Intelligent Systems Laboratory
Computer Vision Performance
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 the
imperfections on the
input data.
Computer vision algorithms are composed of different
sub-algorithms often applied in sequence.
Determination of the performance of
a total computer vision algorithm is possible if the
performance of each
of the sub-algorithm constituents is given. The
problem,
however, is that for most published algorithms,
there is no performance
characterization which has been established in the
research literature.
In this project we are doing the theoretical and
experimental work to develop the relationships
and methodology for performance characterization
of vision algorithms.
