Suggested preparation: Linear algebra, calculus, programming and introductory computer vision.
Optional preparation: Free AI Fundamentals Course: Machine Learning Concepts.
About this course
Study geometric and physical approaches to machine vision. Lectures connect calibration, shape recovery, object recognition, pose and photogrammetry with computational descriptions of images.
Topics and lesson examples
- Lecture 2: Image Formation, Perspective Projection, Time Derivative, Motion Field
- Lecture 7: Gradient Space, Reflectance Map, Image Irradiance Equation, Gnomonic Projection
- Lecture 12: Blob Analysis, Binary Image Processing, Green's Theorem, Derivative and Integral
- Lecture 17: Photogrammetry, Orientation, Axes of Inertia, Symmetry, Orientation
- Lecture 23: Gaussian Image, Solids of Revolution, Direction Histograms, Regular Polyhedra
Course at a glance
| Provider | MIT |
|---|---|
| Platform | YouTube · MIT OpenCourseWare |
| Access | Free Video |
| Level | Professional — WikiFree study-level assessment |
| Language | English |
| Available lessons | 23 |
| Video duration | 32h 52m · self-paced |
| Details checked | 10 October 2026 |
Preparation and study approach
Suggested preparation: Linear algebra, calculus, programming and introductory computer vision.
Follow the available videos in sequence, pausing to summarize the main argument or reconstruct a worked example. Keep a short list of assumptions, unfamiliar terms and questions to revisit. Additional reading, problem solving and practice will take time beyond the listed video duration.
Using this video classroom
Select a lesson from the list, or search for a topic. Previous and Next move through the sequence. You can mark lessons complete; progress is stored in this browser without a WikiFree account.
Free access and course scope
The public MIT OpenCourseWare videos in this classroom are free to watch. This listing provides the available playlist recordings, which may include selected lectures, recitations, demonstrations or supporting discussions rather than every component of the original campus course. Textbooks, equipment, software licenses, supervised labs, graded assignments and instructor feedback are not supplied by WikiFree. No purchase is required to watch the videos or use the note-based study approach above.
WikiFree independently organizes these resources for self-paced study. Watching does not award an MIT certificate, academic credit or admission to MIT.
Video provider and credit: MIT OpenCourseWare on YouTube. WikiFree titles, descriptions and cover artwork are independently prepared.
Related courses for your next step
- Review a lower-level optionFree AI Fundamentals Course: Machine Learning Concepts
Focus: Understand the purpose of machine learning · Explore how a model learns from examples
Beginner - Compare another approachFree MIT Course: Brains, Minds and Artificial Intelligence
Focus: Lecture 0: Tomaso Poggio - Introduction to Brains, Minds, and Machines · Seminar 3: Jessica Sommerville - Infants' Sensitivity to Cost and Benefit
Professional - Review a lower-level optionFree Machine Learning Course: Learn the Fundamentals
Focus: Classical machine learning · Regression
Beginner
Compare course focus, level and study time
| Course | Level | Focus | Study time |
|---|---|---|---|
| This course — Free MIT Course: Machine Vision and Image Understanding | Professional | Lecture 2: Image Formation, Perspective Projection, Time Derivative, Motion Field · Lecture 7: Gradient Space, Reflectance Map, Image Irradiance Equation, Gnomonic Projection · Lecture 12: Blob Analysis, Binary Image Processing, Green's Theorem, Derivative and Integral | 32h 52m of video; practice time is additional |
| Free AI Fundamentals Course: Machine Learning Concepts | Beginner | Understand the purpose of machine learning · Explore how a model learns from examples · Recognize a classification problem | 34m of video; practice time is additional |
| Free MIT Course: Brains, Minds and Artificial Intelligence | Professional | Lecture 0: Tomaso Poggio - Introduction to Brains, Minds, and Machines · Seminar 3: Jessica Sommerville - Infants' Sensitivity to Cost and Benefit · Lecture 7.5: Hynek Hermansky - Auditory Perception in Speech Technology, Part 2 | 46h 30m of video; practice time is additional |
| Free Machine Learning Course: Learn the Fundamentals | Beginner | Classical machine learning · Regression · Python notebooks | 1h 9m of video; practice time is additional |
Provider: MIT. Videos play from the MIT OpenCourseWare channel on YouTube.
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