lec 1 image processing

8/10/2019 lec 1 image processing

1/38

I. Background


2/38

1. Introduction

Historical:

1920: Bartlane cablepicture transmission line (London-New

York) reduced time for transmitting a picture from over a

week to ~ 3 hours, 5 distinct brightness levels,in 1929capability increased to 15 levels

--> Not considered digital image processing

1964: First meaningful IM using computers: using computer

techniquesfor improving images from a space probe at theJet Propulsion Lab(California) to correct distortion of

onboard television camera.


3/38

1. Introduction


4/38

1. Introduction

From 1964 on the field of image processing has

grown to cover many applications such as :

In medicine : e.g. x-ray picture

Biology: microscopic pictures

Geography: to find pollution patterns

Astronomy

Photography


5/38

1. Introduction

X-ray Image


6/38

1. IntroductionSatellite Image


7/38

1. Introduction


8/38

1. Introduction

Light and Electromagnetic Spectrum:

Most images are based on radiation from EMspectrum.

EM wavesare : a stream of massless particleseach traveling in a wavelike pattern at the speedof light. Each contains a certain amount of

energycalled photon. Spectral bands are grouped according to energy

per photon as in the following figure:


9/38

3.Digital Image Fundamentals

Light and the Electromagnetic Spectrum


10/38

1. Introduction

Other applications related to the use incomputer processing and machine perceptionare (following image processing!):

Automatic character recognition Industrial machine vision for assembly and

inspection

Military recognizance

Automatic recognition of fingerprints

Machine processing of aerial and satellite images forweather prediction


11/38

1. Introduction

Printed Circuit Board


12/38

1. Introduction

Colestrol 40xSurface of audio

CD1750x.


13/38

2. Fundamental Steps in Digital Image

Processing


14/38


Processing


15/38


Processing

Image acquisition:

Camera (monochrome or colour), line

scan camera,

Using Sensors


16/38


Processing Image acquisition:camera


17/38


Processing

Image acquisition: High Precision,

Single Sensor


18/38


Processing

Image acquisition: Using Sensor Array


19/38


Processing


20/38


Processing

Image Enhancement:Basic idea is to highlight

features that are obscured, e.g. contrast

enhancement..Very subjective!

Image Restoration: Based on mathematical or

probabilistic models..Rather objective!


21/38


Processing

Color Image Processing:Deals with color

models, basic color processing. Also used for

feature extraction.

Wavelets:Representing images in various

degrees of resolution. (used for imagecompression)


22/38


Processing

Image Compression:based on redundant

information within image. Apparent in file

extensions, e.g. JPEG (Joint PhotographicExpert Group)

Morphological Processing:Deals withtechniques for extracting image components

that are useful in the representation and

description of shape.


23/38


Processing


24/38


Processing

Segmentation: To partition image into

constituent parts or objects.

It is one of the most difficult tasks. E.g. extracting characters and words, extracting

faces, lips, ....


25/38


Processing


26/38


Processing

Feature Selection or Extraction: Transform raw

data into form suitable for computer processing

quantitative information basic for differentiating

one class of objects from others.

boundary or region representation orboth?

holes and corners are used in character

recognition.


27/38


Processing


28/38


Processing

Recognition: Assigning a label to an object based

on information of descriptors


29/38

3. Digital Image Fundamentals

Image Formation in the Eye

Representing Digital Images


30/38


Elements of Visual Perception


31/38


Elements of Visual Perception


32/38

3.Digital Image Fundamentals

Representing Digital Images

b = M x N x k

IF N=M

Then

b = N2x k


33/38

4.Digital Image Properties

Histograms:

of intensity (brightness values)

of gradient values


34/38

4.Digital Image Properties

Histograms: Examples:


35/38

5. Basic Concepts

Convolution

One-Dimensional

Two-Dimensional

Fourier Transform

1D Fourier Transform of continuous signals


2D Fourier Transform of discrete signals

2D Discrete Fourier Transform (DFT)


36/38

5. Basic Concepts

Convolution

1 1 11

1 1 19

1 1 1

* =

Convolution: Example


37/38

* =

1 1 1

1 8 1

1 1 1

Convolution: Example

5. Basic Concepts

Convolution


38/38

5. Basic Concepts

Fourier Transform



2D Fourier Transform of discrete signals

2D Discrete Fourier Transform (DFT)

lec 1 image processing

Documents

Transcript of lec 1 image processing