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Uedu Open / Machine Vision
6.801

Machine Vision

Prof. Berthold Horn | Fall 2004
Data Science, Analytics & Computer Technology AI Machine Learning Computer Science Engineering Electrical Engineering Systems Engineering Artificial Intelligence
前往原始課程
CC BY-NC-SA 4.0
課程簡介
Machine Vision provides an intensive introduction to the process of generating a symbolic description of an environment from an image. Lectures describe the physics of image formation, motion vision, and recovering shapes from shading. Binary image processing and filtering are presented as preprocessing steps. Further topics include photogrammetry, object representation alignment, analog VLSI and computational vision. Applications to robotics and intelligent machine interaction are discussed.
課程資訊
來源MIT 開放式課程
科系Electrical Engineering and Computer Science
語言English
影片數23
課程影片 (23)
1
Lecture 1: Introduction to Machine Vision
Lecture 1: Introduction to Machine Vision
2
Lecture 2: Image Formation, Perspective Projection, Time Derivative, Motion Field
Lecture 2: Image Formation, Perspective Projection, Time Derivative, Motion Field
3
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
4
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
5
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
6
Lecture 6: Photometric Stereo, Noise Gain, Error Amplification, Eigenvalues and Eigenvectors Review
Lecture 6: Photometric Stereo, Noise Gain, Error Amplification, Eigenvalues and Eigenvectors Review
7
Lecture 7: Gradient Space, Reflectance Map, Image Irradiance Equation, Gnomonic Projection
Lecture 7: Gradient Space, Reflectance Map, Image Irradiance Equation, Gnomonic Projection
8
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
9
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
10
Lecture 10: Characteristic Strip Expansion, Shape from Shading, Iterative Solutions
Lecture 10: Characteristic Strip Expansion, Shape from Shading, Iterative Solutions
11
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)
12
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
13
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)
14
Lecture 14: Inspection in PatQuick, Hough Transform, Homography, Position Determination, Multi-Scale
Lecture 14: Inspection in PatQuick, Hough Transform, Homography, Position Determination, Multi-Scale
15
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)
16
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)
17
Lecture 17: Photogrammetry, Orientation, Axes of Inertia, Symmetry, Orientation
Lecture 17: Photogrammetry, Orientation, Axes of Inertia, Symmetry, Orientation
18
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
19
Lecture 19: Absolute Orientation in Closed Form, Outliers and Robustness, RANSAC
Lecture 19: Absolute Orientation in Closed Form, Outliers and Robustness, RANSAC
20
Lecture 20: Space of Rotations, Regular Tessellations, Critical Surfaces, Binocular Stereo
Lecture 20: Space of Rotations, Regular Tessellations, Critical Surfaces, Binocular Stereo
21
Lecture 21: Relative Orientation, Binocular Stereo, Structure, Quadrics, Calibration, Reprojection
Lecture 21: Relative Orientation, Binocular Stereo, Structure, Quadrics, Calibration, Reprojection
22
Lecture 22: Exterior Orientation, Recovering Position & Orientation, Bundle Adjustment, Object Shape
Lecture 22: Exterior Orientation, Recovering Position & Orientation, Bundle Adjustment, Object Shape
23
Lecture 23: Gaussian Image, Solids of Revolution, Direction Histograms, Regular Polyhedra
Lecture 23: Gaussian Image, Solids of Revolution, Direction Histograms, Regular Polyhedra