Automatic improvement of x-ray object recognition

S. Sheraizin; S. Itzikovitz

doi:10.1117/12.776260

16 April 2008 Automatic improvement of x-ray object recognition

S. Sheraizin, S. Itzikovitz

Proceedings Volume 6962, Unmanned Systems Technology X; 69620A (2008) https://doi.org/10.1117/12.776260
Event: SPIE Defense and Security Symposium, 2008, Orlando, Florida, United States

Abstract

In this paper we present our new method of automatic control of X-ray picture gray scale stretch, noise reduction and visual spatial resolution enhancement that improves the human visual picture analysis. The method is based on our set-theoretical model of the image using details clustering of a class of large details with dimensions more 4 pixels and a low dimension detail class. The last class is divided into two subclasses of distinguishable details and detectable details only. The large detail data histogram determines a pixels volume in dark area and a data value related to histogram maximum in the dark area. The received picture features are used as adaptation parameters for optimization of a picture global or local automatic gray scale stretch, noise reduction and visual spatial resolution enhancement improving object recognition. The small detail clustering into the two subclasses provides automatic visual resolution enhancement without noise visibility increase. The developed automatic control of X-ray image improvement was took training and was checked by processing series of objects: test patterns, various baggages, telephone sets, etc. The check results provided a fine tune of the developed automat improving object recognition. The experimental and practical results are discussed in the paper.

Citation Download Citation

S. Sheraizin and S. Itzikovitz "Automatic improvement of x-ray object recognition", Proc. SPIE 6962, Unmanned Systems Technology X, 69620A (16 April 2008); https://doi.org/10.1117/12.776260

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