Machine Vision | Electrical Engineering and Computer Science | MIT OpenCourseWare

Lecture Videos

Lecture 1: Introduction to Machine Vision

Lecture 1: Introduction to Machine Vision

Lecture 2: Image Formation, Perspective Projection, Time Derivative, Motion Field

Lecture 2: Image Formation, Perspective Projection, Time Derivative, Motion Field

Lecture 3: Time to Contact, Focus of Expansion, Direct Motion Vision Methods, Noise Gain

Lecture 3: Time to Contact, Focus of Expansion, Direct Motion Vision Methods, Noise Gain

Lecture 4: Fixed Optical Flow, Optical Mouse, Constant Brightness Assumption, Closed Form Solution

Lecture 4: Fixed Optical Flow, Optical Mouse, Constant Brightness Assumption, Closed Form Solution

Lecture 5: TCC and FOR MontiVision Demos, Vanishing Point, Use of VPs in Camera Calibration

Lecture 5: TCC and FOR MontiVision Demos, Vanishing Point, Use of VPs in Camera Calibration

Lecture 6: Photometric Stereo, Noise Gain, Error Amplification, Eigenvalues and Eigenvectors Review

Lecture 6: Photometric Stereo, Noise Gain, Error Amplification, Eigenvalues and Eigenvectors Review

Lecture 7: Gradient Space, Reflectance Map, Image Irradiance Equation, Gnomonic Projection

Lecture 7: Gradient Space, Reflectance Map, Image Irradiance Equation, Gnomonic Projection

Lecture 8: Shading, Special Cases, Lunar Surface, Scanning Electron Microscope, Green's Theorem

Lecture 8: Shading, Special Cases, Lunar Surface, Scanning Electron Microscope, Green's Theorem

Lecture 9: Shape from Shading, General Case - From First Order Nonlinear PDE to Five ODEs

Lecture 9: Shape from Shading, General Case - From First Order Nonlinear PDE to Five ODEs

Lecture 10: Characteristic Strip Expansion, Shape from Shading, Iterative Solutions

Lecture 10: Characteristic Strip Expansion, Shape from Shading, Iterative Solutions

Lecture 11: Edge Detection, Subpixel Position, CORDIC, Line Detection (US 6,408,109)

Lecture 11: Edge Detection, Subpixel Position, CORDIC, Line Detection (US 6,408,109)

Lecture 12: Blob Analysis, Binary Image Processing, Green's Theorem, Derivative and Integral

Lecture 12: Blob Analysis, Binary Image Processing, Green's Theorem, Derivative and Integral

Lecture 13: Object Detection, Recognition and Pose Determination, PatQuick (US 7,016,539)

Lecture 13: Object Detection, Recognition and Pose Determination, PatQuick (US 7,016,539)

Lecture 14: Inspection in PatQuick, Hough Transform, Homography, Position Determination, Multi-Scale

Lecture 14: Inspection in PatQuick, Hough Transform, Homography, Position Determination, Multi-Scale

Lecture 15: Alignment, PatMax, Distance Field, Filtering and Sub-Sampling (US 7,065,262)

Lecture 15: Alignment, PatMax, Distance Field, Filtering and Sub-Sampling (US 7,065,262)

Lecture 16: Fast Convolution, Low Pass Filter Approximations, Integral Images (US 6,457,032)

Lecture 16: Fast Convolution, Low Pass Filter Approximations, Integral Images (US 6,457,032)

Lecture 17: Photogrammetry, Orientation, Axes of Inertia, Symmetry, Orientation

Lecture 17: Photogrammetry, Orientation, Axes of Inertia, Symmetry, Orientation

Lecture 18: Rotation and How to Represent It, Unit Quaternions, the Space of Rotations

Lecture 18: Rotation and How to Represent It, Unit Quaternions, the Space of Rotations

Lecture 19: Absolute Orientation in Closed Form, Outliers and Robustness, RANSAC

Lecture 19: Absolute Orientation in Closed Form, Outliers and Robustness, RANSAC

Lecture 20: Space of Rotations, Regular Tessellations, Critical Surfaces, Binocular Stereo

Lecture 20: Space of Rotations, Regular Tessellations, Critical Surfaces, Binocular Stereo

Lecture 21: Relative Orientation, Binocular Stereo, Structure, Quadrics, Calibration, Reprojection

Lecture 21: Relative Orientation, Binocular Stereo, Structure, Quadrics, Calibration, Reprojection

Lecture 22: Exterior Orientation, Recovering Position & Orientation, Bundle Adjustment, Object Shape

Lecture 22: Exterior Orientation, Recovering Position & Orientation, Bundle Adjustment, Object Shape

Lecture 23: Gaussian Image, Solids of Revolution, Direction Histograms, Regular Polyhedra

Lecture 23: Gaussian Image, Solids of Revolution, Direction Histograms, Regular Polyhedra

Course Info

Instructor

Prof. Berthold Horn

Departments

Electrical Engineering and Computer Science

As Taught In

Fall 2020

Level

Undergraduate

Course Info

Instructor

Prof. Berthold Horn

Departments

Electrical Engineering and Computer Science

As Taught In

Fall 2020

Level

Undergraduate

Topics

Engineering
- Computer Science
  - Artificial Intelligence
  - Graphics and Visualization

Topics

Learning Resource Types

Exams

Lecture Notes

Problem Sets

Projects

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