Budapest University of Technology and Economics, Faculty of Electrical Engineering and Informatics

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    Industrial Image Processing

    A tantárgy neve magyarul / Name of the subject in Hungarian: Ipari képfeldolgozás és képmegjelenítés

    Last updated: 2022. november 23.

    Budapest University of Technology and Economics
    Faculty of Electrical Engineering and Informatics

    Electrical Engineering Degree Program

    First cycle

    Embedded and control system specialization 
    Course ID Semester Assessment Credit Tantárgyfélév
    VIIIAC04 6 2/1/0/v 4  
    3. Course coordinator and department Dr. Szemenyei Márton,
    4. Instructors Dr. Márton Szemenyei
    5. Required knowledge Mathematics, Signals and systems, Programming,  Image processing and basic knowledge of computer graphics
    6. Pre-requisites
    Kötelező:
    Szakirany("AVINirrend", _)
    VAGY Training.code=("5NAA7")

    A fenti forma a Neptun sajátja, ezen technikai okokból nem változtattunk.

    A kötelező előtanulmányi rend az adott szak honlapján és képzési programjában található.

    7. Objectives, learning outcomes and obtained knowledge With the development of computer technology, the automatic evaluation of image-based information has become a daily practice in quality control, process management, navigation, safety technology, medical diagnostics and many other fields. With the use of increasingly better display techniques, graphic simulation and teleoperation have become everyday technologies. The aim of the subject is to introduce the principles and application of modern computer image processing and display procedures at a skill level, to present the virtual techniques that play a key role in the management of remotely monitored autonomous industrial processes and autonomous warehouse management.
    8. Synopsis
    1. The functioning of human vision, three-dimensional perception. Components of spatial perception, the basics of monocular and binocular perception. The concept and mathematical properties of visual functions. Color systems. Mathematical model of spatial visualization. Correlation between intensity and distance data. The role of reflection models in image interpretation. Coordinate transformations, camera models and calibration procedures. Basic sensing devices. Interfacing issues of image input devices - case study. (6 hours of lectures + 2 hours of practice sessions)

    2. Getting started: Basics of image information processing. Binary image processing. Mathematical morphological foundations. Measurement of geometric properties. Questions of real-time realization.. Analysis of topological properties. The concept of additive set property measure. Euler number concept. Industrial application examples. (6 hours of lectures + 2 hours of practice sessions)

    3. Preparatory processing of the images. Fourier transform. Effect of sampling and quantization. Other spatial transformations. Histogram transformations. Filters in the space and frequency domain. Mathematical model of image segmentation. Segmentation based on level similarity. Segmentation procedures based on rapid changes. Hough transform. Motion-based segmentation. Texture segmentation. Safety and traffic application examples. (6 hours of lectures + 2 hours of practice sessions)
     
    4. Fast object tracking methods. Optical flow. Tracking colors, edges, textures. SSD algorithm. Visual feedback. Navigation, user tracking application examples. (3 hours of lectures + 1 hours of practice sessions)

     5. Property representation. Object recognition (classification) methods. Active vision. Image compression procedures,. Search in image databases. Teleoperation application examples. (6 hours of lectures + 2 hours of practice sessions)

    6. Comparison of possibilities and application techniques of modern image processing program libraries (e.g. Halcon, Matlab, ITK, openCV, LabView). Presentation of alternative solutions to simpler image processing problems. (3 hours of lectures + 1 hours of practice sessions)

    7. Modern image display devices (e.g. HMD, polar filter, anaglyph, shutter, holoTV) and applied rendering methods. 3D display, design and application of 3D displays. Conversion of "traditional" images for stereo rendering. Immersive virtual reality in teleoperation. Medical and telerobotics examples.  (6 hours of lectures + 2 hours of practice sessions)

    8. Simulator systems. Product design and testing supported by hardware in the loop simulation. Automotive application examples. (3 hours of lectures + 1 hours of practice sessions)

    9. IP-based image transmission. DSP-based smart cameras. Real-time image processing. Real-time procedures and architectures. Manufacturing automation application examples. (3 hours of lectures + 1 hours of practice sessions)
    9. Method of instruction The knowledge of the subject is presented in lectures, and the case studies are presented during practice sessions. For the exercises, each student works on a case study independently. During the semester, students prepare a homework assignment that contains a (partial) solution to a practical application.
    10. Assessment
    During the period of classes: case study prsenetation, homework assignment and midterm. The case study focuses on the study of literature, the homework is a independent programming task. The midterm checks the understanding of the theoretical material.

    During the exam period: witten exam.
    11. Recaps
    The submission of the homework can be dealayed for the retake week. One retake opportunity is available for the modterm.
    12. Consultations Avalable once for the homework assignment and before each exam.
    13. References, textbooks and resources
    R. Gonzales: Digital Image Processing, Addison-Wesley, ISBN 0-201-50803-6
    Besl, PJ: "Surfaces in range image understanding". Springer, 1988.
    14. Required learning hours and assignment
    Contact hours48
    Preparation for classes8
    Preparation for midterm12
    Homework assignment8
    Study of specified literature12
    Preparation for exam32
    Total120
    15. Syllabus prepared by

    Dr. László Vajta, assciate professor, Department of Control Engineering and Information Technology

    Dr. István Loványi, assciate professor, Department of Control Engineering and Information Technology