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# Machine Learning (2) Notes | EduRev

## : Machine Learning (2) Notes | EduRev

``` Page 1

Recap: Multiple Views and Motion
• Epipolar geometry
– Relates cameras in two positions
– Fundamental matrix maps from a point in one image to a line (its
epipolar line) in the other
– Can solve for F given corresponding points (e.g., interest points)
• Stereo depth estimation
– Estimate disparity by finding corresponding points along scanlines
– Depth is inverse to disparity
• Motion Estimation
– By assuming brightness constancy, truncated Taylor expansion leads to
simple and fast patch matching across frames
– Assume local motion is coherent
– “Aperture problem” is resolved by coarse to fine approaches and
iterative refinement

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t
T
I v u I
Page 2

Recap: Multiple Views and Motion
• Epipolar geometry
– Relates cameras in two positions
– Fundamental matrix maps from a point in one image to a line (its
epipolar line) in the other
– Can solve for F given corresponding points (e.g., interest points)
• Stereo depth estimation
– Estimate disparity by finding corresponding points along scanlines
– Depth is inverse to disparity
• Motion Estimation
– By assuming brightness constancy, truncated Taylor expansion leads to
simple and fast patch matching across frames
– Assume local motion is coherent
– “Aperture problem” is resolved by coarse to fine approaches and
iterative refinement

? ? 0 ? ? ? ?
t
T
I v u I
Machine Learning

Computer Vision
James Hays, Brown

Slides:  Isabelle Guyon,
Erik Sudderth,
Mark Johnson,
Derek Hoiem
Photo: CMU Machine Learning
Department protests G20
Page 3

Recap: Multiple Views and Motion
• Epipolar geometry
– Relates cameras in two positions
– Fundamental matrix maps from a point in one image to a line (its
epipolar line) in the other
– Can solve for F given corresponding points (e.g., interest points)
• Stereo depth estimation
– Estimate disparity by finding corresponding points along scanlines
– Depth is inverse to disparity
• Motion Estimation
– By assuming brightness constancy, truncated Taylor expansion leads to
simple and fast patch matching across frames
– Assume local motion is coherent
– “Aperture problem” is resolved by coarse to fine approaches and
iterative refinement

? ? 0 ? ? ? ?
t
T
I v u I
Machine Learning

Computer Vision
James Hays, Brown

Slides:  Isabelle Guyon,
Erik Sudderth,
Mark Johnson,
Derek Hoiem
Photo: CMU Machine Learning
Department protests G20
It is a rare criticism of elite American university
students that they do not think big enough. But that is
exactly the complaint from some of the largest
technology companies and the federal government.
At the heart of this criticism is data. Researchers and
workers in fields as diverse as bio-technology,
astronomy and computer science will soon find
themselves overwhelmed with information.
The next generation of computer scientists has to
think in terms of what could be described as Internet
scale.
New York Times
Training to Climb an Everest of Digital
Data.
By Ashlee Vance.
Published: October 11, 2009

Page 4

Recap: Multiple Views and Motion
• Epipolar geometry
– Relates cameras in two positions
– Fundamental matrix maps from a point in one image to a line (its
epipolar line) in the other
– Can solve for F given corresponding points (e.g., interest points)
• Stereo depth estimation
– Estimate disparity by finding corresponding points along scanlines
– Depth is inverse to disparity
• Motion Estimation
– By assuming brightness constancy, truncated Taylor expansion leads to
simple and fast patch matching across frames
– Assume local motion is coherent
– “Aperture problem” is resolved by coarse to fine approaches and
iterative refinement

? ? 0 ? ? ? ?
t
T
I v u I
Machine Learning

Computer Vision
James Hays, Brown

Slides:  Isabelle Guyon,
Erik Sudderth,
Mark Johnson,
Derek Hoiem
Photo: CMU Machine Learning
Department protests G20
It is a rare criticism of elite American university
students that they do not think big enough. But that is
exactly the complaint from some of the largest
technology companies and the federal government.
At the heart of this criticism is data. Researchers and
workers in fields as diverse as bio-technology,
astronomy and computer science will soon find
themselves overwhelmed with information.
The next generation of computer scientists has to
think in terms of what could be described as Internet
scale.
New York Times
Training to Climb an Everest of Digital
Data.
By Ashlee Vance.
Published: October 11, 2009

Machine learning: Overview
• Core of ML: Making predictions or decisions
from Data.
• This overview will not go in to depth about
the statistical underpinnings of learning
methods. We’re looking at ML as a tool. Take
CS 142: Introduction to Machine Learning to

Page 5

Recap: Multiple Views and Motion
• Epipolar geometry
– Relates cameras in two positions
– Fundamental matrix maps from a point in one image to a line (its
epipolar line) in the other
– Can solve for F given corresponding points (e.g., interest points)
• Stereo depth estimation
– Estimate disparity by finding corresponding points along scanlines
– Depth is inverse to disparity
• Motion Estimation
– By assuming brightness constancy, truncated Taylor expansion leads to
simple and fast patch matching across frames
– Assume local motion is coherent
– “Aperture problem” is resolved by coarse to fine approaches and
iterative refinement

? ? 0 ? ? ? ?
t
T
I v u I
Machine Learning

Computer Vision
James Hays, Brown

Slides:  Isabelle Guyon,
Erik Sudderth,
Mark Johnson,
Derek Hoiem
Photo: CMU Machine Learning
Department protests G20
It is a rare criticism of elite American university
students that they do not think big enough. But that is
exactly the complaint from some of the largest
technology companies and the federal government.
At the heart of this criticism is data. Researchers and
workers in fields as diverse as bio-technology,
astronomy and computer science will soon find
themselves overwhelmed with information.
The next generation of computer scientists has to
think in terms of what could be described as Internet
scale.
New York Times
Training to Climb an Everest of Digital
Data.
By Ashlee Vance.
Published: October 11, 2009

Machine learning: Overview
• Core of ML: Making predictions or decisions
from Data.
• This overview will not go in to depth about
the statistical underpinnings of learning
methods. We’re looking at ML as a tool. Take
CS 142: Introduction to Machine Learning to