Course

Unit 1

Human Vision – Machine Vision and Computer Vision – HM, MVS camera -Analog, Digital- CID, CCD, CMOS, Camera Calibration – Frame Grabber, Manual & Auto shutter, Lighting parameters, Lighting sources, selection – Lighting Techniques – Type and selection, Digital camera Interfaces, Camera Computer Interfaces, Specifications and selection

Unit 2

Fundamentals of Digital Image-Filtering technique -Processing of binary and grey scale images-segmentation- thresholding-connectivity-noise reduction-edge detection-region growing and region splitting – binary and gray morphology operations. Feature extraction-Texture Analysis -Pattern recognition, image resolution-depth and volume, color processing, Template Matching -Decision Making, 3D Machine Vision Techniques

Unit 3

Automated visual inspection, In Vehicle vision sysems, image acquisition- caramas and digitization, sampling theorem, realtime hardware and system design considerations.

Applications of machine vision in Automotive Industries, Manufacturing, Electronics, Printing, Pharmaceutical, Biomedical, Robotics, Agricultural Applications

Course Objectives

• Introduce students to the fundamentals of image formation and review image processing techniques
• To make students’ understand the shape and region
• Develop an appreciation for various issues in the design of computer vision and object recognition systems

Course Outcomes

Skills Acquired

Design and implementation of vision based systems for automation applications.

1. R. Davies, Computer and Machine Vision: Theory, Algorithms, Practicalities, Academic Press, 2012
2. Rafael G Gonzalez and Richard E Woods, “Digital Image Processing”, Pearson India,
3. Alexander Hornberg, “Handbook of Machine and Computer Vision: The Guide for Developers and Users”, John Wiley & Sons, 2017.
4. Scott E Umbaugh, “Digital Image Processing and Analysis: Applications with MATLAB and CVIP Tools”, CRC Press, 2017.