Basic Information
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 or 236200 - Signal or 236201 - Introduction to Data Processing And or 236327 - Digital Image and Signal Processing
Course with no extra credit
46200 - Image Processing and Analysis
Semestrial Information
Weekly Hours
3 Academic Credit • 2 Lecture Hours • 1 Discussion Hours • 1 Project Hours
Go to Course Page
Responsible(s)
Or Litany
Registration Groups
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Weekly Hours
3 Academic Credit • 2 Lecture Hours • 1 Discussion Hours • 1 Project Hours
Go to Course Page
Responsible(s)
Alexander Bronstein
Registration Groups
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|
Weekly Hours
3 Academic Credit • 2 Lecture Hours • 1 Discussion Hours • 1 Project Hours
Go to Course Page
Responsible(s)
Alexander Bronstein
Registration Groups
|
|
|
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