Skills Gap Analysis: A Guide to Training Your Teams
November 29, 2023
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This course is part of First Principles of Computer Vision Specialization
Instructor: Shree Nayar
14,871 already enrolled
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(138 reviews)
Recommended experience
Beginner level
Students should know the fundamentals of linear algebra and calculus. The knowledge of any programming language is beneficial, though not required.
(138 reviews)
Recommended experience
Beginner level
Students should know the fundamentals of linear algebra and calculus. The knowledge of any programming language is beneficial, though not required.
Learn how a camera works and how an image is formed using a lens
Understand how an image sensor works and its key characteristics
Design cameras that capture high dynamic range and wide angle images
Learn to create binary images and use them to build a simple object recognition system
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This course covers the fundamentals of imaging – the creation of an image that is ready for consumption or processing by a human or a machine. Imaging has a long history, spanning several centuries. But the advances made in the last three decades have revolutionized the camera and dramatically improved the robustness and accuracy of computer vision systems. We describe the fundamentals of imaging, as well as recent innovations in imaging that have had a profound impact on computer vision.
This course starts with examining how an image is formed using a lens camera. We explore the optical characteristics of a camera such as its magnification, F-number, depth of field and field of view. Next, we describe how solid-state image sensors (CCD and CMOS) record images, and the key properties of an image sensor such as its resolution, noise characteristics and dynamic range. We describe how image sensors can be used to sense color as well as capture images with high dynamic range. In certain structured environments, an image can be thresholded to produce a binary image from which various geometric properties of objects can be computed and used for recognizing and locating objects. Finally, we present the fundamentals of image processing – the development of computational tools to process a captured image to make it cleaner (denoising, deblurring, etc.) and easier for computer vision systems to analyze (linear and non-linear image filtering methods).
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Reviewed on May 27, 2022
This was a great introduction to image processing. I learned a lot that's applicable to my current job. I expect the follow on courses to improve that learning!
Reviewed on Feb 11, 2023
The best course on Coursera in terms of clarity, pace, quality of materials.Thank you!
Reviewed on Nov 1, 2021
Good introduction to camera and imaging topics - great first course for the First Principles of Computer Vision Specialization program.
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