{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/106150"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/106150","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Automated stereophotogrammetry","abstract":"The principle function of any automated stereophotogrammetry algorithm is to identify corresponding points in the stereo pair. In the present approach the widely known technique of successive refinement of parallax based on hierarchical coarse-to-fine resolution steps is used. To eliminate the four-eight connectivity problem of orthogonal arrays, a pseudo-hexagonal array is used at all but the final resolution. Candidate matching points are determined from the oriented edge-vector graph. Candidates will be nodes on the graph, which represent observable features at the gray scale level. Matching is based on the correlation of the edge vectors rather than the gray scale, except at the single pixel resolution where parallax accuracy is refined by a quick gray scale correlation of small windows. A discussion of speed and future studies is given.","abstract_html":"The principle function of any automated stereophotogrammetry algorithm is to identify corresponding points in the stereo pair. In the present approach the widely known technique of successive refinement of parallax based on hierarchical coarse-to-fine resolution steps is used. To eliminate the four-eight connectivity problem of orthogonal arrays, a pseudo-hexagonal array is used at all but the final resolution. Candidate matching points are determined from the oriented edge-vector graph. Candidates will be nodes on the graph, which represent observable features at the gray scale level. Matching is based on the correlation of the edge vectors rather than the gray scale, except at the single pixel resolution where parallax accuracy is refined by a quick gray scale correlation of small windows. 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