The first part of the course introduces to digital image processing techniques such as histogram manipulation, filtering, thresholding, segmentation and shape detection. They are basic components in many applications in image analysis. In the second part, we focus on selected topics in image analysis such as curve estimation, color quantization, clustering techniques, image retrieval, image compression, statistical filtering, and motion analysis.
Schedule: 40 h lectures starting from 8.9.
Monday 10-12 (Room 106)
Tuesday 10-12 (Room 106)
Teacher: Pasi Fränti
Schedule: 20 h starting from 11.9.
Group 1: Thursday 8-10 (106)
Group 2: Thursday 10-12 (179)
Teacher: Alexander Kolesnikov
1. intermediate exam: 13.10. 10-12 (106)
2. intermediate exam: 20.11. 10-12 (106)
If you did not pass these exams, then you can take full course exam on general exam dates
It is possible to make additional project work (Advanced IT Laboratory, course code 173336, 1-5 cu) from selected topics related to Image Analysis course. Preliminary requirement is that Object-Oriented Programming project work has been completed, or good programming skills. Topics will be available on request, or announced during in Lectures and Exercises. Typical project works are 1-2 cu and done individually, larger tasks can be made as group work if necessary. The Project work includes programming task (preferably in C or C++), experiments with images, and writing brief documentation to describe the problem, solution, illustrations of examples, and conclusions drawn from the experiments. Topics:
- Canny Edge Detection Algorithm. (completed by V. Jain)
- Region growing animator, 2 cu. (completed by V. Diatchkov)
- Minimum detection for matrices with Monge-property. (completed by A. Oborina)
- Merge algorithm for polygonal approximation. (completed by A. Kolesnikov)
- Split-and-Merge technique for image segmentation. (completed by Y. Karulin)
- Edge following algorithm. (completed by M. Gunia)
- Image Subsampling with Hilbert's space filling curve. (in progress by Y. Karulin)
- Feature extraction for image retrieval. (completed by A. Mihaila and I. Cleju)
- Local thresholding algorithm. (in progress by J. Savinen)
- Split-and-Merge using Polygonal segmentation, 2 cu. (free)
- Hough transform using edge detection, 2 cu. (free)
- Cone-intersection algorithms for polygonal approximation. (free)
- Realization of Chan-Chen algorithm, 3 cu. (free)
- Median cut algorithm, 2 cu. (free)
- Improvements for Cluster software 5 cu. (free)
P. Fränti, Digital Image Processing, Univ. of Joensuu, Dept. of Computer Science. Lecture notes, 2001.
B. Jähne, Digital Image Processing: Concepts, Algorithms, and Scientific Applications. Springer, 1995.
R.E. Gonzalez, R.E. Woods, Digital Image Processing. Addison-Wesley, 1992.
L.G. Shapiro, G.C.Stockman, Computer Vision. Prentice Hall, 2001.