MR4203 Machine Vision Systems Syllabus:

MR4203 Machine Vision Systems Syllabus – Anna University PG Syllabus Regulation 2021

COURSE OBJECTIVES:

1. To understand the basics concepts of optics and machine vision systems.
2. To learn and understand the fundamentals of image processing
3. To impart knowledge on stereo vision and structure from motion.
4. To understand the design factors in machine vision system design.
5. To demonstrate the various applications of machine vision system.

UNIT I INTRODUCTION

Human vision – Machine vision and Computer vision – Benefits of machine vision – Block diagram and function of machine vision system implementation of industrial machine vision system – Physics of Light – Interactions of light – Refraction at a spherical surface – Thin Lens Equation

UNIT II IMAGE PROCESSING FUNDAMENTALS

Introduction to Digital Image Processing – Image sampling and quantization – Image enhancement: Gray Value Transformations, Radiometric Calibration, Image Smoothing– Geometric transformation– Image segmentation– Object Recognition and Image Understanding – Feature extraction: Region Features, Gray Value Features, Contour Features–Morphology– Edge extraction– Fitting and Template matching.

UNIT III COMPUTATIONAL STEREO AND MOTION

Computational Stereopsis – Geometry, parameters –correlation-based methods, feature-based methods – Epipolar Geometry, eight-point algorithm – Reconstruction by triangulation, scale factor and up to a projective transformation – Visual Motion – Motion field of rigid objects – Optical Flow – Estimation of motion field – 3D structure and motion from sparse and dense motion fields – Motion based segmentation.

UNIT IV SMART VISION SYSTEM DESIGN

Camera types– Field view– Resolution: camera sensor resolution, Spatial resolution, Measurement of accuracy, Calculation of resolution, Resolution for a Line Scan Camera – Choice of camera, Frame grabber and hardware platform– Pixel rate– Lens design – digital and smart cameras.

UNIT V APPLICATIONS AND CASE STUDIES

Machine Vision Applications in Manufacturing, Electronics, Printing, Pharmaceutical, Textile, Applications in Non-Visible Spectrum, Metrology and Gauging, OCR and OCV, Vision Guided Robotics – Field and Service Applications – Agricultural, and Bio Medical Field, Augmented Reality, Surveillance, Bio-Metrics.

TOTAL: 45 PERIODS

COURSE OUTCOMES:

Upon Completion of the course, the students will be able to
CO1. Understand the difference between the vision systems and were able to remember the functions of vision system.
CO2. Understand various image processing techniques and develop algorithms.
CO3. Create the visual serving for mechatronics applications
CO4. Evaluate and select appropriate lighting source, lighting technique, lens, sensor and interfacing.
CO5. Apply various machine vision techniques in various engineering fields.

REFERENCES:

1. Alexander Hornberg, “Handbook of Machine Vision”, First Edition
2. Rafael C. Gonzales, Richard. E. Woods, “Digital Image Processing Publishers”, Fourth Edition
3. Emanuele Trucco, Alessandro Verri, “Introductory Techniques For 3D Computer Vision”, First Edition
4. Yi Ma, Jana Kosecka, Stefano Soatto, Shankar Sastry, “An Invitation to 3-D Vision From Images to Models”, First Edition, 2004
5. Davies E.K., “Machine Vision: Theory, Algorithms, Practicalities”, 3rd Edition, Elsevier, 2005.
6. Milan Sonka, “Image Processing Analysis and Machine Vision”, Vikas Publishing House,2007.