Introduction to Computer Vision
About this Course
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), …
Introduction to Computer Vision
About this Course
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.
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.
This course provides an introduction to computer vision including fundamentals, methods for application and machine learning classification.
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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.
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lesson 1
Introduction
Introduction
lesson 2
Image Processing for Computer Vision
Linear image processing
Model fitting
Frequency domain analysis
lesson 3
Camera Models and Views
Camera models
Stereo geometry
Camera calibration
Multiple views
lesson 4
Image Features
Feature detection
Feature descriptors
Model fitting
lesson 5
Lighting
Photometry
Lightness
Shape from shading
lesson 6
Image Motion
Overview
Optical flow
lesson 7
Tracking
Introduction to tracking
Parametric models
Non-parametric models
Tracking considerations
lesson 8
Classification and Recognition
Introduction to recognition
Classification: Generative models
Classification: Discriminative models
Action recognition
lesson 9
Useful Methods
Color spaces and segmentation
Binary morphology
3D perception
lesson 10
Human Visual System
The retina
Vision in the brain
