
Proceedings Paper
Performance characterization of vision algorithmsFormat | Member Price | Non-Member Price |
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Paper Abstract
In order to design vision systems which work, a sound engineering methodology
must be utilized. In the systems engineering approach, a complex system
is divided into simple subsystems and from the input/output characteristics
of each subsystem, the input/output characteristics of the total system can
be determined. Machine vision systems are complex, and they are composed
of different algorithms applied in sequence. Determination of the performance of a total machine vision system is possible if the performance of each of
the subpieces, i.e. the algorithms, is given. The problem, however, is that
for most algorithms, there is no performance characterization which has been
established and published in the research literature.
Performance characterization has to do with establishing the correspondence
of the random variations and imperfections which the algorithm produces
on the output data caused by the random variations and imperfections
of the input data. This paper illustrates how random perturbation models and
propagation of random errors can be set up for a vision algorithm involving
edge detection, edge linking, arc segmentation, and line fitting. The paper
also discusses important dimensions that must be included in the performance
characterization of any vision module performing a parametric estimation such
as object pose, curve fit, or edge orientation estimation. Finally, we outline
a general parametric model having three components: a relational model; a
noise model; and a computational estimation model.
Paper Details
Date Published: 1 April 1991
PDF: 15 pages
Proc. SPIE 1406, Image Understanding in the '90s: Building Systems that Work, (1 April 1991); doi: 10.1117/12.47987
Published in SPIE Proceedings Vol. 1406:
Image Understanding in the '90s: Building Systems that Work
Brian T. Mitchell, Editor(s)
PDF: 15 pages
Proc. SPIE 1406, Image Understanding in the '90s: Building Systems that Work, (1 April 1991); doi: 10.1117/12.47987
Show Author Affiliations
Robert M. Haralick, Univ. of Washington (United States)
Visvanathan Ramesh, Univ. of Washington (United States)
Published in SPIE Proceedings Vol. 1406:
Image Understanding in the '90s: Building Systems that Work
Brian T. Mitchell, Editor(s)
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