Computer Vision

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