12 November 1981 Geometric Constraints For Interpreting Images Of Common Structural Elements: Orthogonal Trihedral Vertices
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Proceedings Volume 0281, Techniques and Applications of Image Understanding; (1981); doi: 10.1117/12.965762
Event: 1981 Technical Symposium East, 1981, Washington, D.C., United States
Abstract
A simple analytical procedure is introduced for utilizing a ubiquitous engineering and architural structural subelement to facilitate automatically cuing, monoscopically inferring surface structure and orientation, and resolving stereo correspondences: orthogonal trihedral vertices, or OTVs. OTVs occur in profusion indoors and out. They are identifiable, and are a rich source of information regarding relative surface conformation and orientation. Practical considerations often constrain OTVs to be vertically aligned. General obligue perspective properties of OTVs are examined. The especially important case of nadir-viewing aerial stereophotogrammetry is developed in detail. An object-space vertex labeling convention incorporates vertex type and orientation. A set of image space junction signature rules based upon the object space invariance of OT V edge vanishing points enables unambiguous vertex label assignment for interior and exterior OTVs. An independent application of the labeling scheme to both members of a stereo pair, taken at arbitrarily wide convergence angle, identically labels corresponding juntions. An illustrative example is presented. Algorithmic implementation has not yet been undertaken.
© (1981) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Sidney Liebes, "Geometric Constraints For Interpreting Images Of Common Structural Elements: Orthogonal Trihedral Vertices", Proc. SPIE 0281, Techniques and Applications of Image Understanding, (12 November 1981); doi: 10.1117/12.965762; https://doi.org/10.1117/12.965762
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KEYWORDS
Cameras

Solids

Image processing

Image understanding

Photography

Structural engineering

Data modeling

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