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Computer Vision: Exploring the Field of Computer Vision

Categories: AI and ML
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About Course

This course is an introduction to computer vision and classification. We will cover the principles of image generation, camera imaging geometry, feature detection and matching, and multiview geometry, including stereo, motion estimation and tracking in this course. Additionally, we’ll develop the essential methods for computer vision applications, including action detection, tracking, depth recovery from stereo, camera calibration, image stabilisation, and automatic alignment (for example panoramas). 

The emphasis of this course is on developing mathematical intuitions for the procedures, and in the problem sets, we will learn about the differences between theoretical and practical concepts of computer vision.

So get yourself enrolled in this information-packed free course to ace your journey in computer vision so you can build state-of-the-art computer vision applications.

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What Will You Learn?

  • Linear image processing and model fitting.
  • Camera models and calibration.
  • Stereo geometry and multiple views.
  • Image feature detection and descriptors.
  • Difference between parametric and non-parametric models.
  • The human visual system and how our brain perceives visuals around it.

Course Content

Getting Started

  • Introduction and course overview.
    01:01:02

Using Image Processing for Computer Vision

Choosing the Right Camera Model and Views

Image Features

The Impact of Lighting

Image Motion

Tracking

Classification and Recognition

Useful Methods

Human Visual System

Conclusion

Course Completion Quizzes

Student Ratings & Reviews

5.0
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3 years ago
The course covers a wide range of computer vision topics, from image processing and feature extraction to deep learning-based techniques. Each concept is explained in a clear and approachable manner, making it accessible to learners with different levels of expertise.
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