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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 |
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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 |
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More precise information per implementation
| Implementation | Description | Methods of instruction | Implementation |
| 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. |