On recovering hyperquadrics from range data
Kumar, S.
Song Han
Goldgof, D.
Bowyer, K.
Dept. of Comput. Sci. & Eng., Univ. of South Florida, Tampa, FL;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Nov 1995
Volume: 17,
Issue: 11
On page(s): 1079-1083
ISSN: 0162-8828
References Cited: 20
CODEN: ITPIDJ
INSPEC Accession Number: 5122087
Digital Object Identifier: 10.1109/34.473234
Current Version Published: 2002-08-06
Abstract
This paper discusses the applications of hyperquadric models in
computer vision and focuses on their recovery from range data.
Hyperquadrics are volumetric shape models that include superquadrics as
a special case. A hyperquadric model can be composed of any number of
terms and its geometric bound is an arbitrary convex polytope. Thus,
hyperquadrics can model more complex shapes than superquadrics.
Hyperquadrics also possess many other advantageous properties
(compactness, semilocal control, and intuitive meaning). Our proposed
algorithm starts with a rough fit using only six terms in 3D (four in
2D) and adds additional terms as necessary to improve fitting. Suitable
constraints are used to ensure proper convergence. Experimental results
with real 2D and 3D data are presented
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