Computer Vision detailed syllabus for Computer Science & Design (CSD) for 2021 regulation curriculum has been taken from the Anna Universities official website and presented for the CSD 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 Science & Design 5th Sem scheme and its subjects, do visit CSD 5th Sem 2021 regulation scheme. For Professional Elective-I scheme and its subjects refer to CSD 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 Science & Design 5th Sem, visit CSD 5th Sem subject syllabuses for 2021 regulation.
For all Computer Science & Design results, visit Anna University CSD all semester results direct link.