MACHINE VISION SYSTEM

4 students

Course Objectives

  • To learn the fundamentals of visionsystems
  • To understand the image recognition and retrievalalgorithms
  • To learn the concepts of object recognition and applications of visionsystems.

Course Outcomes (Cos)

  • Able to know the basics concepts of visionsystems.
  • To apply the vision concept of designingrobots.
  • To use the algorithms to imageprocessing

Unit I: Vision System

Basic Components – Elements of visual perception, Lenses: Pinhole cameras, Gaussian Optics Cameras Camera-Computeinterfaces

Unit II: VisionAlgorithms

Fundamental Data Structures: Images, Regions, Sub-pixel Precise Contours – Image Enhancement :Gray value transformations, image smoothing, Fourier Transform – Geometric Transformation -Image segmentation – Segmentation of contours, lines, circles and ellipses – Camera calibration –Stereo Reconstruction.

Unit III: Object Recognition

Object recognition, Approaches to Object Recognition, Recognition by combination of views Objects with sharp edges, using two views only, using a single view, use of dept values.

Unit IV: Applications transforming sensor reading, Mapping Sonar Data, Aligning laser scan measurements – Vision and Tracking: Following the road, Iconic image processing, Multiscale image processing, Video Tracking.

Unit V: Robot Vision

Basic introduction to Robotic operating System (ROS) – Real and Simulated Robots – Introduction to Open CV, Open NI and PCL, installing and testing ROS camera Drivers, ROS to Open CV – The CV bridge Package.

Unit VI: Application

Perceiving 3D from 2D Images, 3D Sensing and Object Pose Computation and Integration of a Machine Vision System

Reference(s)

  1. Carsten Steger, Markus Ulrich, Christian Wiedemann, “Machine Vision Algorithms and Applications”, WILEY-VCH, Weinheim,2008.
  2. Damian M Lyons, “Cluster Computing for Robotics and Computer Vision”, World Scientific, Singapore, 2011.
  3. Rafael C. Gonzalez and Richard E. Woods, “Digital Image Processing”, Addition – Wesley Publishing Company, New Delhi,2007.
  4. Shimon Ullman, “High-Level Vision: Object recognition and Visual Cognition”, A Bradford Book, USA,2000.
  5. Patrick Goebel, “ROS by Example: A Do-It-Yourself Guide to Robot Operating System – Volume I”, A Pi Robot Production,2012.
Curriculum is empty

Instructor

Master in Agriculture sciences having India and abroad working experince in Horticulture industry.

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