Welcome to the First Principles of Computer Vision website - https://fpcv.cs.columbia.edu/about. This website serves as a supplementary resource to this specialization. Here, you will find additional information about the faculty team, ongoing research, and the academic resources for this specialization. What you can explore: ● About: Learn more about the faculty and staff behind the specialization. ● Research: The Columbia Imaging and Vision Laboratory (CAVE) at Columbia University is dedicated to the development of advanced vision systems. ● Monographs and lectures: Take a sneak peek at the lecture notes in advance.
First Principles of Computer Vision Specialization
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First Principles of Computer Vision Specialization
Master the First Principles of Computer Vision. Advance the mathematical and physical algorithms empowering computer vision
Instructor: Shree Nayar
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Get in-depth knowledge of a subject
(204 reviews)
Beginner level
Recommended experience
7 months at 10 hours a week
Flexible schedule
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Share your expertise with employers
Get in-depth knowledge of a subject
(204 reviews)
Beginner level
Recommended experience
7 months at 10 hours a week
Flexible schedule
Earn a career credential
Share your expertise with employers
What you'll learn
Master the working principles of a digital camera and learn the fundamentals of imaging processing
Create a theory of feature detection and develop algorithms for extracting features from images
Explore novel methods for using visual cues (shading, defocus, etc.) to recover the 3D shape of an object from multiple images or viewpoints
Get exposed to fundamental perceptions tasks such as image segmentation, object tracking, and object recognition
Overview
Skills you'll gain
- Estimation
- Artificial Neural Networks
- Computer Graphics
- Graph Theory
- Image Analysis
- Virtual Reality
- Dimensionality Reduction
- Algorithms
- Mathematical Modeling
- Visualization (Computer Graphics)
- Computer Vision
- Medical Imaging
- Automation Engineering
- Photography
- Color Theory
- Electronic Components
- Machine Learning Algorithms
- 3D Modeling
- Unsupervised Learning
- Machine Learning
What’s included
Shareable certificate
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Taught in English
143 practice exercises
Advance your subject-matter expertise
- Learn in-demand skills from university and industry experts
- Master a subject or tool with hands-on projects
- Develop a deep understanding of key concepts
- Earn a career certificate from Columbia University
Specialization - 5 course series
What you'll learn
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
Skills you'll gain
Category: Image Analysis
Category: Advanced Mathematics
Category: Linear Algebra
Category: Computer Vision
Category: Electronic Components
Category: Algorithms
Category: Photography
Category: Color Theory
Category: Semiconductors
Category: Computer Graphics
Category: Visualization (Computer Graphics)
What you'll learn
Learn how to detect edges and corners in images.
Develop active contours (snakes) to find complex object boundaries.
Learn about the Hough Transform for finding simple parametric shapes in images.
Learn about image transformations and how to estimate the homography between two images.
Skills you'll gain
Category: Computer Vision
Category: Linear Algebra
Category: Machine Learning Algorithms
Category: Image Analysis
Category: Computer Graphics
Category: Machine Learning
Category: Algorithms
What you'll learn
Learn radiometric concepts related to light and how it interacts with scenes.
Understand reflectance models and the different physical mechanisms that determine the appearance of a surface.
Develop a method for recovering the shape of a surface from its shading.
Understand the principle of photometric stereo where a dense surface normal map of the scene is obtained by varying the illumination direction.
Skills you'll gain
Category: Computer Vision
Category: Image Analysis
Category: Medical Imaging
Category: Computer Graphics
Category: Automation Engineering
Category: Mathematical Modeling
Category: 3D Modeling
Category: Estimation
What you'll learn
Develop a comprehensive model of a camera and learn how to calibrate a camera by estimating its parameters.
Develop a simple stereo system that uses two cameras of known configuration to estimate the 3D structure of a scene.
Design an algorithm for recovering both the structure of the scene and the motion of the camera from a video.
Develop optical flow algorithms for estimating the motion of points in a video sequence.
Skills you'll gain
Category: Computer Vision
Category: Linear Algebra
Category: Computer Graphics
Category: Virtual Reality
Category: Visualization (Computer Graphics)
Category: Image Analysis
What you'll learn
Design algorithms for detecting meaningful changes in a scene
Develop methods for tracking objects in a video while the object undergoes changes in pose and illumination
Learn several approaches to segmenting an image into meaningful regions
Create an end-to-end pipeline for learning and recognizing objects based on their visual appearance
Skills you'll gain
Category: Image Analysis
Category: Computer Vision
Category: Machine Learning Algorithms
Category: Artificial Neural Networks
Category: Dimensionality Reduction
Category: Unsupervised Learning
Category: Deep Learning
Category: Graph Theory
Category: Machine Learning
Category: Feature Engineering
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Frequently asked questions
3 - 5 months.
Fundamentals of linear algebra and the fundamentals of calculus is recommended. No programming language is required.
No.
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