Automatically detect and track infrared small targets with kernel Fukunaga-Koontz transform and Kalman prediction.
Fukunaga-Koontz transform (FKT), stemming from principal component analysis (PCA), is used in many pattern recognition and image-processing fields. It cannot capture the higher-order statistical property of natural images, so its detection performance is not satisfying. PCA has been extended into kernel PCA in order to capture the higher-order statistics. However, thus far there have been no researchers who have definitely proposed kernel FKT (KFKT) and researched its detection performance. For accurately detecting potential small targets from infrared images, we first extend FKT into KFKT to capture the higher-order statistical properties of images. Then a framework based on Kalman prediction and KFKT, which can automatically detect and track small targets, is developed. Results of experiments show that KFKT outperforms FKT and the proposed framework is competent to automatically detect and track infrared point targets.
Liu
Ruiming
R
Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, China 200240. nanjue1975@gmail.com
Liu
Erqi
E
Yang
Jie
J
Zeng
Yong
Y
Wang
Fanglin
F
Cao
Yuan
Y