Introduction to Computer Vision

Online Course

Introduction to Computer Vision

What is the course about?

Introduction to Computer Vision
The course Introduction to Computer Vision is an online class provided by Udacity. The skill level of the course is Intermediate. It may be possible to receive a verified certification or use the course to prepare for a degree.

This course provides an introduction to computer vision including fundamentals of image formation, camera imaging geometry, feature detection and matching, multiview geometry including stereo, motion estimation and tracking, and classification. We’ll develop basic methods for applications that include finding known models in images, depth recovery from stereo, camera calibration, image stabilization, automated alignment (e.g. panoramas), tracking, and action recognition. We focus less on the machine learning aspect of CV as that is really classification theory best learned in an ML course.

Course description
  • Introduction to Computer Vision
  • 4 months
  • Offered at Georgia Tech as CS 6476
  • Build Deep Learning Models Today
  • The focus of the course is to develop the intuitions and mathematics of the methods in lecture, and then to learn about the difference between theory and practice in the problem sets. All algorithms work perfectly in the slides. But remember what Yogi Berra said: In theory there is no difference between theory and practice. In practice there is. (Einstein said something similar but who knows more about real life?) In this course you do not, for the most part, apply high-level library functions but use low to mid level algorithms to analyze images and extract structural information.
  • Introduction
  • Image Processing for Computer Vision
  • Camera Models and Views
  • Image Features
  • Lighting
  • Image Motion
  • Tracking
  • Classification and Recognition
  • Useful Methods
  • Human Visual System
  • Introduction
  • Linear image processing
  • Model fitting
  • Frequency domain analysis
  • Camera models
  • Stereo geometry
  • Camera calibration
  • Multiple views
  • Feature detection
  • Feature descriptors
  • Model fitting
  • Photometry
  • Lightness
  • Shape from shading
  • Overview
  • Optical flow
  • Introduction to tracking
  • Parametric models
  • Non-parametric models
  • Tracking considerations
  • Introduction to recognition
  • Classification: Generative models
  • See the Technology Requirements for using Udacity.
  • Images have become ubiquitous in computing. Sometimes we forget that images often capture the light reflected from a physical scene. This course gives you both insight into the fundamentals of image formation and analysis, as well as the ability to extract information much above the pixel level. These skills are useful for anyone interested in operating on images in a context-aware manner or where images from multiple scenarios need to be combined or organized in an appropriate way.

Prerequisites & Facts

Introduction to Computer Vision

Course Topic

Deep Learning

University, College, Institution


Course Skill Level


Course Language


Place of class

Online, self-paced (see curriculum for more information)



Degree & Cost

Introduction to Computer Vision

To obtain a verified certificate from Udacity you have to finish this course or the latest version of it, if there is a new edition. The class may be free of charge, but there could be some cost to receive a verified certificate or to access the learning materials. The specifics of the course may have been changed, please consult the provider to get the latest quotes and news.
Introduction to Computer Vision
provided by Udacity


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School: Udacity
Topic: Deep Learning