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Applying computer vision techniques to perform semi-automated analytical photogrammetry

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2 Author(s)
David Nilosek ; Rochester Institute of Technology, 54 Lomb Memorial Drive, Chester F. Carlson Center for Imaging Science ; Carl Salvaggio

The purpose of this research is to show how common computer vision techniques can be implemented in such a way that it is possible to automate the process of analytical photogram-metry. This work develops a workflow that generates a sparse three-dimensional point cloud from a bundle of images using SIFT, RANSAC, and a sparse bundle adjustment along with basic photogrammetric methods. It then goes on to show how the output of the sparse reconstruction method can be used to generate denser three-dimensional point clouds that can be facetized and turned into high resolution three-dimensional models. This workflow was successfully tested on a five image dataset taken with RIT's WASP imaging sensor over the Van Lare wastewater treatment plant in Rochester, NY.

Published in:

Image Processing Workshop (WNYIPW), 2010 Western New York

Date of Conference:

5-5 Nov. 2010