Publications
Hybrid object detection system for x-ray radiographs
While object detection is a relatively well-developed field with respect to visible light photographs, there are significantly fewer algorithms designed to work with other imaging modalities. X-ray radiographs have many unique characteristics that introduce additional challenges that can cause common image processing and object detection algorithms to begin to fail. Examples of these problematic attributes include the fact that radiographs are only represented in gray scale with similar textures and that transmission overlap occurs when multiple objects are overlaid on top of each other. In this paper we not only analyze the effectiveness of common object detection techniques as applied to our specific database, but also outline how we combined various techniques to improve overall performance. While significant strides have been made towards developing a robust object detection algorithm for use with the given database, it is still a work in progress. Further research will be needed in order to deal with the specific obstacles posed by radiographs and X-ray imaging systems. Success in this project would have disruptive repercussions in fields ranging from medical imaging to manufacturing quality assurance and national security.