Introduction What is “image processing and computer vision”? Image Representation.

Introduction

What is “image processing and computer vision”?Image Representation

Image Processing and Computer Vision: 1 2

What is “Image Processing and Computer Vision”?

Image Processingmanipulate image datagenerate another image

Computer Visionprocess image data

generate symbolic data

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Computer Vision Reconstruction

Recover 3D information from data Recognition

Detect and identify objects Understanding

What is happening in the scene?

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Historical overview 1920s

Coding images for transmission by telegraph (3 hours)

1960s Computers powerful enough to store

images and process in realistic times Space program

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1960s - 1970s Applications

Medical imaging Remote sensing Astronomy

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Today DTV Image interpretation Biometry GIS Human genome project

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Example images (1)

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Example images (2)

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Sample applications Character recognition (OCR)

Printed text, Hand-printed text, Cursive text

Biometry GIS

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Printed Text Characteristics

Regular shape Regular orientation Good contrast

Can compare characters with a prototype

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Position

No

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on

Template

Input Output

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Hand Printed Text Characteristics

Less regularity Must examine components of

character

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Cursive Text Totally irregular Careful analysis of strokes

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Biometry Using personal characteristics to

identify a person Fingerprints Face Iris DNA Gait etc

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Iris Scan Striations on iris are individually

unique Obvious applications:

Security PIN

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} fixed number of samples

Locate the eye in the head image

Radial resampling of iris

Numerical descriptionAnalysis

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GIS Earth/Planetary Observation

Monitoring Exploration

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Examples

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System Overview

Enhancement

Feature Extraction

Feature Recognition

Image Recognition

Captured data

Labels

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Image Representation Image capture Image quality measurements Image resolution Colour representation Camera calibration Parallels with human visual system

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Image Capture Many sources

Consider requirements of system Resolution

Type of data Transducer

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Representation Sampled data

Spatial Amplitude

On a rectangular array

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Image Resolution How many pixels

Spatial resolution How many shades of grey/colours

Amplitude resolution How many frames per second

Temporal resolution

Nyquist’s theorem

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Nyquist’s Theorem A periodic signal can be

reconstructed if the sampling interval is half the period

An object can be detected if two samples span its smallest dimension

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Spatial Resolution

n, n/2, n/4, n/8, n/16 and n/32 pixels on a side.

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Amplitude Resolution Humans can see:

About 40 shades of brightness About 7.5 million shades of colour

Cameras can see: Depends on signal to noise ratio 40 dB equates to about 20 shades

Images captured: 256 shades

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Shades of Grey

256, 16, 4 and 2 shades.

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Temporal Resolution Nyquist’s theorem for temporal

data How much does an object move

between frames? Can motion be understood

unambiguously?

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Colour Representation Newton

White light composed of seven colours

red, orange, yellow, green, blue, indigo, violet

Three primaries could approximate many colours

red, green, blue

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CIE Colour Diagram

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Other Colour Models YMCK IHS YCrCb etc

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Camera Calibration Link image co-ordinates and world

co-ordinates Extrinsic parameters

Location and orientation of camera with respect to a co-ordinate frame

Intrinsic parameters Relate pixel co-ordinates with camera

reference frame co-ordinates

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Extrinsic Parameters Camera’s

Location Orientation

With respect to world origin

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World frame

Camera frame

translate androtate

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Intrinsic Parameters Characterise

Optical Geometric Digital

properties of camera Relate

Image co-ordinates to camera co-ordinates

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Pinhole Camera

Image

ObjectOpticalcentre

Image and centre, object and centre are similar triangles.

f Z

Z

Yfy

Z

Xfx

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Distortionless If

no distortions uniform sampling

Co-ordinates linearly related offset and scale

s yo yyimy

sxoxximx

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Distorted Periphery is distorted

k2 = 0 is good enough

yxr

rkrkydy

rkrkxdx

222

42

211

42

211

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Parallels With Human Visual System Image capture

Retina Focussing

Cornea and lens Exposure

Iris and retina

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Summary Historical overview Sample applications Resolution Colour models Camera calibration

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640k ought to be enough for anybodyBill Gates, 1981