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Rakesh SoniM.E. (CSE)

Assistant Professor,

PIET

MOTIVATION FOR IP

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WHYSTUDY IP:

BECAUSE

IP has applications

In all walks of human life

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What is an image?

a representation, likeness, or imitation of an

object or thing a vivid or graphic description

something introduced to represent something

else

One Picture is worth more than ten

thousand words.

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An Image is defined as a two-dimensional function, f(x,y),

where x and y are spatial (plane) coordinates, and the amplitude

of f at any pair of coordinates (x,y) is called the intensity or

gray level of the image at that point.

When x, y and the intensity values of f are all finite, discretequantities, we call the image a digital image.

The elements are called picture elements, image elements, pels

and pixels.

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I. P. APPLICATIONS:

Health Care and Medical diagnostics.

Resources Surveying. Industrial Applications.

Security and Surveillance.

Water/ Irrigation project management. Military combat operations.

Environment and Pollution control.

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Case-I:

Medical Diagnosis

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A surgeon is viewing an X-ray plate of

patient suspected to behaving cancerous

growth in chest area. As it is soft-tissue

X-ray, contrast is inadequate to locate the

cancer accurately.

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The surgeon can take one of the two

decisions

1.To go ahead with the operation

2.Not to do the operation.

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If the surgeon decides on the

first choice and opens up the

body and finds that no cancerous cells at

all. The patient unnecessarily goes through

surgical reghours of medical operation.

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If the surgeon decides not to operate and if

there is cancerous cell growth, it will

rapidly spread in the entire body and

ultimately kill the patient, in a few weeks

time.

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Image enhancement techniques in IP

Can compliment the surgeon andassist him to take correctdecision,Know precisely the location

ofCancerous cell growth, thus confinethe operation to limited area.

SAVE THE PATIENT.

WHAT CAN IP DO?:

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Case-II:

Industrial Inspection

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LSI Devices manufacturing plant receives a

large quantity of raw materials, SiliconWafers, with some impurities, not possible

to detect using normal methods.

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Manufacturing proceeds and LSI devices are

produced in bulk. Entire batch gets rejectedas it fails to meet the specifications. All

foundry capacity, time, effort gets wasted.

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WHAT CAN IP DO?:

With proper IP tools, it is possible to

detect impurity levels exceeding limits atraw material stage itself.

I. P. thus saves wastages

And

Boosts productivity.

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CASE-III:

MILITARY COMBATS

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Modern military combats involve

Air Raids with aim of destroying militarybases and thus weaken the enemy.

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If AR operations are carried out

blindly, it will destroy civil amenities,hospitals, schools etc. Military bases

may remain unaffected.

Waste of AR effort and expenses.

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Killing innocent civilians raises

Hue & cry at UN bodies and creates a

world sympathy for the enemy.

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WHAT CAN IP DO?:

With IP one can get precise locations

of Military Bases and weapon storagelocations, through spying ventures.

Thus AR operations can be precisely

targeted to destroy the enemy fully.

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IMAGE PROCESSING

For Life saving, Efficiency and Efficacy .

A common thread in all the above cases is:

Even though only illustrative cases are given

above, IP plays vital roll for variety of applications

namely,R

esourcesS

urveying,

Security &

Surveillance, Water & Irrigation projects,

Astronomy & science search, Environmental &

Pollution control And many many other fields.

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WHAT is IP ?

It is an engineering science in which we

capture two dimensional pictureinformation and process it using digital

computing facilities.

The information is then compared with the

vast knowledge/data base on the subject,

for effective interpretation and correct

decision making.

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Engineering Science, Two-D Information,

Digital Computing Interpretation,Decision making and KNOWLEDGE-BASE

KEYWORDS in IP are:

Image Processing consists of:Image Acquisition.Image digitization & sampling.Image Processing.Image Interpretation.

Image Compression.

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Digital Image Processing Why ?

1. Improvement of pictorial information for human

interpretation

2. Processing of image data for storage ,

transmission and representation for autonomous

machine perception

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Digital Image Processing

Process digital images by means of computer, it covers

low-, mid-, and high-level processes

low-level: inputs and outputs are images

mid-level: outputs are attributes extracted

from input images

high-level: an ensemble of recognition ofindividual objects

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Elements of visual perception:

Field of

DIPis built on a foundation of mathematicaland probabilistic formulations.

Human intuition and analysis play central role in the

choice of one technique versus another.

Developing a basic understanding of human visual

perception as a first step.

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Structure of Human Eye:

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Fig. shows the density of rods and cones for a cross section of the right eye passing

through the region of emergence of the optic nerve from the eye.

The absence of receptors in this area results in the so-called blind spot.

Except for this region, the distribution of receptors is radially symmetric about fovea.

Receptor density is measured in degrees from the fovea.

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Image formation in the Eye:

15/100=h/17 orh=2.55 mm

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Image Sensing & Acquisition:

Image Acquisition

Using

Sensor Arrays:

Transformilluminationenergy into

digital images

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Image Acquisition Using Single Sensor:

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Image Acquisition Using

Sensor Strips:

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A Simple Image formation model:

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A Simple Image Formation Model

( , ) ( , ) ( , )

( , ) : intensity at the point ( , )

( , ) : illumination at the point ( , )

(the amount of source illumination incident on the scene)

( , ) : reflectance/transmissivity

f x y i x y r x y

f x y x y

i x y x y

r x y

! g

at the point ( , )

(the amount of illumination reflected/transmitted by the object)

where 0 < ( , ) < and 0 < ( , ) < 1

x y

i x y r x yg

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Some Typical Ranges of illumination

IlluminationLumen A unit of light flow or luminous flux

Lumen per square meter (lm/m2) The metric unit of measure forilluminance of a surface

On a clear day, the sun may produce in excess of 90,000 lm/m2 of illuminationon the surface of the Earth

O

n a cloudy day, the sun may produce less than 10,000 lm/m2

of illuminationon the surface of the Earth

On a clear evening, the moon yields about 0.1 lm/m2 of illumination

The typical illumination level in a commercial office is about 1000 lm/m2

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Some Typical Ranges of Reflectance

Reflectance

0.01 for black velvet

0.65 for stainless steel

0.80 for flat-white wall paint

0.90 for silver-plated metal

0.93 for snow

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Range of subjective sensations showing a particular adaptationlevel

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Basic Experimental setup used to characterize brightnessdiscrimination

Weber ratio as a function of intensity

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Perceived brightness is not a simple function of intensity. The relative verticalpositions betn two profiles in (b) have no special significance; they were chosenfor clarity

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All inner squares have same intensity, but they appear progressivelydarker as the background becomes lighter

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Optical illusions

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Frasers spiral

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Image Sampling and Quantization

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Effects of varying no. of samples in Digital Image

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(a) 1024*1024, 8-bit image. (b) 512*512 image resampled into1024*1024 pixels by row andcolumn duplication. (c) through (f) 256*256, 128*128, 64*64, an

32*32 images resampled into1024*1024 pixels.

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Image Zooming

steps: 1) the creation of new pixel locations,

2) the assignment of gray levels to those new

locations.

Methods:

1) Nearest neighbor interpolation2) Pixel replication:

3) Bilinear interpolation:

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Image Shrinking

Methods :

For integer factor row-column deletion

For noninteger factorzooming grid analogy

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Neighbors of a Pixel

A pixel p at coordinates (x, y) has fourhorizontaland verticalneighbors whose coordinates are given by

(x+1, y), (x-1, y), (x, y+1), (x, y-1)

This set of pixels, called the 4-neighbors of p, is denoted by N4(p).Each pixel is a unit distance from (x, y), and some of neighbors ofp lie outside the digital image if (x, y) is on the border of the image.

The fourdiagonalneighbors of p have coordinates

(x+1, y+1), (x+1, y-1), (x-1, y+1), (x-1, y-1)

and are denoted by ND(p).

These points, together with the 4-neighbors, are called the 8-eightneighbors of p, denoted by N8(p).

As before, some of the points in ND(p) and N8(p) fall outside theimage if (x, y) is on the border of the image.

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Neighbors of a Pixel

Connectivity

Adjacency

Regions

Boundaries

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Adjacency

Let Vbe the set of gray-level values

4-adjacency:Two pixels p and q with value from Vare4-adjacent if q is in the set N4(p).

8-adjacency:Two pixels p and q with value from Vare

8-adjacent if q is in the set N8(p).

m-adjacency: (mixed adjacency). Two pixels p and q with

values from Vare m-adjacentIf,

(i) q is in N4(p), or

(ii) q is in ND(p) andthe set N4(p) N4(q) has no pixels whosevalues are from V.

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(a) Arrangement of pixels; (b) pixels that are 8-adjacent (shown dashed)

to the center pixel; (c) m-adjacency.

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Distance Measures

For pixels p, q, and z, with coordinates (x, y), (s, t),

and (v, w), respectively, D is a distance function

ormetricif

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Euclidean distance

F

or this distance measure, the pixels having a distanceless than or equal to some

value rfrom (x, y) are the points contained in a disk of

radius r centered at (x, y).

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D4 distance (city-block distance)

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TheD8 distance (chessboard distance)