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CVPR #983 CVPR 2010 Submission #983. CONFIDENTIAL REVIEW COPY. DO NOT DISTRIBUTE. CVPR

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Abstract

In the segmentation of natural images, most algorithms rely on the concept of occlusion. Intuitively, if all of the pixels in a region are the same color, they are probably part of the same object, which is blocking all of the light from the objects behind it. In x-ray images, however, this assumption is violated. In this paper, we introduce SATISφ, a method for separating objects in a set of x-ray images with the assumption of additivity in log space, where the log-attenuation 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 the extent and attenuation properties of objects in the scene. We demonstrate our algorithm on a set of collected x-ray scans, showing that our SATISφ algorithm outperforms a standard image segmentation approach. 1

Year: 2011
OAI identifier: oai:CiteSeerX.psu:10.1.1.204.7476
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