From Event: SPIE Optical Engineering + Applications, 2016
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
Andrew C. Wantuch, Joshua A. Vita, Edward S. Jimenez, and Iliana E. Bray, "Exploration of available feature detection and identification systems and their performance on radiographs," Proc. SPIE 9969, Radiation Detectors: Systems and Applications XVII, 996907 (Presented at SPIE Optical Engineering + Applications: August 31, 2016; Published: 3 October 2016); https://doi.org/10.1117/12.2237211.
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