Camera and Imaging
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.
Monthly Subscription Starting at AED 99 + VAT
- Level Foundation
- Duration 17 hours
- Course by Columbia University
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Offered by
About
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).Modules
Welcome to First Principles of Computer Vision: Camera and Imaging
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- Course Syllabus
- About the Instructor
- Course Information and Support
- Academic Honesty Policy
- Discussion Forum Etiquette
- Frequently Asked Questions
Pre-Course Survey
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- Pre-Course Survey
Week 1: Introduction to First Principles of Computer Vision
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- Introductions
- Week 1 Questions and Feedback
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- Week 1 Lecture Handout
Week 2: Image Formation
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- 2.1 Overview of Image Formation Self-Check Quiz
- 2.2 Pinhole and Perspective Projection Self-check Quiz
- 2.3 Image Formation using Lenses Self-check Quiz
- 2.4 Depth of Field Self-check Quiz
- 2.5 Lens Related Issues Self-check Quiz
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- Week 2 Intrusive Detection System
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- Week 2 Lecture Handout
- 2.3 Video Correction
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- Week 2 Image Formation
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- Week 2 Questions and Feedback
Week 3: Image Sensing
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- 3.1 Overview of Image Sensing Self-check Quiz
- 3.2 A Brief History of Imaging Self-check Quiz
- 3.3 Types of Image Sensors Self-check Quiz
- 3.4 Resolution, Noise and Dynamic Range Self-check Quiz
- 3.5 Sensing Color Self-check Quiz
- 3.6 Camera Response and HDR Imaging Self-check Quiz
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- Week 3 Designing a Camera Lens
- Week 3 Questions and Feedback
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- Week 3 Lecture Handout
- 3.3 Video Correction
- 3.4 Video Correction
- 3.5 Sensing Color Supplementary Reading
Week 3 Quiz
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- Week 3 Image Sensing
Week 4: Binary Images
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- 4.1 Overview of Binary Images Self-check Quiz
- 4.2 Geometric Properties Self-check Quiz
- 4.3 Segmenting Binary Images Self-check Quiz
- 4.4 Iterative Modification Self-check Quiz
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- Week 4 Locating Objects
- Week 4 Questions and Feedback
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- Week 4 Lecture Handout
- 4.2 Video Correction
Week 4 Quiz
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- Week 4 Binary Images
Week 5: Image Processing I
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- 5.2 Pixel Processing Self-check Quiz
- 5.3 LSIS and Convolution Self-check Quiz
- 5.4 Linear Image Filters Self-check Quiz
- 5.5 Non-Linear Image Filters Self-check Quiz
- 5.6 Template Matching by Correlation Self-check Quiz
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- Week 5 Analyzing Convolution
- Week 5 Questions and Feedback
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- Week 5 Lecture Handout
- 5.3 Video Correction
- 5.4 Video Correction
- 5.6 Video Correction
Week 5 Quiz
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- Week 5 Image Processing I
Week 6: Image Processing II
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- 6.2 Fourier Transform Self-check Quiz
- 6.3 Convolution Theorem Self-check Quiz
- 6.4 Image Filtering in Frequency Domain Self-check Quiz
- 6.5 Deconvolution Self-check Quiz
- 6.6 Sampling Theory and Aliasing Self-check Quiz
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- Week 6 Questions and Feedback
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- Week 6 Lecture Handout
- 6.3 Video Correction
- 6.6 Video Correction
Week 6 Quiz
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- Week 6 Image Processing II
Post-Course Survey
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Auto Summary
"Camera and Imaging" is a foundational course in Data Science & AI offered by Coursera. It delves into the fundamentals and recent innovations of imaging, crucial for computer vision systems. Instructor-led, the course covers lens camera characteristics, solid-state image sensors, and image processing techniques. Ideal for beginners, the course spans 1020 minutes and offers Starter and Professional subscription options.
Shree Nayar