Oct 15, 2019   3:20 a.m. Terézia
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Course syllabus I-PVID - Computer Vision (FEEIT - SS 2019/2020)


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University: Slovak University of Technology in Bratislava
Faculty: Faculty of Electrical Engineering and Information Technology
Course unit title: Computer Vision
Course unit code: I-PVID
Mode of completion and Number of ECTS credits: Exam (6 credits)
 
Name of lecturer: doc. Ing. Mikuláš Bittera, PhD. (person responsible for course)
Mgr. Ján Grman, PhD. (lecturer) - slovak, english
 
Learning outcomes of the course unit:
The aim of the course is to acquaint students with the methods of image processing (filtering, transformation, image segmentation) to master the basic procedures for image recognition using deterministic and statistical classifiers and 3D computer vision problems from basic methods of image processing, image detection of significant features to specific methods of determining the spatial coordinates of significant points of the scene, shape reconstruction of spatial object modeling and decision-making processes in recognizing objects within 3D scene
 
Prerequisites and co-requisites: none
 
Course contents:
Video capture. Features and formats of digital image. Stages of image processing. Brightness transformation a correction. Histogram of shade levels, histogram equalization. Local linear operators. Noise reduction. Fourier transform, filtering in the frequency domain , inverse filtering. Restoring an image at a known degradation. Geometric transformation, image rectification. Detection of features in the image. Edge detection. Pattern recognition. Hough transform. Detection of corner points, thresholding (binary, multilevel), methods for determining the threshold . Binary mathematical morphology. Image segmentation by growing area , cutting and joining area. Algorithms for marking related areas. Color models, chromatic chart, properties. Transformation of color images. Measurement of color difference. Texture - types of textures, texture description methods. Principle of classifiers based on descriptors. Decision rules. Descriptors ( moments, invariants) . Fourier descriptors. Recognition of areas according to shape. Recognition baseon of use of artificial intelligence methods. 3D vision. Transformation of coordinates. Homogeneous coordinate system. Inverse perspective view. Camera model. Perspective projection. Coordinate transformation Homogeneous coordinate system. Inverse perspective view. External and internal parameters of the camera. Camera callibration . Triangulation using one camera. The use of structured light. Stereopair, epipolar geometry. Stereopair normal configuration. Correspondence problem. Detecting corner points.
 
Recommended or required reading:
Basic:
DAVIES, E. Machine Vision : theory, algorithms, practicalities. San Diego : Academic Press, 1997. 750 p. ISBN 0-12-206092-X.
HARALICK, R M. -- SHAPIRO, L G. Computer and robot vision: Obs. Vol.I., 627 s.. Vol.II., 630 s. Reading : Addison-Wesley Publishing Company, 1993. 2 p.
HARTLEY, R I. -- ZISSERMAN, A. Multiple View Geometry In Computer Vission. Cambridge, New York: Cambridge University Press, 2003. 672 p. ISBN 978-0-521-54051-3.
HLAVÁČ, V. -- SEDLÁČEK, M. Zpracování signálů a obrazů. Praha : České vysoké učení technické v Praze, 2009. 252 p.
HORNBERG, A. Handbook of Machine Vision. Weiheim: WILEY-VCH, 2006. 798 p. ISBN 3-527-40584-4.
SONKA, M. -- HLAVAC, V. -- BOYLE, R. Image processing, Analysis, and Machine Vision. Stamford : Cengage Learning, 2008. 829 p. ISBN 978-0-495-24438-7.
WOODS, R E. -- GONZALEZ, R C. Digital Image Processing, 3 edition . Upper Saddle River, NJ, USA: Prentice Hall, 2007. 976 p. ISBN 02-015-0803-6.

 
Planned learning activities and teaching methods: Lecture, laboratory exercises, individual project work, individual study
 
Assesment methods and criteria: Ongoing test, reports from the exercises, final exam - writen
 
Language of instruction: Slovak, English
 
Work placement(s): There is no compulsory work placement in the course unit.


Last modification made by RNDr. Marian Puškár on 05/09/2019.

Type of output: