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Exploration of available feature detection and identification systems and their performance on radiographs
Author(s): Andrew C. Wantuch; Joshua A. Vita; Edward S. Jimenez; Iliana E. Bray
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Paper Abstract

Despite object detection, recognition, and identification being very active areas of computer vision research, many of the available tools to aid in these processes are designed with only photographs in mind. Although some algorithms used specifically for feature detection and identification may not take explicit advantage of the colors available in the image, they still under-perform on radiographs, which are grayscale images. We are especially interested in the robustness of these algorithms, specifically their performance on a preexisting database of X-ray radiographs in compressed JPEG form, with multiple ways of describing pixel information. We will review various aspects of the performance of available feature detection and identification systems, including MATLABs Computer Vision toolbox, VLFeat, and OpenCV on our non-ideal database. In the process, we will explore possible reasons for the algorithms' lessened ability to detect and identify features from the X-ray radiographs.

Paper Details

Date Published: 3 October 2016
PDF: 9 pages
Proc. SPIE 9969, Radiation Detectors: Systems and Applications XVII, 996907 (3 October 2016); doi: 10.1117/12.2237211
Show Author Affiliations
Andrew C. Wantuch, Sandia National Labs. (United States)
Joshua A. Vita, Sandia National Labs. (United States)
Edward S. Jimenez, Sandia National Labs. (United States)
Iliana E. Bray, Sandia National Labs. (United States)


Published in SPIE Proceedings Vol. 9969:
Radiation Detectors: Systems and Applications XVII
Gary P. Grim; H. Bradford Barber; Lars R. Furenlid, Editor(s)

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