Topics on Computer Vision

Course Code: 
Winter Semester
Credit Points: 

Course outline

A. Theory

  • Computer vision, Image formation and optical sensors
  • Elements from projective geometry, camera’s calibration, linear and nonlinear estimation of intrinsic and external camera’s parameters 
  • Photogrammetry, shadows and colors 
  • Parametric curves and surfaces 
  • Multidimensional signals and systems. Multidimensional linear spatio-temporal systems, Multidimensional filters, filters Gabor, wavelets, Scale-space decomposition, pyramids
  • Stereopsis and Multiview geometry, scene reconstruction using two and multiple images 
  • Image matching and alignment, geometric and photometric distortions, Modeling geometric distortions via linear (affine) and nonlinear (homographies) transformations, mosaicking. 
  • Feature based Image matching, detection and extraction of features, Features based on corners, blobs, SIFT, Laplacian, DoG and SURF detectors
  • Super resolution 
  • Motion and optical flow estimation, video stabilization. 
  • Machine Learning, Neural Networks, Deep Neural Nets
  • Oblect Detection, Classical and Deep techniques

B. Laboratory Exercises

  • Exercise 1: Basic geometric transformations and their use in the animation 
  • Exercise 2: Image Pyramids, Image de-noising and feature detection and extraction 
  • Exercise 3: Scene reconstruction using a stereo image system 
  • Exercise 4: Area based Image alignment and Joint alignment of  set of images
  • Exercise 5: Feature based Image alignment
  • Exercise 6-8: Open source learning platforms pytorch and tensorflow
  • Exercise 9-10: Implementation of state of the art object detection techniques 

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