Object separation in X-ray image sets

Geremy Heitz, Gal Chechik

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

40 Scopus citations

Abstract

In the segmentation of natural images, most algorithms rely on the concept of occlusion. In x-ray images, however, this assumption is violated, since x-ray photons penetrate most materials. In this paper, we introduce SATIS?, a method for separating objects in a set of x-ray images using the property of additivity in log space, where the logattenuation at a pixel is the sum of the log-attenuations of all objects that the corresponding x-ray passes through. Our method leverages multiple projection views of the same scene from slightly different angles to produce an accurate estimate of attenuation properties of objects in the scene. These properties can be used to identify the material composition of these objects, and are therefore crucial for applications like automatic threat detection. We evaluate SATIS? on a set of collected x-ray scans, showing that it outperforms a standard image segmentation approach and reduces the error of material estimation.

Original languageEnglish
Title of host publication2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2010
Pages2093-2100
Number of pages8
DOIs
StatePublished - 2010
Event2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2010 - San Francisco, CA, United States
Duration: 13 Jun 201018 Jun 2010

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919

Conference

Conference2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2010
Country/TerritoryUnited States
CitySan Francisco, CA
Period13/06/1018/06/10

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