CCS338: Computer Vision syllabus for EIE 2021 regulation (Professional Elective-VII)

Computer Vision detailed syllabus for Electronics & Instrumentation Engineering (EIE) for 2021 regulation curriculum has been taken from the Anna Universities official website and presented for the EIE students. For course code, course name, number of credits for a course and other scheme related information, do visit full semester subjects post given below.

For Electronics & Instrumentation Engineering 6th Sem scheme and its subjects, do visit EIE 6th Sem 2021 regulation scheme. For Professional Elective-VII scheme and its subjects refer to EIE Professional Elective-VII syllabus scheme. The detailed syllabus of computer vision is as follows.

Course Objectives:

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Unit I

Computer Vision – Geometric primitives and transformations – Photometric image formation – The digital camera – Point operators – Linear filtering – More neighborhood operators – Fourier transforms – Pyramids and wavelets – Geometric transformations – Global optimization.

Unit II

Points and patches – Edges – Lines – Segmentation – Active contours – Split and merge – Mean shift and mode finding – Normalized cuts – Graph cuts and energy-based methods.

Unit III

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Unit IV

Shape from X – Active rangefinding – Surface representations – Point-based representations-Volumetric representations – Model-based reconstruction – Recovering texture maps and albedosos.

Unit V

View interpolation Layered depth images – Light fields and Lumigraphs – Environment mattes -Video-based rendering-Object detection – Face recognition – Instance recognition – Category recognition – Context and scene understanding- Recognition databases and test sets.

Laboratory Experiments:

Software needed:
OpenCV computer vision Library for OpenCV in Python / PyCharm or C++ / Visual Studio or or equivalent

  • OpenCV Installation and working with Python
  • Basic Image Processing – loading images, Cropping, Resizing, Thresholding, Contour analysis, Bolb detection
  • Image Annotation – Drawing lines, text circle, rectangle, ellipse on images
  • Image Enhancement – Understanding Color spaces, color space conversion, Histogram equialization, Convolution, Image smoothing, Gradients, Edge Detection
  • Image Features and Image Alignment – Image transforms – Fourier, Hough, Extract ORB Image features, Feature matching, cloning, Feature matching based image alignment
  • Image segmentation using Graphcut / Grabcut
  • Camera Calibration with circular grid
  • Pose Estimation
  • 3D Reconstruction – Creating Depth map from stereo images
  • Object Detection and Tracking using Kalman Filter, Camshift
    1. docs.opencv.org
    2. https://opencv.org/opencv-free-course/

Course Outcomes:

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Text Books:

  1. Richard Szeliski, “Computer Vision: Algorithms and Applications”, Springer- Texts in Computer Science, Second Edition, 2022.
  2. Computer Vision: A Modern Approach, D. A. Forsyth, J. Ponce, Pearson Education, Second Edition, 2015.

Reference Books:

  1. Richard Hartley and Andrew Zisserman, Multiple View Geometry in Computer Vision, Second Edition, Cambridge University Press, March 2004.
  2. Christopher M. Bishop; Pattern Recognition and Machine Learning, Springer, 2006
  3. E. R. Davies, Computer and Machine Vision, Fourth Edition, Academic Press, 2012.

For detailed syllabus of all the other subjects of Electronics & Instrumentation Engineering 6th Sem, visit EIE 6th Sem subject syllabuses for 2021 regulation.

For all Electronics & Instrumentation Engineering results, visit Anna University EIE all semester results direct link.

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