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Proceedings Paper

Hessian Methods For Verification Of 3D Model Parameters From 2D Image Data
Author(s): Robert R. Goldberg; David G. Lowe
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Paper Abstract

A unified approach to instantiating model and camera parameters in the verification process is presented. Recognition implies the generation of a hypothesis, a map between projected model data and image data. An important issue remaining is the instantiation of model and camera parameters to verify the hypothesis. This "camera pose determination" is formulated as a nonlinear least squares problem, with functions minimizing distance between the projected model and image data. This approach treats camera and model parameters the same, simplifying the camera calibration problem. An original data structure Coordinate Trees with Null Com-ponents models the objects in the image. With this calculation of analytical first and second partial derivatives (with respect to parameters of model and camera) are now made possible. The application of various numeric techniques are compared, with tables displaying convergence results for various models and parameters. Minimal information is required, including the absence of depth data. This makes the algorithms robust in noisy images as well. Extensions to vision applications with general models is outlined.

Paper Details

Date Published: 5 January 1989
PDF: 5 pages
Proc. SPIE 1003, Sensor Fusion: Spatial Reasoning and Scene Interpretation, (5 January 1989); doi: 10.1117/12.948915
Show Author Affiliations
Robert R. Goldberg, Queens College (United States)
David G. Lowe, University of British Columbia (Canada)


Published in SPIE Proceedings Vol. 1003:
Sensor Fusion: Spatial Reasoning and Scene Interpretation
Paul S. Schenker, Editor(s)

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