Faculty of Engineering - Ain Shams University, Home

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Machine Vision

What Will Learn?

  • Course Aims
    This course is designed to: •Develop the students' knowledge and understanding of different image representations •Train students to use and develop image filtering techniques •Introduce students to edge detection methods •Train students to use deep learning as well as CNN structure in different machine vision applications
  • Course Goals
  • Decent Work and Economic Growth
  • Industry, Innovation and Infrastructure
  • Sustainable Cities and Communities

Requirements

ECE251s

Description

  • English Description

    Introduction to machine vision. Image processing. Edge detection. Morphological image processing. Detection and recognition. Stereo vision. Harris corner detector. Image segmentation. Hough transform. Geometric transformations. Applications: Embedded vision, Autonomous driving.
  • Arabic Description

    Introduction to machine vision. Image processing. Edge detection. Morphological image processing. Detection and recognition. Stereo vision. Harris corner detector. Image segmentation. Hough transform. Geometric transformations. Applications: Embedded vision, Autonomous driving.
  • Department
    Computer and Systems Engineering
  • Credit Hours
    2
  • Grades
    Total ( 100 ) = Midterm (20) + tr.Student Activities (30 = tr.Industry 0% , tr.Project 10% , tr.Self_learning 0% , tr.Seminar 20% ) + Exam Grade (50)
  • Hours
    Lecture Hours: 2, Tutorial Hours: 1, Lab Hours: 0
  • Required SWL
    100
  • Equivalent ECTS
    4
  • •Digital Image Processing, Rafael Gonzalez and Richard Woods, Third Edition, Prenhall, 2008.
  • •Computer Vision: Algorithms and Applications, Richard Szeliski, Springer, 2022.
  • •Jürgen Beyerer, Fernando Puente León, Christian Frese, Machine Vision, Automated Visual Inspection: Theory, Practice and Applications, Springer, 2016. (EKB.org)