6.801 Machine Vision (Fall 2020, MIT OCW): Lecture 06 - Photometric Stereo, Noise Gain, Error Application, Eigenvalues and Eigenvectors Review

6.801 Machine Vision

6.801 Machine Vision (Fall 2020, MIT OCW). Instructor: Prof. Berthold Horn. This course is an introduction to the process of generating a symbolic description of the environment from an image. It covers the physics of image formation, image analysis, binary image processing, and filtering. Machine vision has applications in robotics and the intelligent interaction of machines with their environment. Students taking the graduate version complete additional assignments. (from ocw.mit.edu)

Lecture 06 - Photometric Stereo, Noise Gain, Error Application, Eigenvalues and Eigenvectors Review

Instructor: Prof. Berthold Horn. In this lecture, Prof. Horn speaks about the applications of linear algebra to motion estimation and noise gain. He also discusses Lambertian Objects, Lambert's Law, and brightness.

Go to the Course Home or watch other lectures:

Lecture 01 - Introduction to Machine Vision

Lecture 02 - Image Formation, Perspective Projection, Time Derivative, Motion Field

Lecture 03 - Time to Contact, Focus of Expansion, Direct Motion Vision Methods, Noise Gain

Lecture 04 - Fixed Optical Flow, Optical Mouse, Constant Brightness Assumption, Closed Form Solution

Lecture 05 - TCC and FOR MontiVision Demos, Vanishing Point, Use of VPs in Camera Calibration

Lecture 06 - Photometric Stereo, Noise Gain, Error Application, Eigenvalues and Eigenvectors Review

Lecture 07 - Gradient Space, Reflectance Map, Image Irradiance Equation, Gnomonic Projection

Lecture 08 - Shading, Special Cases, Lunar Surface, Scanning Electron Microscope, Green's Theorem

Lecture 09 - Shape from Shading, General Case - From First Order Nonlinear PDE to Five ODEs

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

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