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Computer Vision: Foundation & Applications

Computer Vision is one of the fastest growing and most exciting AI disciplines in today’s academia and industry. This course is designed to open the doors for students who are interested in learning about the fundamental principles and important applications of computer vision.
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Prerequisites

Proficiency in Python

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Course Content

  • Computer vision overview 
  • Course logistics 

Physics of light 
Human encoding of color 
Color Spaces 
White Balancing 
Vectors and Matrices

Transformation matrixes 
Eigenvalues and eigenvectors 
Matrix calculus and hessian

Pixels and image representation 
Linear systems 
Convolutions and cross-correlations

Derivative of gaussians 
Sobel filters 
Canny edge detector

RANSAC 
Local features 
Harris corner detection

Difference of gaussians 
Scale invariant feature transform
Image stitching

Energy function 
Seam carving

 Gestalt theory of perceptual grouping 
Aggomerative clustering
Superpixels and oversegmentation

 K-means 
Mean shift

Nearest neighbors 
Classification pipeline

Singular value decomposition
Principal component analysis

 Eigenfaces and fisherfaces 
Linear Discriminant Analysis

Optical Flow
Lucas-Kanade method
Horn-Schunk Method
Pyramids for large motion
Common Fate

Feature Tracking
Lucas Kanade Tomasi (KLT) tracker

Convolutional neural networks
Backpropagation

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