Abstract:
This thesis is concerned with the application of neural network function to the classassociative
target detection technique to get distortion invariant detection for the
objects of different classes by using optical conelator. Here neural network function
generates a synthesized reference image with the help of weighted sum of the
training set containing the original image and the possible distorted versions of the
original image and that is controlled by fringe-adjusted joint transform correlator
(FJTC) function at the output layer. A class of objects may be defined as a group of
objects with similarity or dissimilarity among them, As the target members of the
class are known, the neural network based reference images can be generated in off
line by computer and fed dircctly to thc dctection systems in real time. Classassociative
target detection employs a multi-target detection algorithm (MTDA)
facilitating multiple reference images for which more than one dissimilar objects or
images of different class can be detected. In MTDA several combinations of input
spectra are produced using each reference and input scene separately. For distortion
invariant class-associative target recognition, all the conventional references are
replaced by neural network along with fringe-adjusted JTC generated composite
image, Thus employillg the generated composite image as the reference, the class associative
detection scheme provides distortion invariant multi-class target
identification. A simplified version of one step single layered neural network is
proposed, which is found successful in the implementation. The conditions for
generating neural network based composite image and employing into class associative
fringe-adjusted filter have also been suggested. The simulation shows
satisfactory performance or the proposed scheme in getting distortion invariant
detection of the members of the classes mainly with scale variation, in-plane rotation,
missing data and 3D distortion giving clear correlation peaks for all the distorted and
undistorted images while no or negligible correlation peaks for the non-targets both
in noisy and noise free conditions. This scheme works successfully for both binary
and gray level images.
