Course Catalog 2014-2015
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Course Catalog 2014-2015

SGN-31006 Image and Video Processing Techniques, 6 cr

Person responsible

Atanas Gotchev, Karen Eguiazarian, Serkan Kiranyaz, Alessandro Foi

Lessons

Study type P1 P2 P3 P4 Summer Implementations Lecture times and places
Lectures
Excercises


 


 
 3 h/week
 2 h/week
+3 h/week
+2 h/week


 
SGN-31006 2014-01 Wednesday 14 - 17 , TB109

Requirements

Class-room exercises, lab works, project works.

Learning Outcomes

Upon completion of this course, the students shall learn: * the current approaches for searching, browsing, and mining various types of multimedia data such as images, and video. * methods from machine learning and computer vision for image/video processing and analysis. * a broad range of techniques that will be studied including multimedia features, video analysis and management, retrieval techniques, spatial indexing methods, long-term learning and Relevance Feedback, semantic-based retrieval techniques.

Content

Content Core content Complementary knowledge Specialist knowledge
1. Image Analysis and Content-based image retrieval  Image Processing and standards  Pattern Recognition 
2. Content based Indexing and Retrieval Techniques   Infomation retrieval  Data mining 
3. Video Analysis and Retrival     
4. Image and Video Restoration  denoising, deblurring, inverse halftoning, demosaicing   
5. Image Sampling and Interpolation, compressive sensing  samling below Nyquist's rate   

Instructions for students on how to achieve the learning outcomes

Assignments/Exercises : 25% Final Exam : 75% Project Work (optional) : 10%

Assessment scale:

Numerical evaluation scale (1-5) will be used on the course

Study material

Type Name Author ISBN URL Edition, availability, ... Examination material Language
Book   Content-Based Management of Multimedia Databases   Kiranyaz, Gabbouj         No    English  
Book   Image Databases: Search and Retrieval of Digital Imagery   Vittorio Castelli and Lawrence D. Bergman         No    English  
Book   Introduction to MPEG-7           No    English  
Book   Multimedia Information Retrieval and Management   D. Feng, W. C. Siu and H. J. Zhang         No    English  
Lecture slides             No    English  
Other online content   Local Approximations in Signal and Image Processing   K.Egiazarian, V.Katkovnik, A.Foi, J.Astola, et al       Local Approximations in Signal and Image Processing (LASIP) is a project dedicated to investigations in a wide class of novel efficient adaptive signal processing techniques. Statistical methods for restoration from noisy and blurred observations of one-dimensional signals, images, 3D microscopy, and video were recently developed.   No    English  

Prerequisites

Course Mandatory/Advisable Description
SGN-12006 Basic Course in Image and Video Processing Mandatory    

Prerequisite relations (Requires logging in to POP)



Correspondence of content

Course Corresponds course  Description 
SGN-31006 Image and Video Processing Techniques, 6 cr SGN-3057 Digital Image Processing II, 6 cr +
SGN-3106 Digital Video Processing, 4 cr
 

More precise information per implementation

Implementation Description Methods of instruction Implementation
SGN-31006 2014-01 Course Topic: Current approaches for searching, browsing, and mining various types of multimedia data such as images, and video. The focus is on applying methods from machine learning and computer vision on to these problems. In this course, a broad range of techniques will be studied including multimedia features, video analysis and management, retrieval techniques, spatial indexing methods, long-term learning and Relevance Feedback, audio analysis and retrieval, semantic-based retrieval techniques.        

Last modified02.09.2014