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		   B O S T O N   U N I V E R S I T Y
                                       
		      Computer Science Department
                                       
			  C O L L O Q U I U M
                                       
		       Wednesday October 8, 1997
                                       
			        3:00pm 
		       (Coffee served at 2:45pm)
                                       
			 Seminar Room / MCS 135
                         111 Cummington Street

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            Information-Conserving Object Recognition

                         Margrit Betke

                        Boston  College


Following the theory of statistical estimation, the problem of
recognizing objects imaged in complex real-world scenes is examined
from a parametric perspective. A scalar measure of an object's
complexity, which is invariant under affine transformation and changes
in image noise level, is extracted from the object's Fisher
information. The volume of Fisher information is shown to provide an
overall statistical measure of the object's recognizability in a
particular image, while the complexity provides an intrinsically
physical measure that characterizes the object in any image. An
information-conserving method is then developed for recognizing an
object imaged in a complex scene.  Here the term
information-conserving means that the method uses all the measured
data pertinent to the object's recognizability, attains the
theoretical lower bound on estimation error for any unbiased estimate,
and therefore is statistically optimal. This method is then
successfully applied to finding objects imaged in thousands of complex
real-world scenes.

This is joint work with Nicholas C. Makris, MIT.  It is accepted for
oral presentation at ICCV 98.


Host: Stan Sclaroff (sclaroff@cs.bu.edu)

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