INTRODUCTION TO

IMAGE PROCESSING

Politeknik Kota Malang

Aditya Kurniawan, S.ST

© 2011

Aditya@poltekom.ac.id

Image Processing

Computer Vision

Robot Vision

WHAT IS THE DIFFERENCE ?

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A process to an image focusing on transforming, encoding and transmitting

the image.

IMAGE PROCESSING

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IMAGE IMAGE

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Computer Vision => media to know the world visually supported by

knowledge strength by computational instrument.

COMPUTER VISION

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IMAGE Description /

humanized

information

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Robot Vision => a machine with ability to see his environment designed with

workflow algorithm, so it can make a decision and finish the job automatically.

ROBOT VISION

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IMAGE Action

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BLOCK DIAGRAM

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Image

Prosesing

Artificial

Intelligent

+ Computer

Vision

Hardware

+ Robot

Vision

IT guys

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Image

Prosesing

Artificial

Intelligent

+ Computer

Vision

Hardware

+ Robot

Vision

MECHATRONIC guys

BLOK DIAGRAM

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Modern digital technology has made it possible to manipulate multi-dimensional signals with systems that range from simple digital circuits to advanced parallel computers. The goal of this manipulation can be divided into three categories:

Image Processing

Image in image out

Image Analysis

Image in measurements out

Image Understanding

Image in high-level description out

INTRODUCTION

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INTRODUCTION

Image Processing

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INTRODUCTION

Image Analysis

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INTRODUCTION

Image

Understanding

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We begin with certain basic definitions.

An image defined in the “real world” is

considered to be a function of two real variables, for

example, a(x,y) with a as the amplitude (e.g. brightness)

of the image at the real coordinate position (x,y).

INTRODUCTION

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An image may be considered to contain sub-

images sometimes referred to as regions–of–interest, ROIs,

or simply regions. This concept reflects the fact that

images frequently contain collections of objects each of

which can be the basis for a region.

INTRODUCTION

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INTRODUCTION

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Coordinate

Position

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INTRODUCTION

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Regions

Of

Interest

X1

X2

Y1 Y2

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INTRODUCTION

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Regions

Of

Interest

Regions

Of

Interest X1

X2

Y1 Y2 Y3 Y4

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In a sophisticated image processing system it should be possible to

apply specific image processing operations to selected regions. Thus one part

of an image (region) might be processed to suppress motion blur while

another part might be processed to improve color rendition.

INTRODUCTION

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INTRODUCTION

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Brightness

enhancement

Contrast

enhancement

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A digital image a[m,n] described in a 2D discrete space is derived

from an analog image a(x,y) in a 2D continuous space through a sampling

process that is frequently referred to as digitization. For now we will look at

some basic definitions associated with the digital image. The effect of

digitization is shown in Figure 1.

DIGITAL IMAGE DEFINITIONS

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DIGITAL IMAGE DEFINITIONS

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Sample range of colour in reality world is an analog signal

0,2 nM 3,2 nM

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DIGITAL IMAGE DEFINITIONS

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The idea of digitization

Is taking sample of a range or an analog value

0,2 nM 3,2 nM

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DIGITAL IMAGE DEFINITIONS

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The idea of digitization

Is taking sample of a range or an analog value

0,2 nM 3,2 nM

00 01 10 11

2 bit colour representation

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DIGITAL IMAGE DEFINITIONS

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The 2D continuous image a(x,y) is divided into N rows and M columns. The intersection of a row and a column is termed a pixel (pixel comes from “picture element”). The value assigned to the integer coordinates [m,n] with {m=0,1,2,…,M–1} and {n=0,1,2,…,N–1} is a[m,n]. In fact, in most cases a(x,y) which we might consider to be the physical signal that impinges on the face of a 2D sensor is actually a function of many variables including depth (z), color (l), and time (t). Unless otherwise stated, we will consider the case of 2D, monochromatic, static images in this chapter.

DIGITAL IMAGE DEFINITIONS

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DIGITAL IMAGE DEFINITIONS

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A pixel contain these information : a (x, y, z, l, t)

a = illumination / light exposure in a certain pixel

X = horizontal coordinate

Y = vertical coordinate

Z = depth

L = colour

T = time frame

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DIGITAL IMAGE DEFINITIONS

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A pixel contain these information : a (x, y, z, l, t)

a = illumination / light exposure in a certain pixel

Low

Light

exposure

High

Light

exposure

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DIGITAL IMAGE DEFINITIONS

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A pixel contain these information : a (x, y, z, l, t)

X, Y = 2 dimensional coordinate

X1

X2

Y1 Y2

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DIGITAL IMAGE DEFINITIONS

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A pixel contain these information : a (x, y, z, l, t)

Z = depth

surface

bottom

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DIGITAL IMAGE DEFINITIONS

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A pixel contain these information : a (x, y, z, l, t)

l = colour

Red

colour

Yellow

colour

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DIGITAL IMAGE DEFINITIONS

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Introduction to Image Processing

A pixel contain these information : a (x, y, z, l, t)

t = time frame Picture taken in a different time frame

t1 t2

t3 t4

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The image shown in Figure 1 has been divided into N = 16 rows and M = 16 columns. The value assigned to every pixel is the average brightness in the pixel rounded to the nearest integer value. The process of representing the amplitude of the 2D signal at a given coordinate as an integer value with L different gray levels is usually referred to as amplitude quantization or simply quantization.

DIGITAL IMAGE DEFINITIONS

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COMMON VALUES

There are standard values for the various

parameters encountered in digital image processing.

These values can be caused by video standards, by

algorithmic requirements, or by the desire to keep digital

circuitry simple. Table 1 gives some commonly

encountered values.

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Quite frequently we see cases of M=N=2K where {K = 8,9,10}. This can be motivated by digital circuitry or by the use of certain algorithms such as the (fast) Fourier transform.

The number of distinct gray levels is usually a power of 2, that is, L=2B where B is the number of bits in the binary representation of the brightness levels. When B>1 we speak of a gray-level image; when B=1 we speak of a binary image. In a binary image there are just two gray levels which can be referred to, for example, as “black” and “white” or “0” and “1”.

COMMON VALUES

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There is a variety of ways to classify and characterize image

operations. The reason for doing so is to understand what type of results we

might expect to achieve with a given type of operation or what might be the

computational burden associated with a given operation.

CHARACTERISTIC OF

IMAGE OPERATIONS

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Medical

Sharpen X-Ray result, Analysis of MRI, etc

Technology and Communications

Reduce noise from satellite image, video streaming

Game

Shadow effect on water surface, light effect, etc

Photography and Films

Contrast, brightness, illegal photo manipulations, etc

ADVANTAGES OF IMAGE

PROCESSING

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MEDICAL APPLICATION

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TECHNOLOGY AND

COMMUNICATIONS

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DIGITAL IMAGE

ACQUISITION PROCESS

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