Summary

This course provides an overview of computer vision on modern Intel® architecture. Topics include:

  • Understanding how to use computer vision in industry
  • Learning the main algorithms for image processing
  • Exploring how machine learning is used in computer vision

By the end of this course, students will have practical knowledge of:

  • Different techniques to process, transform, and classify images
  • How to apply deep learning to visual tasks
  • Important computer vision methods, such as image segmentation and edge extraction

The course is structured around eight weeks of lectures and exercises. Each week requires three hours to complete.

Week 1

This class introduces the uses and history of computer vision. Topics include:

  • How the modern industry uses computer vision
  • Significant technologies and libraries
  • Computer vision application development workflow

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Week 2

This class teaches the core techniques of image processing. Topics include:

  • Methods such as interpolation, color conversions, and thresholding
  • How to implement these tools in NumPy and Matplotlib

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Week 3

This class introduces image transformations and their usages. Topics include:

  • How kernel methods (such as convolution) work and their impact on computer vision
  • How to use image gradients to perform edge detection

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Week 4

This class builds on the previous week, teaching more about image transformations:

  • Learn more about edge detection by using Canny edge detectors
  • Understand more advanced techniques through Fourier transformations and wavelet methods

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Week 5

This class teaches about image contours, segmentation, and image matching. Topics include:

  • An overview of image contours and the techniques to find them
  • How to segment the foreground and background to select areas for analysis

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Week 6

This class introduces image features and the techniques to find them. Learn about:

  • The methods to perform object detection and object recognition

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Week 7

This class teaches machine learning algorithms for computer vision. Topics include:

  • Support vector machines (SVM)—a popular algorithm used for classification problems
  • K-nearest neighbor clustering for image analysis
  • Unsupervised and supervised learning techniques

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Week 8

Continuing with machine learning, this class teaches about the application of neural networks. Learn about:

  • The mathematical theory supporting neural networks
  • How to use convolutional neural networks for image classification

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