Syllabus 2016 - 2017¶
After you have succesfully followed the course on image processing and computer vision you will be able to use techniques to process and analyze images.
In this course we will introduce the basic notions in image processing and computer vision in such a way that a student will be able to use them for practical purposes.
Algorithms for image processing and computer vision are often the ‘materialization’ of mathematical formula’s. Being able to make a program from a mathematical description using the Python programming language and Numpy/Scipy packages is an important objective of this course.
A student that followed the course succesfully should:
- be able to apply relatively simple methods to analyze images in practical settings, and
- be prepared for a master level course on computer vision.
Recommended Prior Knowledge¶
Linear algebra, calculus, programming in Python/Numpy.
- Lectures (twice a week)
- Tutor sessions (once a week)
- Lab Sessions (twice a week)
- Lecture Notes (https://staff.fnwi.uva.nl/r.vandenboomgaard/IPCV20162017) including the LabExercises.
- Web resources (refered to in lecture notes).
- Scientific articles (tba and refered to in lecture notes).
- Previous exams
- LabExercises 30%
- Exam 70%
The minimal required grade for the LabExercises and the Final Exam is 5. Note that although you do get credit for the LabExercises these exercises are not meant to really grade your work (you can probably find similar work on the web.. or copy from someone else.. or let your partner do all the work, or...). Lab exercises are meant to help you understand the concepts discussed in the lectures and to prepare for the exams. The final exam is the ultimate test. Be prepared for questions in the final exam regarding the work done in the lab exercises.