Stereo Vision I Introduction to Computer Vision CSE 152 Lecture 13 - - PowerPoint PPT Presentation

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Stereo Vision I Introduction to Computer Vision CSE 152 Lecture 13 CSE152, Spr 07 Intro Computer Vision Announcements Midterm Percentage Score Out of Mean 67 52. 78 pts. Median 62 48 High 96 75 Low 24 18 CSE152,

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CSE152, Spr 07 Intro Computer Vision

Stereo Vision I

Introduction to Computer Vision CSE 152 Lecture 13

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CSE152, Spr 07 Intro Computer Vision

Announcements

Midterm
Out of
78 pts.

18 24 Low 75 96 High 48 62 Median 52. 67 Mean Score Percentage

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CSE152, Spr 07 Intro Computer Vision

Shape-from-X (i.e., Reconstruction)

Methods for estimating 3-D shape from

image data. X can be one (or more) of many cues.

– Stereo (two or more views, known viewpoints) – Motion (moving camera or object) – Shading – Changing lighting (Photometric Stereo) – Texture variation – Focus/blur – Prior knowledge/context – structured light/lasers

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CSE152, Spr 07 Intro Computer Vision

Binocular Stereopsis: Mars

Given two images of a scene where relative locations of cameras are known, estimate depth

f all common scene points.

Two images of Mars

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CSE152, Spr 07 Intro Computer Vision

Mar Rovers: Spirit and Opportunity

Four pairs

f stereo

cameras

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CSE152, Spr 07 Intro Computer Vision

An Application: Mobile Robot Navigation The Stanford Cart,

H. Moravec, 1979.

The INRIA Mobile Robot, 1990.

Courtesy O. Faugeras and H. Moravec.

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CSE152, Spr 07 Intro Computer Vision

Commercial Stereo Heads

Trinocular stereo Trinocular stereo Binocular stereo Binocular stereo

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CSE152, Spr 07 Intro Computer Vision

Stereo can work well

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CSE152, Spr 07 Intro Computer Vision

Need for correspondence

Truco Fig. 7.5

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CSE152, Spr 07 Intro Computer Vision

Triangulation

Nalwa Fig. 7.2

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CSE152, Spr 07 Intro Computer Vision

Stereo Vision Outline

Offline: Calibrate cameras & determine

“epipolar geometry”

Online
1. Acquire stereo images
2. Rectify images to convenient epipolar geometry
3. Establish correspondence
4. Estimate depth

A B C D

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CSE152, Spr 07 Intro Computer Vision

BINOCULAR STEREO SYSTEM Estimating Depth

Z X (0,0) (d,0) Z=f XL XR DISPARITY (XL - XR) Z = (f/XL) X Z= (f/XR) (X-d) (f/XL) X = (f/XR) (X-d) X = (XLd) / (XL - XR) Z = d f (XL - XR) X = d XL (XL - XR) (Adapted from Hager)

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CSE152, Spr 07 Intro Computer Vision

Reconstruction: General 3-D case

Linear Method:

find P such that

Non-Linear Method: find Q minimizing

where q=MQ and q’=M’Q Goal: Given two image measurements p and p’, estimate P.

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CSE152, Spr 07 Intro Computer Vision

Two Approaches

1. Feature-Based

– From each image, process “monocular” image to obtain image features or cues(e.g., corners, lines). – Establish correspondence between the detected features.

2. Area-Based

– Directly compare image regions between the two images.

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CSE152, Spr 07 Intro Computer Vision

Human Stereopsis: Binocular Fusion How are the correspondences established? Julesz (1971): Is the mechanism for binocular fusion a monocular process or a binocular one??

There is anecdotal evidence for the latter (camouflage).
Random dot stereograms provide an objective answer

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CSE152, Spr 07 Intro Computer Vision

Random Dot Stereograms

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CSE152, Spr 07 Intro Computer Vision