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Title: A probabilistic framework for space carving
Author: Broadhurst, A.
Awarding Body: University of Cambridge
Current Institution: University of Cambridge
Date of Award: 2002
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This thesis investigates the problem of reconstructing three-dimensional objects from image sequences. There are two major contributions in this thesis. The first contribution is an extension to the Space Carving framework that eliminates the need for a threshold parameter. This is achieved by introducing a new definition for the consistency function, which leads to a new volumetric representation. The second contribution of this thesis is a probabilistic framework for the Space Carving algorithm. This framework uses the concepts from the earlier algorithm, but presents them from a probabilistic point of view. In this framework the reconstruction is represented using an array of voxels, and each voxel is assigned a probability of it existing in the model. This probability is estimated from the data by applying Bayes' Theorem to two models, one for the voxel existing, and one for the voxel not existing. Both frameworks eliminate the need for a global threshold parameter, and eliminate the possibility of carving large holes in the model, which is a known failing of the original Space Carving algorithm. This thesis presents a technical description of the two frameworks, and a detailed analysis to show how these algorithms improve existing reconstruction techniques.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available