Introduction

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1 Introduction • What IS computer vision? • Where do images come from? the analysis of digital images by a computer

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Introduction. What IS computer vision? Where do images come from?. the analysis of digital images by a computer. Applications. Medical Imaging. CT image of a patient’s abdomen. liver. kidney. kidney. Visible Man Slice Through Lung. 3D Reconstruction of the Blood Vessel Tree. - PowerPoint PPT Presentation

Transcript of Introduction

Page 1: Introduction

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Introduction

• What IS computer vision?

• Where do images come from?

the analysis of digital images by a computer

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Applications

• Medical Imaging

CT image of apatient’s abdomen

liver

kidney kidney

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Visible Man Slice Through Lung

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3D Reconstruction of the Blood Vessel Tree

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Slice of a Chicken Embryo’s Inner Ear

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CBIR of Mouse Eye Images for Genetic Studies

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Robotics

• 2D Gray-tone or Color Images

• 3D Range Images

“Mars” rover

What am I?

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Image Databases:

What categories of image databases exist today?

Images from my Ground-Truth collection.

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Abstract Regions for Object Recognition

Original Images Color Regions Texture Regions Line Clusters

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Documents:

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Insect Recognition for Ecology

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Original Video Frame Structure Regions

Color Regions Color-Merge Regions

Surveillance: Event Recognition in Aerial Videos

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Vision for Graphics:

Recent work of Steve Seitz (CSE) and Aaron Hertzmanon Computing the Geometry of Objects from Images.

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Some Applications from WACV• Face Detection / Skin Detection• Face Recognition• Gesture Recognition• Eye Gaze Estimation• Gender Classification• People Tracking• Group Behavior Recognition• Visual Navigation• Real-Time Precrash Vehicle Detection• Augmented Reality• Vehicle Inspection• Video de-Abstraction (save money on wedding videos)

• Analysis of Auroral Appearance over Canada• Video Endoscopy

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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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Goals of Image Analysis

• Segment the image into useful regions

• Perform measurements on certain areas

• Determine what object(s) are in the scene

• Calculate the precise location(s) of objects

• Visually inspect a manufactured object

• Construct a 3D model of the imaged object

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•The Three Stages of Computer Vision

• low-level

• mid-level

• high-level

image image

image features

features analysis

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Low-Level

blurring

sharpening

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Low-Level

Canny

ORT

Mid-Level

original image edge image

edge image circular arcs and line segments

datastructure

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Mid-level

K-meansclustering

original color image regions of homogeneous color

(followed byconnectedcomponentanalysis)

datastructure

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edge image

consistentline clusters

low-level

mid-level

high-level

Low- to High-Level

Building Recognition

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Difficulty of Computer Vision

• Computer vision is far from completely solved.

• There have been many successful systems used in real applications.

• There are lots of things that humans can do for which vision programs don’t come close to success.

Like what?

Can you name some?


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