Context-based vision: recognizing objects using information from
both 2D and 3D imagery
Pattern Analysis and Machine Intelligence, IEEE Transactions on, 13 (10):
1050--1065 (1991)Abstract
Results from an ongoing project concerned with recognizing objects
in complex scene domains, especially in the domain that includes
the natural outdoor world, are described. Traditional machine recognition
paradigms assume either that all objects of interest are definable
by a relatively small number of explicit shape models or that all
objects of interest have characteristic, locally measurable features.
The failure of both assumptions has a dramatic impact on the form
of an acceptable architecture for an object recognition system. In
this work, the use of the contextual information is a central issue,
and a system is explicitly designed to identify and use context as
an integral part of recognition that eliminates the traditional dependence
on stored geometric models and universal image partitioning algorithms.
This paradigm combines the results of many simple procedures that
analyze monochrome, color, stereo, or 3D range images. Interpreting
the results along with relevant contextual knowledge makes it possible
to achieve a reliable recognition result, even when using imperfect
visual procedures. Initial experimentation with the system on ground-level
outdoor imagery has demonstrated competence beyond what is attainable
with other vision systems
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