First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Engineering
COMPUTER ENGINEERING
Course unit
COMPUTER VISION (Numerosita' canale 1)
INP6075837, A.A. 2017/18

Information concerning the students who enrolled in A.Y. 2017/18

Information on the course unit
Degree course Second cycle degree in
COMPUTER ENGINEERING
IN0521, Degree course structure A.Y. 2009/10, A.Y. 2017/18
N2cn1
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Number of ECTS credits allocated 9.0
Type of assessment Mark
Course unit English denomination COMPUTER VISION
Department of reference Department of Information Engineering
Mandatory attendance No
Language of instruction English
Branch PADOVA

Lecturers
Teacher in charge STEFANO GHIDONI ING-INF/05

Mutuated
Course unit code Course unit name Teacher in charge Degree course code
INP6075837 COMPUTER VISION (Numerosita' canale 1) STEFANO GHIDONI IN2371
INP6075837 COMPUTER VISION (Numerosita' canale 1) STEFANO GHIDONI IN0527
INL1001836 THREE-DIMENSIONAL DATA PROCESSING (Numerosita' canale 1) STEFANO GHIDONI IN0521

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Core courses ING-INF/05 Data Processing Systems 9.0

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

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

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

Syllabus
Prerequisites: None
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; the course will focus on 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, median filters, histograms, Fourier transform, morphological operators.
5. Middle level processing: edge detection, blob detection, contour extraction, Hough transform, pixel clustering, watershed, mean shift, 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 processingRafael C. Gonzalez, Richard E. Woods. Upper Saddle River: Pearson Prentice Hall, --.
  • Szeliski, Richard, Computer visionalgorithms and applicationsRichard Szeliski. New York: Springer, 2011.
  • Klette, Reinhard, Concise Computer Vision. Springer London: --, 2014. Cerca nel catalogo