PTCCS338 Syllabus - Computer Vision - 2023 Regulation Anna University

PTCCS338 Syllabus - Computer Vision - 2023 Regulation Anna University

PTCCS338

COMPUTER VISION

 L T P C

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

6

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

6

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

6

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

6

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

6

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 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.

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