DocumentCode
720701
Title
Automatic target recognition by infrared and visible image matching
Author
Kai-Sheng Cheng ; Huei-Yung Lin
Author_Institution
Dept. of Electr. Eng., Nat. Chung Cheng Univ., Chiayi, Taiwan
fYear
2015
fDate
18-22 May 2015
Firstpage
312
Lastpage
315
Abstract
Automatic target recognition based on the long wave infrared (LIR) and visible-light spectrum (VIS) image matching is a very challenging problem. It is due to the fact that the images between LIR and VIS have lots of different textures. This difficulty is inherent in the thermal radiation imaging affected by one of the principal mechanisms so called heat transfer. In this paper, a novel algorithm is presented for object recognition between the LIR and VIS images under various conditions. It is assumed that the visible light images of the target are available a priori, and the newly acquired infrared images are used to perform the target recognition task. The LIR and VIS images are first initialized with edge detection and binary template matching, followed by a local fuzzy threshold to identify the high similarity objects. Our method has has low computational requirements and can be implemented on a real-time system. Several experiments are carried out using the real scene images with various test objects.
Keywords
edge detection; fuzzy set theory; heat radiation; heat transfer; image matching; infrared imaging; object recognition; optical images; visible spectra; LIR image matching; VIS image matching; automatic target recognition; binary template matching; edge detection; heat transfer; image textures; long wave infrared image matching; object recognition; thermal radiation imaging; visible light spectrum image matching; Cameras; Feature extraction; Image edge detection; Missiles; Target recognition; Video sequences;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Vision Applications (MVA), 2015 14th IAPR International Conference on
Conference_Location
Tokyo
Type
conf
DOI
10.1109/MVA.2015.7153193
Filename
7153193
Link To Document