Publications

Wantuch, Andrew C.; Gittinger, Jaxon M.; Perez, Ismael P.; Jimenez, Edward S.

Abstract not provided.

Wantuch, Andrew C.; Gittinger, Jaxon M.; Perez, Ismael P.; Jimenez, Edward S.

Abstract not provided.

Wantuch, Andrew C.; Gittinger, Jaxon M.; Jimenez, Edward S.; Perez, Ismael P.

Abstract not provided.

Jimenez, Edward S.; Wantuch, Andrew C.; Perez, Ismael P.

Abstract not provided.

Jimenez, Edward S.; Wantuch, Andrew C.

Abstract not provided.

Wantuch, Andrew C.; Vita, Joshua V.; Jimenez, Edward S.

Abstract not provided.

Jimenez, Edward S.; Wantuch, Andrew C.; Perez, Ismael P.

Abstract not provided.

Jimenez, Edward S.; Vita, Joshua V.; Wantuch, Andrew C.

Abstract not provided.

Vita, Joshua V.; Jimenez, Edward S.; Wantuch, Andrew C.

Abstract not provided.

Proceedings of SPIE - The International Society for Optical Engineering

Wantuch, Andrew C.; Vita, Joshua V.; Jimenez, Edward S.; Bray, Iliana E.

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.

Proceedings of SPIE - The International Society for Optical Engineering

Vita, Joshua V.; Wantuch, Andrew C.; Jimenez, Edward S.; Bray, Iliana E.

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.