PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
Surface models embedded in Bayesian or regularization style stereo vision algorithms bias the solution in a nonviewpoint invariant way. This lack of invariance reveals itself when the surface is computed from different viewpoints. Using the consistency between views one can try to adapt the prior surface models in a way that renders them more viewpoint invariant. The goal is to be able to adapt the stereo algorithm over time so that the same surface shape is obtained from different views. The method described in this paper uses the surface consistency measure to choose between the solutions provided by a set of simple prior surface models.
James J. Clark,Michael J. Weisman, andAlan L. Yuille
"Using viewpoint consistency in active stereo vision", Proc. SPIE 1825, Intelligent Robots and Computer Vision XI: Algorithms, Techniques, and Active Vision, (1 November 1992); https://doi.org/10.1117/12.131573
ACCESS THE FULL ARTICLE
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
The alert did not successfully save. Please try again later.
James J. Clark, Michael J. Weisman, Alan L. Yuille, "Using viewpoint consistency in active stereo vision," Proc. SPIE 1825, Intelligent Robots and Computer Vision XI: Algorithms, Techniques, and Active Vision, (1 November 1992); https://doi.org/10.1117/12.131573