Introduction to Computer Vision and Image Processing. Signals And Linear Systems in 2d. Sampling of Images Over The Affine Grid, And The Aliasing Effect. Scalar and Vector Quantization, Error Diffusion Quantization For Better Visual Effects, Quantization For Color Images. Image Enhancement Using Memory-less (lut) Operations, Linear And Non-linear Spatial Filters, Denoising, Sharpening, Edge Detection. Image Restoration-basics, Statistical Estimators (ml and Map) And Their Use, Image Priors, The Use of Examples. Linear and Non-linear Transforms in 2d, Pyramidal Representations For Images. Introduction To Information Theory, Redundancies in Images, Lossless and Lossy Image Compression Algorithms. Computerized Tomography - The Radon Transform and Its Inversion, Algebraic Methods For Reconstruction From Projections. Video Processing-motion Estimation, Denoising And Compression For Image Sequences.

Faculty: Computer Science
|Undergraduate Studies |Graduate Studies

Pre-required courses

(44130 - Signals and Systems and 236200 - Signal and 236201 - Introduction to Data Processing And and 236327 - Digital Image and Signal Processing)

Course with no extra credit

46200 - Image Processing and Analysis

Semestrial Information