PATTERN RECOGNITION AND IMAGE PROCESSING
Group A

Hyperplane properties and decision functions. Minimum distance pattern classification with
simple and multiple prototypes.
Clustering: K means and isodata algorithm, pattern classification by likelihood functions,
bayes classifier, learning and estimation of mean vector and covariance matrix.
Trainable pattern classifier—Gradient technique, Robbins-Monre algorithm, potential
functions and least mean square errors.
Feature selection by entropy minimization, Karhuner-Lucke expansion and divergence
maximization.

Group B

 

Image representation, digitization, quantization, compression and coding.
Transform for image processing, restoration enhancement, segmentation, thinning.
Description of line and shape, statistical and syntactic models of image classification.
Morphological methods of image analysis.
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