70/LM-0033 - IMAGE AND VIDEO PROCESSING
Academic Year 2019/2020
Free text for the University
ALESSANDRO FLORIS (Tit.)
- Teaching style
- Lingua Insegnamento
|[70/91] INTERNET ENGINEERING||[91/00 - Ord. 2018] INGEGNERIA DELLE TECNOLOGIE PER INTERNET||2||20|
The course introduces the essential notions allowing for the conception of automatic analysis and processing of visual information; it is mainly focused on the theory and algorithms for image and video analysis and processing, with particular reference to practical application, such as industrial (non-destructive testing, surveillance, etc.), remote sensing and medical imaging. The students will be introduced to the basic techniques of the field of Computer Vision. They will learn to apply Image Processing techniques where appropriate.
Lexical: understanding and ability to use technical-scientific language, in particular regarding mathematics and information theory.
Informatics: ability to use / learn tools and software and basic programming skills.
Communications: knowing how to present concepts and information in oral, written and graphic form.
Organizational: know how to organize activities during the day and plan a medium-term work / study plan.
Knowledge: signal theory, probability theory, numerical computation and information theory and codes (short reviews will be made).
Skills: ability to define the link between phenomena, their properties and their abstract representation.
The Laboratory includes the following activities:
1. digital images: vision psychology elements and HVS; digital image representation; sampling and quantization; histogram; color representation; geometry.
2. Image acquisition - visible light; technology and standard; noise and quality.
3. Enhancement and regulation: point operators; histogram; brightness, contrast, saturation, etc.; equalization.
4. Local operators: classification (linear/non-linear, FIR, IIR), kernel and convolution; FIR LP and HP filters; bilateral smoothing; rank operators; median filter; GL morphology operators.
5. Edges, borders and regions: spatial features extraction and representation; edges (1st and 2nd derivative, Canny); contours (Freeman; Hough transform); region segmentation (region-growing; split-and-merge).
6. Texture: texture analysis; statistical approaches; clustering-based classification and segmentation.
7. Video: motion detection and its applications.
The lab takes place during one semester; it is based on frontal lessons with presentations and group exercises dealing with the development of image processing algorithm using the C programming language. The laboratory has a total duration of 20 hours, on a 3-hour weekly basis.
During the lessons, participation is solicited through questions, requests for interpretation of the analytical results and reflections on the applicative aspects.
Verification of learning
Students will be asked to complete a weekly quiz assessment and provide the results of a project assignment by the end of the semester. Grading will be based 50% on the weekly assessments + 50% on the assignment.
Lecture slides (EN)
Anil K. Jain, Fundamentals of Digital Image Processing, Prentice Hall Information and System Sciences Series.
Michael Seul, Lawrence O'Gorman, Michael J. Sammon, Practical Algorithms for Image Analysis: Descriptions, Examples, and Code, Cambridge University Press.
J. R. Parker, Algorithms for Image Processing and Computer Vision, Wiley.
John C. Russ, The Image Processing Handbook, Fifth Edition, CRC.
A. Murat Tekalp, Digital Video Processing, Prentice Hall PTR.
H.R. Wu, K.R. Rao, Digital Video Image Quality and Perceptual Coding, CRC.
Course slides in pdf format are provided.
The following freeware/opensource tools are suggested:
Compilatore ANSI C qualunque (Dev C++)
Irfanview con plugin http://www.irfanview.com/
Image analyzer http://meesoft.logicnet.dk/
UTHSCSA ImageTool http://ddsdx.uthscsa.edu/dig/itdesc.html
Insight Segmentation and Registration Toolkit (ITK). http://www.itk.org/