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.
© 2007 Optical Society of America
Original Manuscript: May 4, 2007
Revised Manuscript: August 31, 2007
Manuscript Accepted: September 3, 2007
Published: October 31, 2007
Ruiming Liu, Erqi Liu, Jie Yang, Yong Zeng, Fanglin Wang, and Yuan Cao, "Automatically detect and track infrared small targets with kernel Fukunaga-Koontz transform and Kalman prediction," Appl. Opt. 46, 7780-7791 (2007)