crossing
Kejia Yi Tingquan Deng Jing Guan Gongze Wang Hao Chenb
College of Computer Science and Technology, Harbin Engineering University, Harbin 150001,China Insti College of Computer Science and Technology, Harbin Engineering University, Harbin 150001,China Institute for Pattern Recognition and Artificial Intelligence, Huazhong University of Science andTec Institute for Pattern Recognition and Artificial Intelligence, Huazhong University of Science and Te
国际会议
桂林
英文
1-7
2011-11-01（万方平台首次上网日期，不代表论文的发表时间）