Abstract
Infrared (IR) small target detection has been extensively studied due to its importance in IR search and tracking (IRST) systems. Existing methods have some limitations in suppressing cluttered high-contrast backgrounds, which may result in more false detections. In this letter, on the one hand, we propose an improved fuzzy C-mean (IFCM) clustering to accurately segment IR small targets and complex backgrounds. On the other hand, an IFCM-based descriptor fusing multiple features (IFCM-MF) is proposed to suppress complex backgrounds. First, a sliding window is designed to quickly extract the candidate pixels. Then, to better enhance the target and suppress the background, we construct an IFCM-based local window and calculate the IFCM-MF. Finally, IR small targets are detected by adaptive thresholding operation. The experimental results show that our method can better suppress cluttered high-contrast backgrounds and significantly improve the detection performance with high speed.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
