MVILAIN@G.BBN.COM (Marc Vilain) (02/25/88)
BBN Science Development Program
AI Seminar Series Lecture
OBJECT RECOGNITION USING MASSIVELY PARALLEL HYPOTHESIS TESTING
Lewis W. Tucker
Thinking Machines Corporation
Cambridge, MA
(TUCKER@THINK.COM)
BBN Labs
10 Moulton Street
2nd floor large conference room
10:30 am, Tuesday March 1
Problems in computer vision span several layers of data representation
and computational requirements. While it is easy to see how advances in
parallel machine architectures enhance our capability in "low-level"
image analysis to process the large quantities of data in typical
images, it is less obvious how parallelism can be exploited in the
"higher" levels of vision such as object recognition.
Traditional approaches to object recognition have relied on
constraint-based tree search techniques that are not necessarily
appropriate for parallel processing.
This talk will introduce a model-based object recognition system
designed at Thinking Machines Corporation and its implementation
on the Connection Machine. The goal of this system is to be able
to recognize a large number of partially occluded objects in 2-D
scenes of moderate complexity. In contrast to previous
approaches, the system described here utilizes a massively
parallel hypothesize-and-test paradigm that avoids serial search.
Perceptual grouping of features forms the basis for generating
hypotheses; parameter space clustering accumulates weak evidence;
template matching provides verification; and conflict resolution
ensures the consistency of scene interpretation.
Results from experiments with databases ranging from 10 to 100
objects show the relative independence of the time to recognize
objects with either the complexity of the scene or number of
objects in the database.
-------