| Kursusnavn (dansk): | Videregående Billedanalyse |
| Kursusnavn (engelsk): | Advanced Image Analysis |
| Semester: | Forår 2002 |
| Udbydes under: | cand.it., multimedieteknologi (mmt) |
| Omfang i ECTS: | 10,00 |
| Kursussprog: | Dansk |
| Kursushjemmeside: | https://learnit.itu.dk |
| Min. antal deltagere: | 0 |
| Forventet antal deltagere: | 0 |
| Maks. antal deltagere: | 10 |
| Formelle forudsætninger: | Image Processing at ITU. |
| Læringsmål: | The aim is to give the student knowledge of
- advanced statistical methods and models for analysing image data, and
- give the student the competence to apply these techniques in different applications.
The course attempts at making the participants recognize that the use of appropriate statistical models can extract useful knowledge from image data - knowledge that is not directly accessible.
In the course a series of advanced image analysis techniques will be presented through lectures and computer exercises.
Frequently, in image analysis problems we have well structured prior information about the visual phenomenon that we are observing. In the course we will formulate the inclusion of such prior information in the analysis by use of the Bayesian paradigm. The Bayesian paradigm combines prior information and observations in a statistical setting.
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| Fagligt indhold: |
- Review the Bayesian paradigm and utilise it for a series of purposes (e.g. segmentation, regularisation, restoration).
- The prior models considered in the course include Markov Random Field image models for modelling texture and deformable template models for modelling shape.
- Also, we will consider the analysis of multivariate image data. Here the purpose is extraction of relevant information (data mining).
- Furthermore, we consider the application of greyscale mathematical morphology for extracting structures (e.g. blobs) from images.
- Geostatistics is the statictics of spatial dispersed data. We will shortly review the method og kriging (linear interpolation between randomly sampled data).
- Finally, we will consider the problem of infering (3D) structure and scene-camera motion from a video stream.
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| Læringsaktiviteter: | Lectures, weekly computer exercises, and a major (2.5p) individual assignment |
| Eksamensform og -beskrivelse: | X. experimental examination form (7-scale; external exam), 13-skala, Intern censur Evaluation: Approval of mandatory exercises based on short reports, and approval of individual project based on a report
Examination: pass/fail (no censor)
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| Litteratur udover forskningsartikler: | Lecture Notes |
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Kursusoversigt