| Department of Natural Sciences | |||||||||||||||
| Earliest Possible Year | MSc. 1 year to MSc. 2 year | ||||||||||||||
| Duration | Half a block | ||||||||||||||
| Credits | 7.5 (ECTS) | ||||||||||||||
| Course Level | MSc Is also expected to function as a PhD course. | ||||||||||||||
| Examination | Final Examination written examination and oral examination All aids allowed Description of Examination: A written project report and oral presentation for all course participants of the project. Projects will be done in groups. Weight: Project report 67%, oral presentation 33%. 13-point scale, no second examiner | ||||||||||||||
| Requirement For Attending Exam | Presence at at least 75% of the exercises. | ||||||||||||||
| Organisation of Teaching | Lectures/consultation about 8 hours/week, Computer exercises/computer project work about 16 hours/week, oral presentations of projects the last two days. | ||||||||||||||
| Block Placement | Block 4b Week Structure: Outside schedule, En undervisningsfri dag pr. uge Every day in 2 blocks of 2 hours each. | ||||||||||||||
| Teaching Language | English | ||||||||||||||
| Optional Prerequisites | Recommended prerequisites: new course "Matematik og modeller". | ||||||||||||||
| Restrictions | Number of participants is limited by the number of computers available in the computer room (20?). | ||||||||||||||
| Areas of Competence the Course Will Address | |||||||||||||||
| Competences obtained within Basic science: Knowledge about common digital image formats and the pitfalls of image compression. Comprehend algorithms and principles of low level image processing (image improvement) such as noise reduction. Comprehend algorithms and principles of low level basic image analysis (histograms, transformations, filters). Comprehend algorithms and principles for segmentation and morphological analysis, analysis in the frequency domain and analysis of multispectral information. Understand basic principles of programming on the script level as well as the image filter level, including understanding basic analysis of the runtime complexity of programs. Competences obtained within Applied Science: Apply principles of image analysis and programming to extend and adapt standard tools to obtain image analysis solutions tailored for specific, more complex problems. Competences obtained within Ethics & Values: - | |||||||||||||||
| Course Objectives | |||||||||||||||
| The participants shall be introduced to automatic image analysis and digital images. They shall learn about fundamental problems in image analysis and algorithms to solve them. They shall learn to use standard tools (programs) for image analysis. Through the examples in the course the participants shall learn about KVL-relevant typical applications for image analysis. Finally, the participants shall learn to extend and adapt by (simple) programming a standard image analysis tool. | |||||||||||||||
| Course Contents | |||||||||||||||
| The following subjects will be covered: Digital image representation and -compression, image models, filters, image improvement (low level image processing), simple characterisation of images (e.g. histograms), segmentation and counting of objects, morphological transformations, characterisation of objects in images, transformation to the frequency domain (Fourier transformation), analysis in the frequency domain, colour spaces and multispectral imagery. There may also be time to cover simple analysis of image sequences, camera models and projective geometry, 2D transformations and co-registration of images. Examples will be taken mainly from research groups at KVL using image analysis; these groups will be consulted during the development of the course. At least images from different types of microscopy and from electrophoresis and mircroarrays can be included and possibly also normal "macro" images e.g. of greenhouse plants or weeds on a field. Open source standard tools will be used in the course, and apart from acquiring basic skills using the tools the participants will also have to learn to make simple extensions and adaptations (through simple programming). In the last part of the course the participants will be working on group projects. A project could for example focus on making an application which in a user friendly fashion solves a specific image analysis problem with a "known" algorithm (e.g. counting cells in a microscopy mage) or a project could be more "experimental" in nature and seek algorithms to extract information from a certain type of images (e.g. segment different kinds of tissue in ultrasound images). At the end an oral presentation of each project must be made for the other participants. The grade obtained for the course will be based on the project report together with the oral presentation. | |||||||||||||||
| Teaching And Learning Methods | |||||||||||||||
| In the first part of the course, every day will be a mixture of lectures and hands-on computer exercises. In the second part of the course, during which the participants are working on projects, there will be scheduled time in the computer rooms and time for consultation on the projects. | |||||||||||||||
| Course Coordinator | |||||||||||||||
| Morten Larsen, ml@dina.kvl.dk, Department of Natural Sciences/Mathematics & Computer Science, Phone: 35332390 | |||||||||||||||
| Study Board | |||||||||||||||
| Study Committee NSN | |||||||||||||||
| Course Scope | |||||||||||||||
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