First Principles of Computer Vision

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First Principles of Computer Vision is a lecture series presented by Shree Nayar who is faculty in the Computer Science Department, School of Engineering and Applied Sciences, Columbia University. Computer Vision is the enterprise of building machines that “see.” This series focuses on the physical and mathematical underpinnings of vision and has been designed for students, practitioners, and enthusiasts who have no prior knowledge of computer vision.

fpcv.cs.columbia.edu

First Principles of Computer Vision

94,148 views 4 years ago

First Principles of Computer Vision is a lecture series presented by Shree Nayar, T. C. Chang Professor of Computer Science in the School of Engineering at Columbia University. Computer Vision is the enterprise of building machines that “see.” This series focuses on the physical and mathematical underpinnings of vision and has been designed for students, practitioners and enthusiasts who have no prior knowledge of computer vision.

Introduction

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Overview | Introduction

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What is Computer Vision? | Introduction

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What is Vision Used For? | Introduction

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How Do Humans Do It? | Introduction

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Topics Covered | Introduction

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About the Lecture Series | Introduction

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Image Formation | Image Sensing | Binary Images

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Overview | Image Formation

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Pinhole and Perspective Projection | Image Formation

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Image Formation using Lenses | Image Formation

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Depth of Field | Image Formation

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Lens Related Issues | Image Formation

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Wide Angle Cameras | Image Formation

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Image Processing I | Image Processing II

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Overview | Image Processing I

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Pixel Processing | Image Processing I

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LSIS and Convolution | Image Processing I

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Linear Image Filters | Image Processing I

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Non-Linear Image Filters | Image Processing I

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Template Matching by Correlation | Image Processing I

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Edge Detection | Boundary Detection | SIFT Detector

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Overview | Edge Detection

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What is an Edge? | Edge Detection

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Edge Detection Using Gradients | Edge Detection

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Edge Detection Using Laplacian | Edge Detection

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Canny Edge Detector | Edge Detection

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Corner Detection | Edge Detection

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Image Stitching | Face Detection

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Overview | Image Stitching

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2x2 Image Transformations | Image Stitching

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3x3 Image Transformations | Image Stitching

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Computing Homography | Image Stitching

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Dealing with Outliers: RANSAC | Image Stitching

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Warping and Blending Images | Image Stitching

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Radiometry and Reflectance | Photometric Stereo | Shape from Shading

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Overview | Radiometry and Reflectance

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Radiometric Concepts | Radiometry and Reflectance

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Scene Radiance and Image Irradiance | Radiometry and Reflectance

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BRDF: Bidirectional Reflectance Distribution Function

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Reflectance Models | Radiometry and Reflectance

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Reflection from Rough Surfaces | Radiometry and Reflectance

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Depth from Defocus | Active Illumination Methods

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Overview | Depth from Defocus

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Point Spread Function | Depth from Defocus

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Depth from Focus | Depth from Defocus

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Depth from Defocus | Depth from Defocus

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Overview | Active Illumination Methods

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Photometric Stereo Systems | Active Illumination Methods

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Camera Calibration | Uncalibrated Stereo

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Overview | Camera Calibration

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Linear Camera Model | Camera Calibration

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Camera Calibration | Camera Calibration

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Intrinsic and Extrinsic Matrices | Camera Calibration

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Simple Stereo | Camera Calibration

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Overview | Uncalibrated Stereo

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Optical Flow | Structure from Motion | Object Tracking

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Overview | Optical Flow

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Motion Field and Optical Flow | Optical Flow

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Optical Flow Constraint Equation | Optical Flow

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Lucas-Kanade Method | Optical Flow

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Coarse-to-Fine Flow Estimation | Optical Flow

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Application of Optical Flow | Optical Flow

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Image Segmentation

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Overview | Image Segmentation

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Segmentation by humans | Image Segmentation

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Segmentation as Clustering | Image Segmentation

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k-Means Segmentation | Image Segmentation

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Mean-Shift Segmentation | Image Segmentation

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Graph Based Segmentation | Image Segmentation

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Appearance Matching

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Overview | Appearance Matching

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Shape vs. Appearance | Appearance Matching

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Learning Appearance | Appearance Matching

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Principal Component Analysis | Appearance Matching

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Finding Principal Components | Appearance Matching

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PCA and SVD | Appearance Matching

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Neural Networks

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Overview | Neural Networks

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Perceptron | Neural Networks

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Perceptron Network | Neural Networks

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Activation Function | Neural Networks

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Neural Network | Neural Networks

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Gradient Descent | Neural Networks

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