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Learning Objectives

  • LO1: To introduce the fundamentals of image formation
  • LO2: To provide understanding of segmentation techniques in vision-based applications
  • LO3: To impart knowledge on advanced concepts in image representation techniques
  • LO4: To provide insights on implementation of computer vision algorithms for biomedical applications

Course Outcomes

  • CO1: Ability to understand the fundamental concepts in computer vision
  • CO2: Ability to apply segmentation techniques and descriptors
  • CO3: Ability to analyse medical problems using computer vision techniques
  • CO4: Ability to evaluate performance of computer vision algorithms in biomedical applications

Course Contents

Camera Models and Calibration – Camera Projection Models – Orthographic – Affine -Perspective – Projective models – Projective geometry – Transformation of 2-D and 3-D -Internal Parameters – Lens distortion models – Calibration methods – Linear, direct, indirect and multiplane methods – Epipolar geometry – Stereo and Multi-view reconstruction. Image representation – Edge detection – Motion estimation – Regularization theory – Optical computation – Stereo vision – Structure from motion. Shape representation and Segmentation – Deformable curves and surfaces – Snakes and active contours – Level set representations – Feature detectors and descriptors – Object Recognition – Hough transforms – Case study – Vision model for mammogram image analysis.


  1. Richard Szeliski, Computer Vision: Algorithms and Applications, Springer; 2011.
  2. D. Forsyth and J. Ponce, Computer Vision – A modern approach, Prentice Hall.
  3. Sonka M, Hlavac V, Boyle R, Image processing, analysis, and machine vision, Cengage Learning; 2014.
  4. Gonzalez R C and Woods R E, Digital Image Processing, Third Edition, Prentice Hall, 2010.

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