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The goal of this thesis is to investigate the shape matching and
recognition of 3D objects using artificial potential fields.
The potential-based approach recognizes the shape of a 3D object
by identifying the best match from a selected group of
template objects. The proposed model assumes that boundary of
every 3D template object is uniformly charged. An initially
small input object, represented by its boundary samples,
placed inside a template object will experience the repulsive
force and torque arising from the potential field. A better
match in the shape between the template object and the input
object can be obtained if the input object translates and
reorients itself to reduce the potential while growing in size.
The input object with the largest final size corresponds to the
best match and represents the shape of the giveninput object.
The potential and the associated repulsive force and torque
between the input object and the template object are
analytically tractable. The proposed approach is intrinsically
invariant under translation, rotation and size changes of the
input object.
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