SGN-31006 Image and Video Processing Techniques, 6 cr
Person responsible
Moncef Gabbouj
Lessons
Implementation 1: SGN-31006 2015-01
| Study type | P1 | P2 | P3 | P4 | Summer |
|
|
|
|
|
|
|
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 | Additional information | Examination material |
| Book | Content-Based Management of Multimedia Databases | Kiranyaz, Gabbouj | No | |||
| Book | Image Databases: Search and Retrieval of Digital Imagery | Vittorio Castelli and Lawrence D. Bergman | No | |||
| Book | Introduction to MPEG-7 | No | ||||
| Book | Multimedia Information Retrieval and Management | D. Feng, W. C. Siu and H. J. Zhang | No | |||
| Lecture slides | No | |||||
| 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 |
Prerequisites
| Course | Mandatory/Advisable | Description |
| SGN-12006 Basic Course in Image and Video Processing | Mandatory |
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 |