Computer Vision
This course introduces you to the theory and applications of computer vision technology, which is becoming increasingly important across many industries.
This free online course offers a unique insight into the emerging field of Computer Vision. You will be introduced to a variety of topics that explain the technology and its applications, including image processing, geometry and homography. Start this course today – It is packed with illustrations, instructions and examples, this instructor-led course will give you a competitive edge.
What You Will Learn In This Free Course
Image Processing and Transforms
This module presents an overview of the technologies involved in image processing, as well as its properties and transforms.; Module
Projective Geometry and Homography
In this module, concepts of projective geometry, how lines and points are represented on a plane, projective transformations and more.; Module
Camera Geometry
In this module, the working principles of the camera, its similarities to the human eye, the properties of a projection matrix and more.; Module
Stereo Geometry – Part 1
In this module, you will learn about the concepts of epipolar geometry and the computation of essential and fundamental matrices in stereo imaging.; Module
Stereo Geometry – Part 2
In this module: the estimation of fundamental matrices using singularity constraints; plane-induced homography, affine epipolar geometry and more.; Module
Feature Detection and Description
In this module, you will learn about feature detection and description, scale-invariant detection, region descriptors and more.; Module
Feature Matching and Model Fitting
In this module, you will learn about feature matching, techniques for computing feature vectors, model fitting and more.; Module
Color Fundamentals
In this module, colour and its properties, different models for matching colours, concepts of lighting and more.; Module
Range Image Processing
In this module, you will learn about the principles of range imaging and the characteristics of differential geometry, range data, roof edges and more.; Module
Clustering and Classification
In this module, you will learn different clustering and classification techniques in image processing and Artificial Neural Networks and their properties are introduced.; Module
Dimensional Reduction and Sparse Representation
In this module, the applications of Principal Component Analysis, techniques involved in dimension reduction, singular value decomposition and more.; Module
Deep Neural Architecture
In this module, distinguishing the features of Classical and Deep Neural Architecture, plus concepts in Convolution Neural Networks.; Module
Live Session
In this module, you will learn from answers to questions on: object recognition, applications of stereo geometry, future of computer vision and more.; Module
Course assessment