A knowledge-based framework for image enhancement in aviation security.
The main aim of this paper is to present a knowledge-based framework for automatically selecting the best image enhancement algorithm from several available on a per image basis in the context of X-ray images of airport luggage. The approach detailed involves a system that learns to map image features that represent its viewability to one or more chosen enhancement algorithms. Viewability measures have been developed to provide an automatic check on the quality of the enhanced image, i.e., is it really enhanced? The choice is based on ground-truth information generated by human X-ray screening experts. Such a system, for a new image, predicts the best-suited enhancement algorithm. Our research details the various characteristics of the knowledge-based system and shows extensive results on real images.
Singh
Maneesha
M
Autonomous Technologies Research, Department of Computer Science, University of Exeter, Exeter EX4 4QF, UK.
Singh
Sameer
S
Partridge
Derek
D
D000465:Algorithms; D001185:Artificial Intelligence; D001359:Aviation / Q000379:methods*; D005103:Expert Systems; D006801:Humans; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D012637:Security Measures; D012815:Signal Processing, Computer-Assisted; D023481:Terrorism / Q000517:prevention & control; D014584:User-Computer Interface