Syllabus

Feature Detection and Characterization, Scale Space idea, Laplacian and Gaussian Derivatives, Differential Invariant Structure–Nonlinear Scale Space, Anisotropic Diffusion, PDE for image processing

Image Enhancement-Noise models, image de-noising using linear filters, order statistics-based filters and wavelet shrinkage methods, image sharpening, image super-resolution using Bayesian methods

Image segmentation: Graph-based techniques, Active Contours, Active Shape Models, Shape Analysis

Fundamentals in Shape Analysis – Moment Invariants, Contour-based Invariants, Active Appearance Models (AAM), Elliptical Harmonics, Medial Axis Representation

Object Segmentation, Generalized Hough Transform – 3D Deformable Models, Snakes, Level set evolution

Image Quality-Natural scene statistics, quality assessment based on structural and statistical approaches, blind quality assessment

High Dynamic Range (HDR) Imaging - Multi-exposure fusion for static and dynamic scenes, low light image enhancement, retinex methods, dark channel prior, defogging