PTCCS338 Computer Vision Syllabus:

PTCCS338 Computer Vision Syllabus – Anna University Part time Regulation 2023

COURSE OBJECTIVES:

 To understand the fundamental concepts related to Image formation and processing.
 To learn feature detection, matching and detection
 To become familiar with feature based alignment and motion estimation
 To develop skills on 3D reconstruction
 To understand image based rendering and recognition

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 FEATURE-BASED ALIGNMENT & MOTION ESTIMATION

2D and 3D feature-based alignment – Pose estimation – Geometric intrinsic calibration – Triangulation – Two-frame structure from motion – Factorization – Bundle adjustment – Constrained structure and motion – Translational alignment – Parametric motion – Spline-based motion – Optical flow – Layered motion.

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.

30 PERIODS
PRACTICAL EXERCISES: 30 PERIODS
LABORATORY EXPERIMENTS:

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

TOTAL : 60 PERIODS
COURSE OUTCOMES:

At the end of this course, the students will be able to:
CO1:To understand basic knowledge, theories and methods in image processing and computer vision.
CO2:To implement basic and some advanced image processing techniques in OpenCV.
CO3:To apply 2D a feature-based based image alignment, segmentation and motion estimations.
CO4:To apply 3D image reconstruction techniques
CO5:To design and develop innovative image processing and computer vision applications.

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.

REFERENCES:

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.