By Topic

An abstraction-based approach to 3-D pose determination from range images

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
F. Quek ; Dept. of Electr. Eng. & Comput. Sci., Illinois Univ., Chicago, IL, USA ; R. Jain ; T. E. Weymouth

An abstraction-based paradigm that makes explicit the process of imposing assumptions on data is discussed. The units of abstraction are models in which levels of abstraction are determined by the degree of assumption necessary for their application. A general-to-specific refinement process provides a mechanism to proceed gracefully through the abstraction hierarchy. This strategy was applied to the recognition and pose determination of objects comprising simple and compound cylindrical and planar surfaces in dense range data. A method of computing reliable Gaussian and mean curvature sign-map descriptors from the polynomial approximations of surfaces is demonstrated. A means for determining the pose of constructed geometric forms whose algebraic surface descriptions are nonlinear in terms of their orienting parameters is developed. It is shown that biquadratic surfaces are suitable companion-linear forms for cylinder approximation and parameter estimation. The estimates provide the initial parametric approximations necessary for a nonlinear regression stage to fine tune the estimates by fitting the actual nonlinear form to the data

Published in:

IEEE Transactions on Pattern Analysis and Machine Intelligence  (Volume:15 ,  Issue: 7 )