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Computer Vision/Digital Image Processing (Spring 2005) |
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| Course code : | EDIP-U1 | ||
| ECTS Credits : | 7,5 | Status : | Optional |
| Revised : | 25/02 2005 | Written : | 07/01 2005 |
| Placement : | 5-7 semester | Hours per week : | 4 |
| Length : | 1 semester | Teaching Language : | Danish if no English students are present |
| Objective : | Digital imaging, video and related processing have experienced exceptional growth in recent years and have become prevalent in a wide variety of applications: Consumer, industrial, military as well as scientific markets. With the study module the student will learn and apply fundamental concepts and techniques of digital image processing to typical applications such as: automatic image enhancement, object inspection and flaw detection, object classification/recognition and image data compression/decompression. Specifically the student will: - Acquire knowledge of image sources, acquisition techniques and digital representation of images - Acquire knowledge of human light and colour perception - Be able to analyse images in the spatial or frequency domains and subsequently apply automatic processing for image enhancement - Be able to apply pseudo-colour or full colour processing - Comprehend and apply techniques for edge detection and linking in computer automated object classification/recognition - Comprehend and apply methods for compression/decompression of images - Design and test processing algorithms with MATLAB’s image processing toolbox. |
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| Principal Content : | Image sources and image acquisition Image representation Human visual perception Image enhancement in the spatial domain Image enhancement in the frequency domain Image restoration Color image processing Image compression/decompression Image segmentation Object recognition |
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| Teaching method : | Alternating lectures (approx. 50% of scheduled time), laboratory exercises and course work assignments. Three compulsory course work assignments are to be worked out in groups/teams and documented in team reports. | ||
| Required prequisites : | DSM3A or DSM4, or similar documented competences, i.e. - Time and frequency domain analysis and filtering of sampled data. Time-frequency transforms (Fourier) - Probabilistic analysis and modeling of sampled data, including prediction. - Basic information theory: Entropy, coding and compression - Matrix algebra and MATLAB |
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| Recommended prerequisites : | - | ||
| Relations : | - | ||
| Type of examination : | Look under remarks | ||
| External examiner : | Internal | ||
| Marking : | Scale of 13 | ||
| Remarks : | Admission to the exam is pending on participation in the compulsory work assignments as well as on a satisfactory level of the team reports. The exam has two phases: - In the first phase each student makes a 10-minute presentation of a self-chosen topic from one of the work assignments, while the other group members are present. The actual assignment is to be decided on in advance. - In the second phase each student makes a 10-minute presentation of a topic, drawn from the other two work assignments. The grade given will be based on: the level of the team reports and the performance during the two presentations. |
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| Teaching material : | Digital Image Processing by Rafael C. Gonzales, Richard E. Woods. Second edition 2002. Prentice Hall. ISBN 0-201-18075-8 Image Processing Toolbox User"s Manual (available in electronic form) |
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| Responsible teacher : | Keld Baden-Kristensen
, keba@dtu.dk |
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