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 a nd 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,  rest oration enhancement, segmentation, thinning.
Description of line and shape, statistical and syntactic mode ls of image classification.
Morphological methods of image analysis.

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