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Course: 2024/2025

Computer image processing

(14926)

Master in Robotics and Automatization (Plan: 296 - Estudio: 77)

EPI

Coordinating teacher: ARMINGOL MORENO, JOSE MARIA

Department assigned to the subject: Systems Engineering and Automation Department

Type: Compulsory

ECTS Credits: 6.0 ECTS

Course: 1º

Semester: 1º

Objectives

With this subject it is tried that the student acquires basic knowledge that allow him to analyze and to desigh computer vision systems.

Skills and learning outcomes

Link to document

Description of contents: programme

1. Introduction - Applications - Elements 2. Bottom-up design - Image preprocessing - Edge detection - Region segmentation - Object description 3. To-down design - Rigid models - Deformable models - Optimization 4. Stereo Vision - Perspective projection - Camera calibration - Image rectification 5. Introduction to deeplearning

Learning activities and methodology

Skillful classes, individual presentations of the students, individual tutorials and personal work of the student; oriented to the theoretical knowledge acquisition.

Assessment System

% end-of-term-examination 40
% of continuous assessment (assigments, laboratory, practicals...) 60

Calendar of Continuous assessment

Continuous evaluation based on works, participation in class and tests of evaluation of abilities and knowledge.

Basic Bibliography

Duda, R. O., Hart, P. E., and Stork, D. G.. Pattern Classification. John Wiley & Sons. 2000
Gonzalez, R. C. and Woods, R. E.. Digital Image Processing. Prentice Hall. 2007
Jain, R., Rangachar, K., and Schunk,. Computer Vision. McGraw-Hill.
Pratt, W. K. Digital Image Processing. Wiley Interscience.
Russ, J. C.. The Image Processing. Handbook CRC.
Shapiro, L. G. and Stockman. Computer Vision. Prentice-Hall.

Electronic Resources *

· Awesome Computer Vision On Github : http://https://github.com/jbhuang0604/awesome-computer-vision#books
Carnegie Mellon University · The Computer Vision Homepage : www.cs.cmu.edu/~cil/vision.html
Richard Szeliski · Computer Vision: Algorithms and Applications : //http://szeliski.org/Book/
Stanford · Stanford Computer Vision Lab : http://vision.stanford.edu/

Additional Bibliography

Baggio, Daniel Lélis. Mastering OpenCV with Practical Computer Vision Projects. Packt Publishing. 2012
Davies, E. R.. Computer and machine vision: theory, algorithms, practicalities. Elsevier. 2012
Gary Bradski, Adrian Kaehler. Learning OpenCV: Computer Vision with the OpenCV Library. O'Reilly Media. 2008
Margarita N. Favorskaya, Lakhmi C. Jain . Computer Vision in Control Systems-2. Springer. 2015
Nixon, Mark S.. Feature extraction & image processing for computer vision. Academic Press. 2012

Electronic Resources *

· Open Source Computer Vision : //http://opencv.org/
· Computer Vision platform using Python : http://http://simplecv.org/
Springer · International Journal of Computer Vision : http://http://www.springer.com/computer/image+processing/journal/11263

(*) Access to some electronic resources may be restricted to members of the university community and require validation through Campus Global. If you try to connect from outside of the University you will need to set up a VPN

The course syllabus may change due academic events or other reasons.