Computer Vision








Last updated on June 19, 2024 10:13 am

This course introduces you to the theory and applications of computer vision technology, which is becoming increasingly important across many industries.

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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

  • Discuss the fundamentals of image pr…
  • Outline the principles involved in p…
  • Compute projection camera matrices…
  • Explain the concepts in epipolar geo…
  • Discuss the fundamentals of image processing
  • Outline the principles involved in projective geometry and homography
  • Compute projection camera matrices
  • Explain the concepts in epipolar geometry
  • Summarise the techniques involved in feature detection and description
  • Analyze the frameworks for feature matching and model fitting
  • Discuss the principles and techniques involved in colour representation
  • Explain the procedures for computing range data
  • Discuss the approaches to image classification
  • Outline the applications of principal component analysis
  • Identify the features of deep neural architecture
  • 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

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