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Jožef Stefan
International
Postgraduate School

Jamova 39
SI-1000 Ljubljana
Slovenia

Phone: +386 1 477 31 00
Fax: +386 1 477 31 10
Email: info@mps.si

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Course Description

Computer Vision

Program

Sensor Technologies

Lecturers:

prof. dr. Aleš Ude

Goals:

Objectives:
The student is able to assess problems and chose appropriate methods to acquire information from digital images. He can justify his choice based on his theoretical knowledge and experience gained by practical work. His choice of methods is based on requirements of the practical problem and design properties of the technical system to be implemented.

Competences:
• Assessing if computer vision methods can be used to solve a given technical problem.
• Understanding the functionality of different computer vision methods in practical problems.
• The ability to independently apply appropriate methods to practical problems.
• The ability to implement and practically test different algorithms for problems like object recognition and tracking.
• Knowing how to find appropriate methods in the literature when faced with a new problem.

Content:

Introduction
• Introduction to computer vision.
• Practical applications of computer vision.
• Introduction to Matlab.

Image formation
• Photometrical models and optics.
• Human vision.
• Camera calibration.
• Projective geometry and invariances.

2-D image processing
• Acqusition and representation of digital images.
• Digital filters and edge detection.
• Image segmentation and region detection.
• Morphology.
• Color and histogramms.
• Pattern matching.
• Application: Contrast adjustment.

3-D computer vision
• Stereo vision; calibration, correspondence problem and triangulation.
• Range images.
• Reconstruction of geommetrical models.
• Application: modeling cultural heritage.

Motion detection and tracking
• Optical flow.
• Motion approximation.
• Object tracking and Kalman filter.
• Navigation.
• Aplication: human head tracking.

Object recognition
• Issues in object recognition and computational mechanisms.
• View-based approaches.
• Hypotheses generation and verification.
• Recognition by parts.
• Application: face recognition.

Course literature:

Books:
• R. Szeliski, Computer Vision; Algorithms and Applications, Springer, London, Dordrecht, Heidelberg, New York, 2010.
• P. Cork, Robotics, Vision and Control, Springer-Verlag, Berlin, Heidelberg, 2011.

Periodicals:
• IEEE Transactions of Pattern Analysis and Machine Intelligence.
• International Journal of Computer Vision.

Significant publications and references:

Bibliography:
• 27 original scientific articles,
• 10 invited lectures,
• 102 conference contributions,
• 5 patents,
• 1 monograph,
• 8 other completed works,
• 590 pure citations (SCOPUS),
• h-index: 13.

Representative references:
• D. Schiebener, J. Morimoto, T. Asfour and A. Ude (2013) Integrating visual perception and manipulation for autonomous learning of object representations, Adaptive Behavior, vol. 21, no. 5, pp. 328-345.
• A. Ude, D. Schiebener, N. Sugimoto, and J. Morimoto (2012) Integrating surface-based hypotheses and manipulation for autonomous segmentation and learning of object representations, IEEE International Conference on Robotics and Automation (ICRA), Saint Paul, Minnesota, pp. 1709-1715 (pdf file). Finalist for Best Cognitive Robotics Paper award.
• D. Omrčen and A. Ude (2010) Redundancy control of a humanoid head for foveation and three-dimensional object tracking: A virtual mechanism approach, Advanced Robotics, vol. 24, no. 15, pp. 2171-2197.
• A. Ude, D. Omrčen, and G. Cheng (2008) Making object learning and recognition an active process, International Journal of Humanoid Robotics, vol. 5, no. 2, pp. 267-286.
• Nosilec večih evropskih projektov (PACO-PLUS, Xperience, IntellAct, ACAT), v katerih ima računalniški vid veliko vlogo.

Examination:

Seminar work with presentation and defence of the proposed solution for the selected problem from student’s research work (60%)
Oral exam (40%)

Students obligations:

Seminar work with presentation and defence of the proposed solution for the selected problem from student’s research work.
Oral exam.

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