Starting a new Lecture Notes Series on MIT 6.801 Machine Vision, Fall 2020
.png)
.png)
Youtube Lecture Playlist CreditsChannel Name: MIT OpenCourseWare
So Let Us Start to This Journey of Learning
MIT 6.801 Machine Vision, Fall 2020 By Lecture Notes together!
Lecture 1: Lecture 1: Introduction to Machine Vision
Lecture 4: Lecture 4: Fixed Optical Flow, Optical Mouse, Constant Brightness Assumption, Closed Form Solution
Lecture 5: Lecture 5: TCC and FOR MontiVision Demos, Vanishing Point, Use of VPs in Camera Calibration
Lecture 6: Lecture 6: Photometric Stereo, Noise Gain, Error Amplification, Eigenvalues and Eigenvectors Review
Lecture 7: Lecture 7: Gradient Space, Reflectance Map, Image Irradiance Equation, Gnomonic Projection
Lecture 8: Lecture 8: Shading, Special Cases, Lunar Surface, Scanning Electron Microscope, Green's Theorem
Lecture 9: Lecture 9: Shape from Shading, General Case - From First Order Nonlinear PDE to Five ODEs
Lecture 12: Lecture 12: Blob Analysis, Binary Image Processing, Green's Theorem, Derivative and Integral
Lecture 13: Lecture 13: Object Detection, Recognition and Pose Determination, PatQuick (US 7,016,539)
Lecture 14: Lecture 14: Inspection in PatQuick, Hough Transform, Homography, Position Determination, Multi-Scale
Lecture 15: Lecture 15: Alignment, PatMax, Distance Field, Filtering and Sub-Sampling (US 7,065,262)
Lecture 16: Lecture 16: Fast Convolution, Low Pass Filter Approximations, Integral Images (US 6,457,032)
Lecture 20: Lecture 20: Space of Rotations, Regular Tessellations, Critical Surfaces, Binocular Stereo
Lecture 21: Lecture 21: Relative Orientation, Binocular Stereo, Structure, Quadrics, Calibration, Reprojection