mental image transformation and surface reconstruction NN

[Jenq-Neng Hwang]

Technical Report available from neuroprose:

          MENTAL IMAGE TRANSFORMATION AND MATCHING USING 
              SURFACE RECONSTRUCTION  NEURAL NETWORKS

                  Jenq-Neng Hwang, Yen-Hao Tseng

                 Information Processing Laboratory
             Department of Electrical Engineering, FT-10
             University of Washington, Seattle, WA 98195 


ABSTRACT

Invariant 2-D/3-D object recognition and motion estimation
under detection/occlusion noise and/or partial object viewing
are  difficult pattern recognition tasks. On the other hand, the
biological neural networks of human are extremely  adept in these
tasks.   It has been suggested by the studies of experimental
psychology  that the task of matching rotated  and scaled shapes by
human is  done by mentally rotating and scaling gradually  one of the
shapes into the orientation and size of  the other and then testing
for a match. Motivated by these studies,  we present a novel and
robust neural network solution for these tasks  based on detected
surface boundary data or range data. The method operates in two
stages:  The object is first parametrically represented by a surface
reconstruction neural network (SRNN) trained by the boundary points
sampled from the exemplar object. When later presented with  boundary
points sampled from the distorted object without point correspondence,
this parametric   representation allows  the mismatch information
back-propagate through the SRNN to  gradually determine (align) the
best similarity transform of the distorted object. The distance
measure can then be computed in the reconstructed representation
domain between the surface reconstructed exemplar object and the
aligned distorted object.   Applications to invariant 2-D target
classification and  3-D object motion estimation using sparse  range
data  collected from a single aspect view  are presented.


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