Introduction to Vision and to Image Processing. 2d Signals and Linear Systems. Sampling and Reconstruction# Uniform Sampling, Aliasing, General Sampling Grids. Quantization# Scalar, Visual Perception, Color Quantization. Image Enhancement# Gray Level Transformations, Histogram Processing, Spatial Filtering, Smoothing, Sharpening. Image Restoration# Maximum-likelihood, Maximum a Posteriori. 2d Discrete Transforms. Multiresolution and Multi-scale Representations And Analysis of Images. Image Compression# Fundamental Concepts Of Information Theory, Image Redundancies, Error-free Compression, Lossy Compression. Introduction to Computer Vision.

Faculty: Electrical and Computer Engineering
|Undergraduate Studies |Graduate Studies

Pre-required courses

(44130 - Signals and Systems and 104034 - Introduction to Probability H) or (44130 - Signals and Systems and 94412 - Probability (advanced)) or (44130 - Signals and Systems and 104222 - Probability Theory) or (44130 - Signals and Systems and 94411 - Probability (ie)) or (44131 - Signals and Systems and 104034 - Introduction to Probability H) or (44131 - Signals and Systems and 94412 - Probability (advanced)) or (44131 - Signals and Systems and 104222 - Probability Theory) or (44131 - Signals and Systems and 94411 - Probability (ie))


Course with no extra credit

336027 - Medical Image Processing 236860 - Digital Image Processing


Course with no extra credit (contains)

99798 - Strategic Manag. of Technology Innovatio


Semestrial Information