JHC%OZ.AI.MIT.EDU@XX.LCS.MIT.EDU (11/11/86)
THE USE OF VERIFICATION IN MODEL-BASED RECOGNITION
David Clemens, MIT AI Lab
The recognition of objects in images involves a gigantic and complex
search through a library of models. Even for a single model, the
correspondence between parts of the model and parts of the image can
be difficult, especially if parts of the object may be occluded in the
image. Verification is a general search strategy which can reduce the
amount of processing required to find the best image/model match, but
it cannot guarantee that the best match has been found. Verification
is the Test phase of the familiar Hypothesize and Test paradigm, and
is commonly used in the last stages of recognition to weed out final
hypotheses. However, the concept can be applied more generally and
used to drive the recognition process at much earlier stages. Also
called "hypothesis-driven" recognition, this approach allows a more
focused search for evidence to support, invalidate, or modify a
hypothesis, thus decreasing the amount of data processed and improving
the accuracy of the interpretation. Unfortunately, it requires a
commitment to a finite set of initial hypotheses which must include an
early version of correct hypotheses. Thus, there are trade-offs
between hypothesis-driven modules and "data-driven" modules, which
simply process all data uniformly without committing to early
hypotheses. Several recognition systems will be discussed in this
context, demonstrating the strengths and weaknesses of the two basic
approaches applied to visual object recognition.
Thursday, November 13, 4pm
NE43 8th floor playroom