Faculty of Engineering - Ain Shams University, Home

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Machine Vision

What Will Learn?

  • Course Aims
    *Introduce the fundamental concepts, principles, and applications of machine vision and computer vision systems. *Develop students’ ability to acquire, process, enhance, and analyze digital images for extracting meaningful visual information. *Develop students’ understanding and practical skills in feature extraction, object detection, recognition, and image-based classification techniques. *Enable students to apply machine learning and deep learning techniques to solve practical machine vision problems. *Develop students’ ability to design, implement, evaluate, and communicate complete machine vision solutions for real-world engineering applications.
  • Course Goals
  • Quality Education
  • 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: 2.0, Tutorial Hours: 1.0, Lab Hours: 0.0
  • Required SWL
    100
  • Equivalent ECTS
    4.0
  • •Digital Image Processing, Rafael Gonzalez and Richard Woods, Third Edition, Prenhall, 2008.
  • •Computer Vision: Algorithms and Applications, Richard Szeliski, Springer, 2022.