MR3701 Machine Vision Systems Syllabus:
MR3701 Machine Vision Systems Syllabus – Anna University Regulation 2021
COURSE OBJECTIVES:
1. To introduce the various concepts in machine vision
2. To understand the concepts in image acquisition
3. To learn about a various basics in image processing
4. To knowledge about the feature extraction and vision techniques
5. To understand the various applications in machine vision
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 ACQUISITION
Scene constraints – Lighting parameters – Lighting sources, Selection – Lighting Techniques – Types and Selection – Machine Vision Lenses and Optical Filters, Specifications and Selection – Imaging Sensors – CCD and CMOS, Specifications – Interface Architectures – Analog and Digital Cameras – Digital Camera Interfaces – Camera Computer Interfaces, Specifications and Selection – Geometrical Image formation models – Camera Calibration
UNIT – III IMAGE PROCESSING
Machine Vision Software – Fundamentals of Digital Image – Image Acquisition Modes – Image Processing in Spatial and Frequency Domain – Point Operation, Thresholding, Grayscale Stretching – Neighborhood Operations, Image Smoothing and Sharpening – Edge Detection – Binary Morphology – Colour image processing.
UNIT – IV FEATURE EXTRACTION
Feature extraction – Region Features, Shape and Size features – Texture Analysis – Template Matching and Classification – 3D Machine Vision Techniques – Decision Making.
UNIT – V MACHINE VISION APPLICATIONS
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 successful completion of the course, students should be able to:
CO 1: Know the various types of sensors, lightings, hardware and concept of machine vision
CO 2: Acquire the image by the appropriate use of sensors, lightings and hardware
CO 3:Apply the various techniques of image processing in real time applications
CO 4: Select the suitable sensors, lightings and hardware for machine vision system
CO 5: Apply the machine vision techniques in machine vision system
TEXT BOOKS
1. Eugene Hecht, A. R. Ganesan “Optics”, Fourth Edition, 2008
2. Alexander Hornberg, “Handbook of Machine Vision”, First Edition, 2006
REFERENCES
1. Emanuele Trucco, Alessandro Verri, “Introductory Techniques For 3D Computer Vision”, First Edition, 1998
2. Rafael C. Gonzales, Richard. E. Woods, “Digital Image Processing Publishers”, Fourth Edition, 1992