4- Course Content :
|
Topic |
No. of hours |
Lecture |
Tutorial/Practical |
|
Introduction to pattern recognition and image processing. |
3 |
3 |
_ |
|
Bayesian decision theory. |
3 |
3 |
_ |
|
Maximum likelihood and Bayesian estimation. |
3 |
3 |
_ |
|
Non-parametric techniques. |
3 |
3 |
_ |
|
Linear discriminate functions. |
3 |
3 |
_ |
|
Feature Detection including Scale space. - Gaussian derivatives - Nonlinear scale space and anisotropic diffusion - Differential invariant structure. |
3 |
3 |
_ |
|
Feature Extraction techniques. |
3 |
3 |
_ |
|
Image Registration I (Rigid and non-rigid transformations, objective functions). |
3 |
3 |
_ |
|
Image Registration II ( Joint entropy, optimization methods). |
3 |
3 |
_ |
|
Image understanding and Shape Analysis ( Shape representations - Theory of shape spaces - Shape statistics (means, variability)). |
3 |
3 |
_ |
|
Image Segmentation I (statistical classification, morphological operators, connected components). |
6 |
6 |
_ |
|
Image Segmentation II ( Level set segmentation (PDE) - Deformable models. - Markov random fields - Mean shift ). |
3 |
3 |
_ |
|
Applications of advanced image processing an pattern recognition. |
3 |
3 |
_ |