lecture nine image processing. 2 learning outcomes by the end of the lecture you will be familiar...

Lecture Nine

Image processing

2

Learning outcomes

By the end of the lecture you will be familiar with:

• The importance of image processing in multi-media• The range of application areas in which image

processing is used• Have an understanding of some of the basic

principles and concepts• Image enhancement techniques

3

Introduction

• What is Image Processing / Computer Vision• Application areas• An Image model• Image bands• Sampling and quantization• Elements of an image processing system• Image acquisition• Image enhancement

4

What is image processing…

• Covers those techniques concerned with obtaining information from imagery

• Not limited to visible band, but included for example, infra-red images

• Encompasses studies in: computer science, physics, electronics, mathematics

• Main concern of image processing is the manipulation and analysis of pictures by computer

5


• Main topics include:– digitization, coding / compression– enhancement, restoration– segmentation, description– recognition, analysis– feature extraction, interpertation

6


• Graphics vs. Image processing vs. Computer Vision

Graphics

Image processing

Computer vision

Circle (20, 20)

Triangle (50,50)

Square (30,40)

Circle (20, 20)

Triangle (50,50)

Square (30,40)

7


• The computer vision hierarchy:

Data

Knowledge

Dec

reas

ing

data

Incr

easi

ng k

now

ledg

e

High

Medium

Low

Computer vision

Pattern recognition

Interpertation

Feature extraction

Image analysis

Image enhancement

Image processing

8

Applications

• Medical image processing– enhancement of X-ray, CT, MRI data– 3D visualization of scans– Analysis of shape and size of

structures• Factory automation

– PCB inspection– Quality control– Robotics

• Satelte Imagery– Classification of terrain– Detection of resources– Automatic map-making

• Military– Target tracking

• Video conferencing• Secutiry

– Finger/hand print analysis– Eye retina comparision

• Weather mapping• Documentation reading• Licence plate inspection• Tax disk inspection• Face recognition

9

Image model…

• An image is a picture, photograph, display or other form

• A digital image is a sampled and quantized representation of an scene

• Sampling is achieved by averaging the brightness of small patches in an image – pixels

• Quantization corresponds to assigning a (discrete) value to the brightness of a pixel (i.e. a greylevel)

10

Image model…•Typical values:

•Image size 256x256 512x512

•Greyscale – 0.255 (16 bits for MRI)

•Memory implications???

•Axis convention

•Image [row][col]

Image origin (0,0)

Image size 256x256

Pixel

11

Image bands

• A scene can have several images associated with it

• Colour images formed from three separate components, (RGB)

• Multispectral scanners flown in aircraft or satellites typically gather between 3 and 11 images of a scene

• Each individual image is known as a band

12

Elements of an image processing system…

ADCFrame store

DAC

DAC

DAC

Monitor

Image processing software

Camera

13


• Illumination– The success of most industrial image processing

systems is fundamentally based on adequate illumination

– Uncontrolled light – a particular challenge– Objects positioned between camera and light –

silhoutte of the object– Relative position of object, light and camera

important– For moving scenes, flashing strobe light us used

to “freeze” the image

14


• Processing– Computer will acquire and process image data– Reads sensors– Usually requires special-purpose

computers/hardware– Typical software development environment: library

of standard procedures, tools for realising new algorithms, (high-level language compiler, debugger, etc.) and an appropriate U.I.

15

Image acquisition…

• Real-time capture– 25 images / second for no “flicker”– 1 second of 512*512 mono images requires 6.25

Mb of storage

• Range images– Light intensity is not captured but object distance

from the sensor is modelled

16


• Scanners– Generally capture a still, flat image with

considerable accuracy– Resolution 100 dpi to 1200dpi +– Use a single row of CCDs (eg 2048) to collect data– Main problems:

• Only still images• Mechanical operation – may not be reliable, some hand

held

17


• Satellite imagery– Used in military, meteorological, geographical and

agricultural applications– Typically scan 6 horizontal lines at a time and

produce images of very high quality 2340*3380 8 bits per pixel

– Resolution varies depending on height of satellite (or aircraft), camera/scanning technology and weather conditions

– Current technology may be able to read a car number plate at an altitude of 180 miles

18


• Ranging devices– Ultrasound radar: short range (up to 40m) image

collection– Laser radar: pulses of light are transmitted at

points equivalent to pixels on the image – the transmitter is switched off and the reciever ‘sees’ the increase in light intensity and calculates distance from time taken for beam to return

19

Image enhancement

Image enhancement refers to any technique that:• Improves or modifies the image data

– either for purposes of subsequent visual evaluation or for further numerical processing.

Image enhancement techniques include: – grey-level and contrast manipulation– noise reduction– edge sharpening/detection

Image enhancements carried out by:– point- and region-based operations. – Point operations modify pixels of an image based on the

value of the pixel.

20

Point operations…

• Aim– To emphasise or suppress grey-levels

• Grey-level smoothing• Emphasising grey-level differences• Sharpening grey-level steps

• How?– Pass a operator matrix over the image– Assign a new value to a pixel– New value determined by the surrounding pixels

21

Greylevel Smoothing

• Used to smooth edges and reduce noise• Noise can be introduced in the image acquisition or

transmission stages• Operations :

– Mean – Min – Max– Median

• Can unfortunately remove fine detail, may need to emphasise edges first!

22

Edge detection

• Also uses operators to calculate new pixel values• Utilises areas of sharp contrasts within the image

– Looks at gradients within the image– Edges are characterised by large slopes in the image

function f(x,y)

x

y

6

12

2 4

2

6

Gradient 6/2 = 3

23

Emphasising greylevel differences

• Prewitt operator

• Sobel operator1 2 1

0 0 0

-1 -2 -1

-1 0 1

-2 0 2

-1 0 1

1 2 1

0 0 0

-1 -2 -1

-1 0 1

-2 0 2

-1 0 1

24

How do edge operators work?

• Sobel operator• Operation

– Image data is a series of grey-level values

– The horizontal and vertical operators are passed over the image with the centre of the matrix on each pixel

– New value for the pixel calculated and stored accordingly

1 2 1

0 0 0

-1 -2 -1

-1 0 1

-2 0 2

-1 0 1

123 65 78 95 123

45 96 256 78 36

147 56 96 32 78

65 125 86 35 69

78 148 248 75 69

25

How do edge operators work?

• Centred around 96 we would compute:

X= 123*-1 + 65*0 + 78*1 + 45*-2 +96*0 + 256*2 +147*-1 + 56*0 + 96*1

Y= 123*1 + 65*2 + 72*1 + 45*0 +96*0 + 256*0 + 147*-1 + 56*-2 + 96*-1

Output = x2 + y2 new value = 327 (rounded down to 256)

26Prewitt Sobel

Pattern Recognition

28

Introduction

• The purpose of pattern recognition is to place objects in a given world into categories

• The interface between the world and the pattern recognition system is provided by sensors

29

Pattern Recognition Procedure

• The first step of the procedure extracts features from the input data which characterise the objects.

• Based on these features, the objects are identified and sorted into classes.

30

Labels

• In order to sort the objects, the system needs information concerning the features of the objects i.e. the system needs a label

Training

SourceFeature

ExtractionClassifi-cationWorld Decision

yellowredlongbentround...

AppleBananaPear...

Labeling

31

Types of Pattern Recognition System

• Un-Supervised

These are able to generate their labels themselves, assigning them to objects with similar features which could belong to the same class. These systems cannot recognise the object they are analysing.

32

• SupervisedThese systems are ‘taught’ such information as ‘this is a banana’. The work for this system is divided into two stages:

• Training step - requires a teacher who describes the properties of each class.

• Classification step - compares the features of an actual object with those values which have been taught. The object is assigned to the class which fits best.

Types of Pattern Recognition System

33

Problem with second approach

If someone inserts a foreign object into a fruit recognising system (e.g. a calculator), then there is a class into which the calculator ‘fits better’ than any other. This can be overcome by introducing a rejection level which tests the limits of similarity.

34

Feature Space

• The features extracted from the input data form a feature space

blueblue

greengreen

yellowyellow

redred

AppleApple

PlumPlum

BananaBanana

OrangeOrange

colourcolour

compactnesscompactness(surface area:Volume)(surface area:Volume)

35

Problems with the Feature Space

• Too few or unsuitable features results in classes which are not separable– If an appropriate choice of features is not possible, or

too expensive, then the aim should be to use features leading to a minimum classification error.

– To avoid the classification errors, it may be necessary to ‘reduce’ the world, e.g. restrict the colour of apples to green. If this is not practical, then an additional feature must be introduced,e.g. surface texture.

36

Review

During this lecture we have looked at:– Image processing – The image model– Sampling and quantization– The image processing system– Image acquisition– Point operations– Image enhancement– Pattern recognition

37

Questions?

lecture nine image processing. 2 learning outcomes by the end of the lecture you will be familiar...

Documents