Expectation-Maximization Optical Tomosynthetic Volume Imaging
Abstract (Summary)
Optical tomosynthetic imaging of 3-D objects from disparate 2-D images has been limited in the past by a lack of algorithmic enforcement of physical constraints, in particular within-scene obscuration and object self-occlusion. This paper presents a stochastic observation model of a tomosynthetic collection that explicitly includes an obscuration operator that is unknown by the sensor. The expectation-maximization algorithm is used to iteratively estimate the obscuration operator and to reconstruct the 3-D volume of interest. Explicit inclusion of obscuration effects greatly enhances the spatial and spectral accuracy of 3-D results without use of costly post-processing techniques. Performance metrics are introduced and the resultant receiver operating characteristics are presented.
Bibliographical Information:
Advisor:
School:Wright State University
School Location:USA - Ohio
Source Type:Master's Thesis
Keywords:3 d reconstruction tomosynthesis performance estimation classification error probability expectation maximization
ISBN:
Date of Publication:01/01/2008