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April 25, 2025
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Master the First Principles of Computer Vision. Advance the mathematical and physical algorithms empowering computer vision
Instructor: Shree Nayar
9,811 already enrolled
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(193 reviews)
Recommended experience
Beginner level
Learners should know the fundamentals of linear algebra and calculus. Knowing any programming language is beneficial, but not required.
(193 reviews)
Recommended experience
Beginner level
Learners should know the fundamentals of linear algebra and calculus. Knowing any programming language is beneficial, but not required.
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
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This specialization presents the first comprehensive treatment of the foundations of computer vision. It focuses on the mathematical and physical underpinnings of vision and has been designed for learners, practitioners and researchers who have little or no knowledge of computer vision. The program includes a series of 5 courses. Any learner who completes this specialization has the potential to build a successful career in computer vision, a thriving field that is expected to increase in importance in the coming decades.
Applied Learning Project
Learners will develop the fundamental knowledge of computer vision by applying the models and tools including: image processing, image features, constructing 3D scene, image segmentation and object recognition. The specialization includes roughly 250 assessment questions. Proficiency in the fundamentals of computer vision is valued by a wide range of technology companies and research organizations.
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
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.
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.
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.
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
For more than 250 years, Columbia has been a leader in higher education in the nation and around the world. At the core of our wide range of academic inquiry is the commitment to attract and engage the best minds in pursuit of greater human understanding, pioneering new discoveries and service to society.
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3 - 5 months.
Fundamentals of linear algebra and the fundamentals of calculus is recommended. No programming language is required.
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Mastery of this course can pave the way to a successful career as a computer vision engineer or computer vision researcher in the fields of artificial intelligence, machine vision, visual inspection, robotics, factory automation, computer graphics, virtual reality, augmented reality, human-computer interfaces, digital imaging, medical imaging, scientific imaging, or remote sensing.
This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. If you only want to read and view the course content, you can audit the course for free. If you cannot afford the fee, you can apply for financial aid.
Financial aid available,