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Image Processing

Ch1: Introduction

Prepared by: Hanan Hardan

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Introduction

“One picture is worth more than ten thousand words”

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References

“Digital Image Processing”, Rafael C. Gonzalez & Richard E. Woods, Addison-Wesley, 2002

Much of the material that follows is taken from this book

“Machine Vision: Automated Visual Inspection and Robot Vision”, David Vernon, Prentice Hall, 1991

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Contents

This lecture will cover:

What is a digital image?

What is digital image processing?

State of the art examples of digital image processing

Key stages in digital image processing

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What is a Digital Image?

A digital image: is a representation of a two-dimensional image as a finite number of elements, each one has a particular location and value.. These element are called picture elements, image elements or pixels.

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What is a Digital Image? (cont…)

Pixels: Elements of the digital image , each has intensity.

Intensity of pixel: the amplitude غزارة of

gray level (in gray scale images)

1 pixel

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What is digital Image?

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What is digital Image?

An image can be defined as function of 2 variables , f(x,y), where x and y are spatial coordinates, and the amplitude of f at any pair of coordinates (x , y) is called the intensity of the image at that point

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What is digital image?

The image consists of finite number of pixels ( f(x,y) )

Every pixel Is an intersection between a row and a column.

Every pixel has intensity

pixel

Ex:

f(2,6)= 123

Refers to a pixel existing on the intersection between row 2 with column 6, and its intensity is 123.

Remember digitization implies that a digital image is an approximation of a real scene

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Digital image processing focuses on two major tasks

Improve image quality(pictorial information) for human perception and interpretation

Processing of image data for storage, transmission and representation for autonomous machine perception

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1. Computer Graphics: the creation of image

2. Image processing: enhancement or other manipulation of the image

3. Computer vision: analysis of the content

Image processing fields:

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What are digital image processing levels?

low level processes:

Input and output are images

Tasks: Primitive operations, such as, image processing to reduce noise, contrast enhancement and image sharpening

مثال صورة قديمة نريد تحسينها

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What are digital image processing levels?

Mid-Level Processes:

Inputs, generally, are images. Outputs are attributes extracted from those images (edges, contours, identity of individual objects)

Tasks:

Segmentation (partitioning an image into regions or objects)

Description of those objects to reduce them to a form suitable for computer processing

Classifications (recognition) of objects

صورة لكرسي نريد تعديلها حاسوبيا لنبرز حوافه : مثالHanan Hardan 13

What are digital image processing levels?

High-Level Processes

Input: Attributes Output: Understanding

Tasks: recognizing objects

Image analysis and computer vision(Analysis of the image content)

Examples: Scene understanding

صورة لمشتبه فيه نريد الحاسوب ان يتعرف عليه: مثال

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Uses of DIP

Image enhancement/restoration

Artistic effects

Medical visualisation

Law enforcement

Human computer interfaces

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Examples: Image Enhancement

One of the most common uses of DIP techniques: improve quality, remove noise etc

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Examples: The Hubble Telescope Launched in 1990 the Hubble telescope can take images of very distant objects

However, an incorrect mirror made many of Hubble’s images useless

Image processing techniques were used to fix this

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Examples: Artistic Effects

Artistic effects are used to make images more visually appealing, to add special effects and to make composite images

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Examples: Medicine Take slice from MRI (Magnetic Resounance Imaging) scan of a heart, and find boundaries between types of tissue

Image with gray levels representing tissue density

Use a suitable filter to highlight edges

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Examples: GIS

Geographic Information Systems

Digital image processing techniques are used extensively to manipulate satellite imagery

Terrain classification (التضاريس)

Meteorology (األرصاد الجوية)

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Examples: Law Enforcement

Image processing techniques are used extensively by law enforcers

Number plate recognition for speed cameras

Fingerprint recognition

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Examples: HCI

Try to make human computer interfaces more natural

Face recognition

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Fundamental steps in digital image processing

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1.Image Acquisition:

(capturing an image in digital form)

Image Acquisition

Image Restoration

Morphological Processing

Segmentation

Representation &

Description

Image Enhancemen

t

Object Recognition

Problem Domain

Colour Image

Processing

Image Compression Hanan Hardan 24

2.Image Enhancement: making an image look better in a

subjective way.

Image Acquisition

Image Restoration

Morphological Processing

Segmentation

Representation &

Description

Image Enhancemen

t

Object Recognition

Problem Domain

Colour Image

Processing

Image Compression Hanan Hardan 25

3.Image Restoration:

improving the appearance of any image objectively.

Image Acquisition

Image Restoration

Morphological Processing

Segmentation

Representation &

Description

Image Enhancemen

t

Object Recognition

Problem Domain

Colour Image

Processing

Image Compression Hanan Hardan 26

4.Morphological Processing: extracting image components that are useful in the

representation and description of shape

Image Acquisition

Image Restoration

Morphological Processing

Segmentation

Representation &

Description

Image Enhancemen

t

Object Recognition

Problem Domain

Colour Image

Processing

Image Compression Hanan Hardan 27

5.Segmentation:

partitioning an image into its constituent parts or objects.

Image Acquisition

Image Restoration

Morphological Processing

Segmentation

Representation &

Description

Image Enhancemen

t

Object Recognition

Problem Domain

Colour Image

Processing

Image Compression Hanan Hardan 28

6.Object Recognition: assigning a label to an object based on

its descriptors

Image Acquisition

Image Restoration

Morphological Processing

Segmentation

Representation &

Description

Image Enhancemen

t

Object Recognition

Problem Domain

Colour Image

Processing

Image Compression Hanan Hardan 29

7.Representation & Description:

boundary representation vs. region representation.

Boundary descriptors vs. region descriptors

Image Acquisition

Image Restoration

Morphological Processing

Segmentation

Representation &

Description

Image Enhancemen

t

Object Recognition

Problem Domain

Colour Image

Processing

Image Compression Hanan Hardan 30

8.Image Compression:

reducing the stored and transmitted image data.

Image Acquisition

Image Restoration

Morphological Processing

Segmentation

Representation &

Description

Image Enhancemen

t

Object Recognition

Problem Domain

Colour Image

Processing

Image Compression Hanan Hardan 31

9.Colour Image Processing: color models and basic color processing

Image Acquisition

Image Restoration

Morphological Processing

Segmentation

Representation &

Description

Image Enhancemen

t

Object Recognition

Problem Domain

Colour Image

Processing

Image Compression Hanan Hardan 32