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Course unit
COMPUTER VISION (Numerosita' canale 2)
INP6075837, A.A. 2017/18

Information on the course unit
Degree course Second cycle degree in
AUTOMATION ENGINEERING
IN0527, Regulation 2008/09, A.Y. 2017/18
1163838
Number of ECTS credits allocated 9.0
Course unit English denomination COMPUTER VISION
Department of reference Department of Information Engineering
Mandatory attendance No
Language of instruction English
Campus PADOVA

Lecturers
Teacher in charge PIETRO ZANUTTIGH ING-INF/03

Mutuating
Course unit code Course unit name Teacher in charge Degree course code
INP6075837 COMPUTER VISION (Numerosita' canale 2) PIETRO ZANUTTIGH IN2371

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Educational activities in elective or integrative disciplines ING-INF/05 Data Processing Systems 9.0

Mode of delivery (when and how)
Period Second semester
Year 1st Year
Teaching methods frontal

Organisation of didactics
Type of hours Credits Hours of
teaching
Hours Individual
study
Shifts
Lecture 9.0 72 153.0 No turn

Calendar
Start of activities 26/02/2018
End of activities 01/06/2018

Examination board
Examination board not defined

Syllabus
Target skills and knowledge: The course presents the principles and techniques of computer vision. It will provide the skills required for automatic image analysis and processing and for the extraction of information from these data. Tools needed for developing real-world applications of the presented techniques will also be provided. In particular the course will present C++ applications based on the OpenCV open source library.
Examination methods: Written exam, homeworks and final project
Assessment criteria: The student will need to demonstrate that he has acquired the basic theoretical concepts of the course and that he is able to apply the theory to practical computer vision problems that can be solved using the tools discussed during the course. This will be evaluated also through the homeworks and the final project.
Course unit contents: 1. Cameras: sensors, lenses and image formation. Colors: additive and subtractive color models, color spaces, Bayer pattern.
2. Projective geometry, image formation, pinhole camera model.
3. Intrinsic and extrinsic camera calibration.
4. Image processing algorithms, low level: convolutional, bilateral and median filters. histogram processing, Fourier domain processing, morphological operators.
5. Middle level processing: edge detection, blob detection, Hough transform, segmentation: clustering-based approaches, watersheds, split and merge, region growing.
6. Image features: keypoints and descriptors.
7. High level algorithms: template matching, object recognition.
8. C++ Templates: libraries and classes, template library examples.
9. Class hierarchy and inheritance.
10. Data management for computer vision applications, applications using OpenCV.
Planned learning activities and teaching methods: Lessons and laboratories
Additional notes about suggested reading: Slides and other documents provided by the instructor
Textbooks (and optional supplementary readings)
  • Gonzalez, Rafael C.; Woods, Richard Eugene, Digital image processing. Upper Saddle River: Pearson Prentice Hall, 2010. Cerca nel catalogo
  • Szeliski, Richard, Computer visionalgorithms and applications. New York: Springer, 2011. Cerca nel catalogo
  • Klette, Reinhard, Concise Computer Vision. London: Springer, 2014. Cerca nel catalogo
  • Kaehler, Adrian, Bradski, Gary R., Learning OpenCV 3. --: O'Reilly Media, 2016. Cerca nel catalogo