Adaptive image processing and implementation by multiple microcomputer system.
Abstract
This thesis has two parts, both related to the development
of smart sensor systems. The first part is a theoretical
development of two families of adaptive spatial filters for
suppressing background clutters in infrared images and based
on the minimization of mean- squared error or the maximization
of signal to noise ratio criterion. Seven different nonlinear
search techniques have been developed for the adaptation process.
They have been applied to two real world infrared test
images and exhibit fast convergence rate with no misadjustment
. The second part is an experimental development of a
multiple microcomputer system which can be a candidate for an
on-board processor system. A multiple star, multiple cluster
architecture was developed whose intercommunication is managed
by a three level control including central controller, distributed
controller and random priority controller. The
adaptive spatial filter has been successfully implemented on
this system using partitioning for parallel computing.
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