Invariant descriptors for 3D object recognition and pose
Forsyth, D.
Mundy, J.L.
Zisserman, A.
Coelho, C.
Heller, A.
Rothwell, C.
Dept. of Eng. Sci., Oxford Univ.;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Oct 1991
Volume: 13,
Issue: 10
On page(s): 971-991
ISSN: 0162-8828
References Cited: 50
CODEN: ITPIDJ
INSPEC Accession Number: 4084149
Digital Object Identifier: 10.1109/34.99233
Current Version Published: 2002-08-06
Abstract
Invariant descriptors are shape descriptors that are unaffected by
object pose, by perspective projection, or by the intrinsic parameters
of the camera. These descriptors can be constructed using the methods of
invariant theory, which are briefly surveyed. A range of applications of
invariant descriptors in 3D model-based vision is demonstrated. First, a
model-based vision system that recognizes curved plane objects
irrespective of their pose is demonstrated. Curves are not reduced to
polyhedral approximations but are handled as objects in their own right.
Models are generated directly from image data. Once objects have been
recognized, their pose can be computed. Invariant descriptors for 3D
objects with plane faces are described. All these ideas are demonstrated
using images of real scenes. The stability of a range of invariant
descriptors to measurement error is treated in detail
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