image processing rev02

8/8/2019 Image Processing Rev02

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IMAGE PROCESSING

Bhupendra P.Karandikar

Dept.of Instrumentation Science

University of Pune


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INTRODUCTION Image:An image is defined in real world as afunction of two variables, a(x,y)

Where, a = amplitude(brightness)

x,y= co-ordinate position

An image frequently contains collections ofsub-images called as regions-of-interest(ROIs).


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Image operations

Digital technology has made it possible tomanipulate multi-dimensional signals withcomputer systems that can be divided into:

Image Analysis

Image Transformation

Image understanding


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Image operations

Image analysis

Input : Image

Output : Measurement results

Process: Comparison in under test sample with standard

gauge or best fit circle.

Example: a online inspection Detection & measurement ofdiameter of a rubber O rings manufactured.

Advantages:

Non-contact measurements.

Human being not involved.


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Image operations

Image Transformation

Input : Image

Output : Image

Process: Image morphing comparing each pixel of the

image.

Example: Digital image mixing, animation tool.

Advantages:

No series of picture frames are required.

any type of image can be produced.


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Image operations

Image Understanding

Input : Image

Output : High level description

Process: Collecting & arranging the Image information

which can be compared with another image.

Example: Finger print matching, Human Face recognition

Optical character recognition.

Advantages:

Machine generated support to results by human being.


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Objective of the seminar

Featuring the mathematical operations

used for these three operations to the

image

Image

Grabbing

tool

Image

Processing

tool/s

Operated

Image/

image

information


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Digital images

Image representation:

f(x,y) with origin as the upper left corner.

Sampling :

- measuring value of image at discrete intervals in space.- sampling rate

- spatial resolution

Quantization:- Grey scale image

- Colour image, f(x,y) is a vector with three components R,G,B. This

can be represented in RGB Colour cube.


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Grey scale image

Human tendency to average brightness over

small areas, so that black dots and their white

background merge and perceived as grey

shade. Use of this in black & white printing technology

Half toning:

The process of generating a binary pattern of

black and white dots from an image.

Patterning Dithering


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Digital Half toning technique

Patterning:

- replacing each pixel by a binary font

- each grey level is assigned a value ofbinary font


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Dithering

thresholding the image against a dithermatrix.

Each pixel is compared with dither matrix

The pixel becomes white if value exceedsthe threshold or black otherwise.

0 128

D1=example for operations on 8 bit

192 64 images


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The Colour cube

RED

GREEN

CYAN

MAGENTA

BLUE

WHITE

BLACK

YELLOW1

0

1

1


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Simple image operations

I. Addition & averaging:

- addition of two images pixel by pixel

- two images of identical dimensions- Noise removal by averaging no.of images

- alpha blending, g(x,y) = f1(x,y)+(1- ) f2(x,y)

- =0.5 gives evenly weighted average


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Examples:


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Subtraction:- subtraction of two images

- change detection in two images

- g(x,y) = I f1(x,y) - f2(x,y)I

Division:- division of two images for meaningful results

- ratio image can be formed featuring illumination &

surface topography- remote sensing applications


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Adjustments of brightness & contrast:

- converting an image into interpretable form

- image brightness, g(x,y) = f(x,y) + b

- image contrast, g(x,y) = a f(x,y)

where, [f1,f2] are grey levels

g is the range(0-255).


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Simple image operationsLinear mapping

Map a particular range of grey levels [f1,f2] onto

a new range [g1,g2].

g(x,y) = g1 +(g2-g1 / f2-f1)[f(x,y)]


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Simple image operationsLinear mapping

f1 , here acts as a threshold up to and including

which grey levels are mapped as 0 and values

higher are mapped onto 255, hence this technique

is called as Thresholding.

This technique is used for image enhancement.


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Simple image operationsNon-linear mapping

Disadvantage of linear mapping: single valued

function is necessary.

Logarithmic mapping solves this problem, by

logarithmic mapping of input grey level onto outputgrey level.

Effective to enhance detail in the darker region of

image


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Simple image operationsNon-linear mapping

Example:


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Image histograms

The histogram of an image records the

frequency distribution of grey levels in that

image.

In bin 0, we record the number of times a greylevel of 0 occurs & similarly for other grey levels.

Useful indication of relative importance of

different grey levels in the image and judging the

requirements of contrast & brightness


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Histogram equalization

Redistributes grey levels in an attempt to flatten

the frequency distribution

More grey levels are allocated where there are

most pixels, fewer grey levels where there are

fewer pixels.


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Presentation for Seminar 3

Morphological Image processing

- Basic Concepts

- Fundamental operations- Compound operations

- Filtering


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Review of 2nd seminar

Digital images

Image operations

- Half toning, Dithering, addition,subtraction, division

- Linear and non-linear mapping

- Histogram and histogram equalization


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Histogram equalization


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Morphological operationsIntroduction

Describes range of non-linear image

processing techniques dealing with shape

of image

Removal of imperfections caused by

noise, texture or inaccurate thresholding to

binary images


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Morphological operationsFundamental binary operations

Basic concept:

typically to probe an image with a small

shape or template known as structuringelement.

The structuring element is positioned at

various positions in the image and compared

with the corresponding neighborhood pixels.


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Structuring elements

A matrix of known dimension and with pre-

defined elements.

Types of structuring elements:

- Square shaped

- Diamond shaped

- Cross shaped etc.

Advantage & disadvantages of structuring

elements- for erosion / dilation.


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Morphological operationsStructuring elements

Examples:


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Fitting & hitting:

Checking whether the element hits the

image or fits the image.

Hitting of an image:

Fitting of an image:


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Erosion:

Pixels are eroded from both the inner and outer

boundaries of regions, so the erosion will enlarge

the holes enclosed by a single region as well asmaking the gap between different regions larger.

This is fitting of image.

Erosion of an image f by a structuring element s

is given by,f s

g (x,y) = 1 if s fits f

= 0 otherwise.


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Morphological operationsErosion operations

Example of erosion:

Advantages:

Removal of unwanted, small scale features.

Disadvantage:

Side-effect on image as reduction in size of features


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Boundary detection:

Eroded image lacks boundary pixels, hence we

can subtract the eroded image from the original

image to get the boundary.

g = f - (f s)

Morphological operationsApplications of erosion


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Example: Boundary detection


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Morphological operationsDilation operation

Dilation:

the structuring element hits the boundary regionand source image is enlarged, known as hitting

to image.

Dilation of an image f by a structuring element s is given by,

f sg (x,y) = 1 if s hits f

= 0 otherwise.


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Morphological operationsDilation operation

Dilation operation will shrink the holes

enclosed by a single region and make the

gaps between different regions smaller.

To fill in any small intrusions into a regions

boundaries.

Opposite effect of erosion.


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Disadvantage:

Boundary finding is highly dependent on the ratio

of structuring element to image resolution.


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Morphological operationsApplication of Dilation

Enlarging or enhancing the regions of

interest (ROI)

Smoothening of the boundary regions

Limitations:

Exhibits unnecessary alteration to thesharp edges


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Morphological operationsDilation operations

Example:


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Morphological operationsCompound operations

Combinations of erosion, dilation & various

other operations

- Inversion- opening of an image

- Closing of an image


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Review of 3rd seminar

Structuring elements & its types

Erosion

Applications of erosion Dilation

Applications of dilation

Introduction of Opening & closing of image


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Morphological filtering

Filtering is removal of noise andunnecessary information from image.

Example: a biscuit inspection system.

Objective is to measure / monitor size &shape.

The noise is the image capturing elementnoise or the biscuit crumbs.


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Morphological filtering

Example:


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Segmentation

Partitioning of the image into distinct regions

Applications as Image analysis & interpretation- Industrial inspection

- tracking of objects

- detection and measurement of objects

Low level Image processing

High level Image processing

Segmentation


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Segmentation

Classification:

- Non-contextual: these techniques ignore

the relationships between the two featuresof an image

- Contextual: exploit the relationships

between the two features in an image


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SegmentationNon-contextual techniques

Regions are treated as independentimages

Thresholding: Classification of pixels in

two distinct categories. The strength of an edge-typically grey

level is detected by assigning value 0 if

the gradient falls below the threshold or anon-zero (usually 1) is assigned.


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Non-contextual techniquesThresholding

Classification of pixels into two categories.

0 f(x,y) < T

g(x,y) =

1 , f(x,y) T.

For the brighter feature.

This shows that this technique is featuredependent and cannot be used for automaticdetection.

Example, a robot camera to view its cards in hand.


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Example:



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Thresholding by a pair:

0 f(x,y) < T1g(x,y) = 1 T1 f(x,y) T2

0 f(x,y) > T2.



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Thresholding groups together according to

their global attribute, such as grey level.

This can be more successful as it takes into

account that pixels belonging to a single

object are close to one another.

It can be based on concept of similarity or

concept of discontinuity.

Contextual techniques


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This is a central concept of both edge and

region based approaches.

Types: 4-neighbourhood8-neighbourhood

Contextual techniquesPixel connectivity


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Convolution

image processing rev02

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