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Coursebooks
Image processing II
MICRO-512
Lecturer(s) :
Liebling Michael Stefan DanielSage Daniel
Unser Michaël
Van De Ville Dimitri Nestor Alice
Language:
English
Summary
Study of advanced image processing; mathematical imaging. Development of image-processing software and prototyping in JAVA; application to real-world examples in industrial vision and biomedical imaging.Content
- Review of fundamental notions. Multi-dimensional Fourier transform. Convolution. z-transform. Digital filters.
- Continuous representation of discrete data. Splines. Interpolation. Geometric transformations. Multi-scale decomposition (pyramids and wavelets).
- Image transforms. Karhunen-Loève transform (KLT). Discrete cosine transform (DCT). JPEG coding. Image pyramids. Wavelet decomposition.
- Reconstruction from projections. X-ray scanners. Radon transform. Central slice theorem. Filtered backprojection. Iterative methods.
- Deconvolution. Inverse and Wiener filtering. Matrix formulations. Iterative techniques (ART).
- Statistical pattern classification. Decision making. Bayesian classification. Parameter estimation. Supervised learning. Clustering.
- Image analysis. Pixel classification. Contour extraction and representation. Shape. Texture. Snakes and active contours.
Learning Prerequisites
Required courses
Image Processing I
Recommended courses
Signals and Systems I & II, linear algebra, analysis
Important concepts to start the course
Basic image processing and related analytical tools (Fourier transform, z-tranform, etc.)
Learning Outcomes
By the end of the course, the student must be able to:- Construct interpolation models and continuous-discrete representations
- Analyze image transforms
- Design image-reconstruction algorithms
- Formalize multiresolution representations using wavelets
- Design deconvolution algorithms
- Perform image analysis and feature extraction
- Design image-processing software (plugins)
- Synthesize steerable filters
Transversal skills
- Plan and carry out activities in a way which makes optimal use of available time and other resources.
- Manage priorities.
- Access and evaluate appropriate sources of information.
- Use both general and domain specific IT resources and tools
In the programs
- SemesterSpring
- Exam formWritten
- Credits
3 - Subject examined
Image processing II - Lecture
3 Hour(s) per week x 14 weeks
- Semester
- SemesterSpring
- Exam formWritten
- Credits
3 - Subject examined
Image processing II - Lecture
3 Hour(s) per week x 14 weeks
- Semester
- SemesterSpring
- Exam formWritten
- Credits
3 - Subject examined
Image processing II - Lecture
3 Hour(s) per week x 14 weeks
- Semester
- SemesterSpring
- Exam formWritten
- Credits
3 - Subject examined
Image processing II - Lecture
3 Hour(s) per week x 14 weeks
- Semester
- SemesterSpring
- Exam formWritten
- Credits
3 - Subject examined
Image processing II - Lecture
3 Hour(s) per week x 14 weeks
- Semester
- SemesterSpring
- Exam formWritten
- Credits
3 - Subject examined
Image processing II - Lecture
3 Hour(s) per week x 14 weeks
- Semester
- SemesterSpring
- Exam formWritten
- Credits
3 - Subject examined
Image processing II - Lecture
3 Hour(s) per week x 14 weeks
- Semester
- SemesterSpring
- Exam formWritten
- Credits
3 - Subject examined
Image processing II - Lecture
3 Hour(s) per week x 14 weeks
- Semester
- SemesterSpring
- Exam formWritten
- Credits
3 - Subject examined
Image processing II - Lecture
3 Hour(s) per week x 14 weeks
- Semester
- SemesterSpring
- Exam formWritten
- Credits
3 - Subject examined
Image processing II - Lecture
3 Hour(s) per week x 14 weeks
- Semester
- SemesterSpring
- Exam formWritten
- Credits
3 - Subject examined
Image processing II - Lecture
3 Hour(s) per week x 14 weeks
- Semester
- SemesterSpring
- Exam formWritten
- Credits
3 - Subject examined
Image processing II - Lecture
3 Hour(s) per week x 14 weeks
- Semester
- SemesterSpring
- Exam formWritten
- Credits
3 - Subject examined
Image processing II - Lecture
3 Hour(s) per week x 14 weeks
- Semester
- SemesterSpring
- Exam formWritten
- Credits
3 - Subject examined
Image processing II - Lecture
3 Hour(s) per week x 14 weeks
- Semester
- SemesterSpring
- Exam formWritten
- Credits
3 - Subject examined
Image processing II - Lecture
3 Hour(s) per week x 14 weeks
- Semester
- SemesterSpring
- Exam formWritten
- Credits
3 - Subject examined
Image processing II - Lecture
3 Hour(s) per week x 14 weeks
- Semester
- SemesterSpring
- Exam formWritten
- Credits
3 - Subject examined
Image processing II - Lecture
3 Hour(s) per week x 14 weeks
- Semester
- SemesterSpring
- Exam formWritten
- Credits
3 - Subject examined
Image processing II - Lecture
3 Hour(s) per week x 14 weeks
- Semester
- SemesterSpring
- Exam formWritten
- Credits
3 - Subject examined
Image processing II - Lecture
3 Hour(s) per week x 14 weeks
- Semester
- SemesterSpring
- Exam formWritten
- Credits
3 - Subject examined
Image processing II - Lecture
3 Hour(s) per week x 14 weeks
- Semester
- SemesterSpring
- Exam formWritten
- Credits
3 - Subject examined
Image processing II - Lecture
3 Hour(s) per week x 14 weeks
- Semester
- SemesterSpring
- Exam formWritten
- Credits
3 - Subject examined
Image processing II - Lecture
3 Hour(s) per week x 14 weeks
- Semester
- SemesterSpring
- Exam formWritten
- Credits
3 - Subject examined
Image processing II - Lecture
3 Hour(s) per week x 14 weeks
- Semester
- SemesterSpring
- Exam formWritten
- Credits
3 - Subject examined
Image processing II - Lecture
3 Hour(s) per week x 14 weeks
- Semester
Reference week
| Mo | Tu | We | Th | Fr | |
|---|---|---|---|---|---|
| 8-9 | |||||
| 9-10 | |||||
| 10-11 | CM011 CO260 CO4 CO5 CO6 ELD020 | ||||
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Lecture
Exercise, TP
Project, other
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- Autumn semester
- Winter sessions
- Spring semester
- Summer sessions
- Lecture in French
- Lecture in English
- Lecture in German