Computer Vision detailed syllabus for Computer & Communication Engineering (CCE) for 2021 regulation curriculum has been taken from the Anna Universities official website and presented for the CCE 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 Computer & Communication Engineering 5th Sem scheme and its subjects, do visit CCE 5th Sem 2021 regulation scheme. For Professional Elective-I scheme and its subjects refer to CCE Professional Elective-I syllabus scheme. The detailed syllabus of computer vision is as follows.
Course Objectives:
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Unit I
INTRODUCTION TO IMAGE FORMATION AND PROCESSING
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
FEATURE DETECTION, MATCHING AND SEGMENTATION
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
3D RECONSTRUCTION
Shape from X – Active rangefinding – Surface representations – Point-based representations-Volumetric representations – Model-based reconstruction – Recovering texture maps and albedosos.
Unit V
IMAGE-BASED RENDERING AND RECOGNITION
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.
Practical Exercises
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
- docs.opencv.org
- https://opencv.org/opencv-free-course/
Course Outcomes:
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Text Books:
- Richard Szeliski, “Computer Vision: Algorithms and Applications”, Springer- Texts in Computer Science, Second Edition, 2022.
- Computer Vision: A Modern Approach, D. A. Forsyth, J. Ponce, Pearson Education, Second Edition, 2015.
Reference Books:
- Richard Hartley and Andrew Zisserman, Multiple View Geometry in Computer Vision, Second Edition, Cambridge University Press, March 2004.
- Christopher M. Bishop; Pattern Recognition and Machine Learning, Springer, 2006
- E. R. Davies, Computer and Machine Vision, Fourth Edition, Academic Press, 2012.
For detailed syllabus of all the other subjects of Computer & Communication Engineering 5th Sem, visit CCE 5th Sem subject syllabuses for 2021 regulation.
For all Computer & Communication Engineering results, visit Anna University CCE all semester results direct link.