EC60502: Pattern Recognition And Image Understanding

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EC60502
Course name Pattern Recognition And Image Understanding
Offered by Electronics & Electrical Communication Engineering
Credits 4
L-T-P 3-1-0
Previous Year Grade Distribution
4
12
11
14
2
3
1
EX A B C D P F
Semester Spring


Syllabus

Syllabus mentioned in ERP

Pre-requisites: EC61501Pattern Representation: features, feature vectors; Supervised classification: Bayesâ Rule, Bayesâ classifier, minimum risk classifier, minimum distance classifier, PDF estimation from samples, lLnear discriminator, Perceptron criterion, MSE criterion, Multi class classification, KeslerâÂÂs construction, Ho-Kashyap procedure; Unsupervided classification: Nearest neighbor, KNN classifier, MSE clustering, k-means clustering, fuzzy kmeans clustering; Neural Pattern Recognition: Probabilistic neural network, multi-layer perceptron; Image understanding: Review of segmentation, Image component description â boundary representation, region representation; Image component representation: feature vector representation, graphical representation; Image Interpretation: Pattern recognition techniques, graphical techniques.


Concepts taught in class

Student Opinion

How to Crack the Paper

Classroom resources

Additional Resources