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Course Catalog 2013-2014
SGN-12006 Basic Course in Image and Video Processing, 5 cr |
Additional information
This course is equivalent to SGN-3010
Person responsible
Atanas Gotchev, Serkan Kiranyaz, Moncef Gabbouj
Lessons
| Study type | P1 | P2 | P3 | P4 | Summer | Implementations | Lecture times and places |
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Requirements
Weekly Exercises and final exam plus an optional computer project.
Completion parts must belong to the same implementation
Principles and baselines related to teaching and learning
- Power point presentations with animations will be the primary tools used in the lectures - white board will also be used to illustrate the concepts - hands on are given during the exercise sessions - each student will carry out the exercises using mostly Matlab programming tools
Learning Outcomes
This course is designed to help the student: * Apply principles and techniques of digital image processing in applications related to digital imaging system design and analysis. * Analyze and implement image processing algorithms. * Gain hands-on experience in using software tools for processing digital images.
Content
| Content | Core content | Complementary knowledge | Specialist knowledge |
| 1. | Students learn the building blocks of a digital image processing system, and learn about the different types of images to be processed in the course as well as the type of problems to be solved. Students then learn the principles of image formation, sampling, quantization and the human visual system, which will allow them to investigate specific image processing techniques later on. | Human Visual System, Image Acqusition, Sampling theorem. Fourier Family and other Transforms. | Nonlinear filtering. |
| 2. | The first major task to be learned is image intensity transformations and spatial filtering for the purpose of image enhancement in the spatial and frequency domains.The second major task is image restoration in the spatial and frequency domains. Students learn how to deal with different types of noise models and degradation processes. Then they learn about inverse filtering and Wiener filtering. | LTI systems, Impulse response, and frequency response of Linear Filters. | Image analysis and segmentation. |
| 3. | the students will learn about color spaces and color image processing and how to restore and enhance color images and different color spaces. | Colorimetry and color science. Color Processing and Enhancement. | |
| 4. | Students will learn the basics of video processing and applications: The fundamentals of digital video processing, basic video file formats, resolutions, and bit rates. The principles of video acqusition. | 3D signals and video enhancement. | Video file formats |
| 5. | Finally, various video applications will be covered such as: digital video applications, motion analysis and estimation, motion-compensated filtering methods for noise removal, de-interlacing, and resolution enhancement, a brief overview of video analysis including shot-boundary detection and scene extraction. | 2D/3D Motion Estimation and Analysis | MPEG standards |
Instructions for students on how to achieve the learning outcomes
Course Outcomes: This course requires the student to demonstrate the ability to: 1. Explain the basic elements and applications of image processing 2. Analyze image sampling and quantization requirements and implications 3. Perform Gray level transformations for Image enhancement 4. Apply histogram equalization for image enhancement 5. Use and implement order-statistics image enhancement methods 6. Design and implement two-dimensional spatial filters for image enhancement 7. Model the image restoration problem in both time and frequency domains 8. Explain Wiener filtering for de-blurring and noise removal 9. Explain the representation of colors in digital color images 10. Use Matlab to implement different image processing tasks 11. Document implementation code, report experimental results and draw proper conclusions 12. Prepare and submit a (optional) project report. Course assessment criteria: Grading is on a scale of 1-5. In order to pass the course, students must collect at least half the points from the final exam and attend at least 8 exercise sessions. A project work may also be assigned.
Assessment scale:
Numerical evaluation scale (1-5) will be used on the course
Partial passing:
Study material
| Type | Name | Author | ISBN | URL | Edition, availability, ... | Examination material | Language |
| Book | Digital Image Processing | R. Gonzalez and R. Woods | http://www.imageprocessingplace.com/, 3rd edition, Prentice-Hall, New Jersey, 2008 | Yes | English | ||
| Lecture slides | Yes | English |
Prerequisites
| Course | Mandatory/Advisable | Description |
| SGN-11006 Basic Course in Signal Processing | Advisable |
Additional information about prerequisites
Equivalent knowledge of the basics of digital signal processing is required.
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 |
| Image and Video Processing: Content: image processing basics, multi-dimensional sampling theory, aliasing, and quantization, fundamentals of color, human visual system, 2-D Fourier Family, DFT, and image filtering, edge detection, enhancement, and restoration, basic video file formats, resolutions, and bit rates for various digital video applications, motion analysis and estimation,de-interlacing, and resolution enhancement, a brief overview of video analysis including shot-boundary detection and scene extraction. State-of-the-art digital image and video compression methods and standards will also be covered in this course. |