cse 455 computer vision autumn 2014

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CSE 455 Computer Vision Autumn 2014 Professor Linda Shapiro [email protected] TA: Ezgi Mercan [email protected] TA: Bilge Soran [email protected] 1

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CSE 455 Computer Vision Autumn 2014. Professor Linda Shapiro [email protected] TA: Ezgi Mercan [email protected] TA: Bilge Soran [email protected]. Introduction. What IS computer vision? Where do images come from?. The analysis of digital images by a computer. - PowerPoint PPT Presentation

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Page 1: CSE 455 Computer Vision Autumn 2014

CSE 455Computer Vision

Autumn 2014

Professor Linda Shapiro

[email protected]

TA: Ezgi Mercan [email protected]

TA: Bilge Soran [email protected]

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Page 2: CSE 455 Computer Vision Autumn 2014

Introduction

• What IS computer vision?

• Where do images come from?

The analysis of digital images by a computer

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You tell me!

Page 3: CSE 455 Computer Vision Autumn 2014

Applications: Medical Imaging

CT image of apatient’s abdomen

liver

kidney kidney

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Page 4: CSE 455 Computer Vision Autumn 2014

Visible Man Slice Through Lung

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Page 5: CSE 455 Computer Vision Autumn 2014

3D Reconstruction of the Blood Vessel Tree

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Page 6: CSE 455 Computer Vision Autumn 2014

Robotics• 2D Gray-tone or Color Images

• 3D Range Images

“Mars” rover

What am I?

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Page 7: CSE 455 Computer Vision Autumn 2014

Robot Soccer

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Google Driverless Car

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Page 9: CSE 455 Computer Vision Autumn 2014

Image Databases:

Images from my Ground-Truth collection:http://www.cs.washington.edu/research/imagedatabase/groundtruth

• Retrieve images containing trees

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Page 10: CSE 455 Computer Vision Autumn 2014

Original Images Color Regions Texture Regions Line Clusters

Some Features for Image Retrieval

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Documents:

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Page 13: CSE 455 Computer Vision Autumn 2014

Classified as Cal as Dor

Cal 114 72

Dor 70 133

Previous Classification Results:

Yor (Yor)

Classified as Cal as Yor

Cal 171 16

Yor 0 99

CalineuriaCalineuria (Cal) DoroneuriaDoroneuria (Dor)

Science

13UW and Oregon State University

Page 14: CSE 455 Computer Vision Autumn 2014

Soil Mesofauna

AchipteriaA BdellozoniumI BelbaA BelbaI CatoposurusA EniochthoniusA

EntomobrgaTM EpidamaeusA EpilohmanniaA EpilohmanniaD EpilohmanniaT HypochthoniusLA

HypogastruraA

IsotomaAIsotomaVI LiacarusRA MetrioppiaA

NothrusF

onychiurusAOppiellaA PeltenuialaA PhthiracarusA

PlatynothrusFPlatynothrusI

PtenothrixV

PtiliidA

QuadroppiaA

SiroVITomocerusA

TraychetesA XenillusA ZyqoribafulaA

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Original Video Frame

Structure RegionsColor Regions

Surveillance: Event Recognition in Aerial Videos

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Page 16: CSE 455 Computer Vision Autumn 2014

2D Face Detection

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Page 17: CSE 455 Computer Vision Autumn 2014

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Face Recognition

Glasgow University

Page 18: CSE 455 Computer Vision Autumn 2014

2D Object Recognition from “Parts”

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Oxford University

Page 19: CSE 455 Computer Vision Autumn 2014

Andy Serkis, Gollum, Lord of the Rings

Graphics: Special Effects

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Page 20: CSE 455 Computer Vision Autumn 2014

3D Reconstruction and Graphics Viewer

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Page 21: CSE 455 Computer Vision Autumn 2014

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3D Craniofacial Shape Analysis from Meshes of Children’s Heads

Page 22: CSE 455 Computer Vision Autumn 2014

Digital Breast Biopsy ImageShowing Regions of Interest

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Digital Image Terminology:

0 0 0 0 1 0 0 0 0 1 1 1 0 0 0 1 95 96 94 93 92 0 0 92 93 93 92 92 0 0 93 93 94 92 93 0 1 92 93 93 93 93 0 0 94 95 95 96 95

pixel (with value 94)

its 3x3 neighborhood

• binary image• gray-scale (or gray-tone) image• color image• multi-spectral image• range image• labeled image

region of medium intensity

resolution (7x7)

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Page 24: CSE 455 Computer Vision Autumn 2014

The Three Stages of Computer Vision

• low-level

• mid-level

• high-level

image image

image features

features analysis

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Page 25: CSE 455 Computer Vision Autumn 2014

Low-Level

blurring

sharpening

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Page 26: CSE 455 Computer Vision Autumn 2014

Low-Level

Canny

ORT

Mid-Level

original image edge image

edge image circular arcs and line segments

datastructure

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Page 27: CSE 455 Computer Vision Autumn 2014

Mid-level

K-meansclustering

original color image regions of homogeneous color

(followed byconnectedcomponentanalysis)

datastructure

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Page 28: CSE 455 Computer Vision Autumn 2014

edge image

consistentline clusters

low-level

mid-level

high-level

Low- to High-Level

Building Recognition28