20051739 Iris Recongition (1)

8/11/2019 20051739 Iris Recongition (1)

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1ABSTRACT

2 A biometric system provides automatic recognition of an individual based on some

sort of unique feature or characteristic possessed by the individual. Biometric systemshave been developed based on fingerprints, facial features, voice, hand geometry,

handwriting, the retina and the one presented in this thesis, the iris.

3 The objective of the project is to implement an open source iris recognition system in

order to verify the claimed performance of the technology. The development tool used is

!AT"AB, and emphasis is on the software for performing recognition, and not hardware

for capturing an eye image.

#ris recognition analy$es the features that e%ist in the colored tissue

surrounding the pupil, which has 2&' points used for comparison, including rings,

furrows, and frec(les. #ris recognition uses a regular video camera system and can be

done from further away than a retinal scan. #t has the ability to create an accurate enough

measurement that can be used for #dentification purposes, not just verification.

The probability of finding two people with identical iris patterns is considered

to be appro%imately ) in )'&2*population of the earth is of the order )')' +. ot even one

egged twins or a future clone of a person will have the same iris patterns. The iris is

considered to be an internal organ because it is so well protected by the eyelid and thecornea from environmental damage. #t is stable over time even though the person ages.

#ris recognition is the most precise and fastest of the biometric authentication methods.

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CHAPTER - 1

BIOMETRIC TECHNOLOGY

1.1 INTRODUCTION: A biometric system provides automatic recognition of an individual

based on some sort of unique feature or characteristic possessed by the individual.

Biometric systems have been developed based on fingerprints, facial features, voice, hand

geometry, handwriting, the retina and the one presented in this thesis, the iris.

Biometric systems wor( by first capturing a sample of the feature, such

as recording a digital sound signal for voice recognition, or ta(ing a digital colour image

for face recognition. The sample is then transformed using some sort of mathematical

function into a biometric template. The biometric template will provide a normalised,

efficient and highly discriminating representation of the feature, which can then be

objectively compared with other templates in order to determine identity. !ost biometric

systems allow two modes of operation. An enrolment mode for adding templates to a

database, and an identification mode, where a template is created for an individual and

then a match is searched for in the database of pre enrolled templates.

A good biometric is characteri$ed by use of a feature that is0 highly

unique, stable and be easily captured and the chance of any two people having the samecharacteristic will be minimal. Also the feature does not change over time .#n order to

provide convenience to the user, and prevent misrepresentation of the feature.

1.2 ADVANTAGES OF USING BIOMETRICS:

/asier fraud detection

Better than password1 # or smart cards

o need to memori$e passwords -equires physical presence of the person to be identified

nique physical or behavioral characteristic annot be borrowed, stolen, or forgotten

annot leave it at home

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1.3 TYPES OF BIOMETRICS:

4 hysical Biometrics 5inger print -ecognition

5acial -ecognition

6and 7eometry #-#8 -ecognition

9 A

4Behavioral Biometrics 8pea(er -ecognition

8ignature

:eystro(e ;al(ing style

1.4 IRIS RECOGNITION SYSTEM:

The purpose of


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#n the chapter 2, we will discuss about the #-#8 -ecognition system, its

strengths and wea(ness. #n the chapter 3, we will discuss about the implementation of

#-#8 recognition system. #n chapter >, we will discuss about the unwrapping technology

.#n chapter &, about the wavelet analysis. #n chapter ?, about the software used for the

implementation.

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M!" o0 Co0!0 (""!#& Mi%i0!&"i i$("io&

#("!

S!$+#i"/ A )i$("io&%

I#i%

#!$o &i"io&

I#i% P(""!#& 1 1 266 666 Hi Hi -%!$+#i"/

($i)i"i!%

5ingerprinting 5ingerprints )1),''' !edium niversal6and shape 8i$e, length,

and thic(nessof hands

)1D'' "ow "ow security

facilities

5acial

recognition

utline, shapeanddistribution ofeyes and nose

)1)'' "ow "ow security

facilities

8ignature 8hape ofletters, writingorder, pen pressure

)1)'' "ow "ow security

facilities

@oice printing @oicecharacteristics

)13' "ow Telephone

service

TAB"/ 2.2 #-#8 -/ 7 #T# 8E8T/! A -A E

The comparison of various biometrics techniques is given in tables 2.)

and 2.2. The comparison shows that #-#8 recognition system is the most stable, precise

and the fastest biometric authentication method.

2.2 HUMAN IRIS:

The iris is a thin circular diaphragm, which lies between the cornea and

the lens of the human eye. A front on view of the iris is shown in 5igure ).). The iris is

perforated close to its centre by a circular aperture (nown as the pupil. The function of

the iris is to control the amount of light entering through the pupil, and this is done by the

sphincter and the dilator muscles, which adjust the si$e of the pupil. The average

diameter of the iris is )2 mm, and the pupil si$e can vary from )'F to G'F of the iris

diameter.

The iris consists of a number of layers0 the lowest is the epithelium layer,

which contains dense pigmentation cells. The stromal layer lies above the epithelium

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layer, and contains blood vessels, pigment cells and the two iris muscles. The density of

stromal pigmentation determines the color of the iris.

The e%ternally visible surface of the multi layered iris contains two $ones,

which often differ in color. An outer ciliary $one and an inner pupillary $one, and these

two $ones are divided by the collarette H which appears as a $ig$ag pattern

2.3 7OR8ING OF IRIS RECOGNITION SYSTEM:

#mage processing techniques can be employed to e%tract the unique iris

pattern from a digiti$ed image of the eye, and encode it into a biometric template, which

can be stored in a database. This biometric template contains an objective mathematical

representation of the unique information stored in the iris, and allows comparisons to be

made between templates. ;hen a subject wishes to be identified by iris recognitionsystem, their eye is first photographed, and then a template created for their iris region.

This template is then compared with the other templates stored in a database until either a

matching template is found and the subject is identified, or no match is found and the

subject remains unidentified.

5ig 2.) 6 !A /E/

2.4 FEATURES OF IRIS RECOGNITION:

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M!(%+#(,)! P /%i$() F!("+#!%: 2&' degrees of freedom,2&' non related unique

features of a person=s iris U&i*+!: /very iris is absolutely unique. o two iris are the same

S"(,)!: iris remains stable from )styear till death. A$$+#("!: #ris recognition is the most accurate of the commonly used biometric

technologies. F(%": #ris recognition ta(es less than 2 seconds. 2' times more matches per

minute than its closest competitor. No&-I&'(%i'!: o bright lights or lasers are used in the imaging and iris

authentication process.

2.5 IRIS RECOGNITION SYSTEM STRENGTHS:

Hi )/ P#o"!$"!0: #nternal organ of the eye. /%ternally visible0 patterns imaged

from a distance M!(%+#(,)! F!("+#!% o i#i% (""!#&: #ris patterns possess a high degree of

randomness

@ariabilityI 2>> degrees of freedom

/ntropyI 3.2 bits per square millimeter U&i*+!&!%%:8et by combinatorial comple%ity

S"(,)!: atterns apparently stable throughout life

9+i$ (&0 ($$+#("! I /ncoding and decision ma(ing are tractable

#mage analysis and encoding timeI ) second

2.; IRIS RECOGNITION SYSTEM 7EA8NESS:

8ome difficulty in usage as individuals doesn=t (now e%actly where they should position themselves.

And if the person to be identified is not cooperating by holding the head still and

loo(ing into the camera.

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CHAPTER - 3

IRIS SYSTEM IMPLEMENTATION

3.1 IMAGE AC9UISITION: #mage acquisition is considered the most critical step in the project

since all subsequent stages depend highly on the image quality. #n order to accomplish

this, we use a 9 camera. ;e set the resolution to ?>'%>G', the type of the image to

jpeg, and the mode to white and blac( for greater details. The camera is situated normally

between half a meter to one meter from the subject. *3 to )' inches+

The 9 cameras job is to ta(e the image from the optical system and

convert it into electronic data. 5ind the iris image by a monochrome 9 * harged

couple 9evice+ camera transfer the value of the different pi%els out of the 9 chip.

-ead out the voltages from the 9 chip. Thereafter the signals of each data are

amplified and sent to an A9 *Analog to 9igital onverter+.

5igs 3.) B" : 9#A7-A! 5 #!A7/ A C #8#T# 8# 7 9 A!/-A

3.2 IMAGE MANIPULATION:

#n the preprocessing stage, we transformed the images from -7B to gray

level and from eight bit to double precision thus facilitating the manipulation of the

images in subsequent steps.

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3.3 IRIS LOCALI then use the canny operator with the default threshold value given by !atlab,

to obtain the gradient image.

8ince the picture was acquired using an infrared camera the pupil is a

very distinct blac( circle. The pupil is in fact so blac( relative to everything else in the

picture simple edge detection should be able to find its outside edge very easily.

5urthermore, the thresholding on the edge detection can be set very high as to ignore

smaller less contrasting edges while still being able to retrieve the entire perimeter of the pupil. The best edge detection algorithm for outlining the pupil is canny edge detection.

This algorithm uses hori$ontal and vertical gradients in order to deduce edges in the

image. After running the canny edge detection on the image a circle is clearly present

along the pupil boundary.

anny /dge 9etector finds edges by loo(ing for the local ma%ima of the

gradient of the input image. #t calculates the gradient using the derivative of the 7aussian

filter. The anny method uses two thresholds to detect strong and wea( edges. #t includes

the wea( edges in the output only if they are connected to strong edges. As a result, the

method is more robust to noise, and more li(ely to detect true wea( edges.

3.4 EDGE DETECTION:

/dges often occur at points where there is a large variation in the luminance

values in an image, and consequently they often indicate the edges, or occluding

boundaries, of the objects in a scene. 6owever, large luminance changes can also

correspond to surface mar(ings on objects. oints of tangent discontinuity in the

luminance signal can also signal an object boundary in the scene.

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S"!1 :

#n order to implement the canny edge detector algorithm, a series of steps must

be followed. The first step is to filter out any noise in the original image before trying to

locate and detect any edges. And because the 7aussian filter can be computed using a

simple mas(, it is used e%clusively in the anny algorithm. nce a suitable mas( has

been calculated, the 7aussian smoothing can be performed using standard convolution

methods. The 7aussian mas( used is shown below.

S"! 2:

After smoothing the image and eliminating the noise, the ne%t step is to find the

edge strength by ta(ing the gradient of the image. The 8obel operator performs a 2 9

spatial gradient measurement on an image. Then, the appro%imate absolute gradient

magnitude *edge strength+ at each point can be found. The 8obel operator uses a pair of

3%3 convolution mas(s, one estimating the gradient in the % direction *columns+ and the

other estimating the gradient in the y direction *rows+.

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They are shown belowI

The magnitude, or /97/ 8T-/ 7T6, of the gradient is then

appro%imated using the formulaI

L7L M L7%L N L7yL

3.; HOUGH TRANSFORM:

The 6ough transform is a technique which can be used to isolate features of

a particular shape within an image. Because it requires that the desired features be

specified in some parametric form, the classical 6ough transform is most commonly used

for the detection of regular curves such as lines, circles, ellipses,etc. A generali$ed

6ough transform can be employed in applications where a simple analytic description ofa feature*s+ is not possible. 9ue to the computational comple%ity of the generali$ed

6ough algorithm, we restrict the main focus of this discussion to the classical 6ough

transform.

9espite its domain restrictions, the classical 6ough transform *hereafter

referred to without theclassical prefi%+ retains many applications0 as most manufactured

parts contain feature boundaries which can be described by regular curves. The main

advantage of the 6ough transform technique is that it is tolerant of gaps in feature

boundary descriptions and is relatively unaffected by image noise.

The 6ough transform is computed by ta(ing the gradient of the original

image and accumulating each non $ero point from the gradient image into every point

that is one radius distance away from it.

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That way, edge points that lie along the outline of a circle of the given radius

all contribute to the transform at the center of the circle, and so pea(s in the transformed

image correspond to the centers of circular features of the given si$e in the original

image. nce a pea( is detected and a circle OfoundO at a particular point, nearby points

*within one half of the original radius+ are e%cluded as possible circle centers to avoid

detecting the same circular feature repeatedly.

5ig 3.2I 6ough

circle detection

with gradientinformation.

ne

way of reducing

the computation required to perform the 6ough transform is to ma(e use of gradient

information which is often available as output from an edge detector. #n the case of the

6ough circle detector, the edge gradient tells us in which direction a circle must lie from

a given edge coordinate point.

The 6ough transform can be seen as an efficient implementation of a

generali$ed matched filter strategy. #n other words, if we created a template composed of

a circle of )Os *at a fi%ed + and 'Os everywhere else in the image, then we could convolv

it with the gradient image to yield an accumulator array li(e description of all the circles

of radius in the image. 8how formally that the basic 6ough transforms *i.e. the

algorithm with no use of gradient direction information+ is equivalent to templatematching.

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*3.3.)+ *3.3.2+ *3.3.3+ *3.3.>+

5ig 3.3 /97/ !A 8I

3.3.)+ an eye image 3.3.2+ corresponding edge map 3.3.3+ edge map with only hori$ontal

gradients 3.3.>+ edge map with only vertical gradients.

3.= NORMALISATION:

nce the iris region is successfully segmented from an eye image, the ne%t

stage is to transform the iris region so that it has fi%ed dimensions in order to allow

comparisons. The dimensional inconsistencies between eye images are mainly due to thestretching of the iris caused by pupil dilation from varying levels of illumination. ther

sources of inconsistency include, varying imaging distance, rotation of the camera, head

tilt, and rotation of the eye within the eye soc(et. The normalisation process will produce

iris regions, which have the same constant dimensions, so that two photographs of the

same iris under different conditions will have characteristic features at the same spatial

location. Another point of note is that the pupil region is not always concentric within the

iris region, and is usually slightly nasal. This must be ta(en into account if trying to

normali$e the


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UN7RAPPING

4.1 INTRODUCTION:

#mage processing of the iris region is computationally e%pensive. #n additionthe area of interest in the image is a OdonutO shape, and grabbing pi%els in this regio

requires repeated rectangular to polar conversions. To ma(e things easier, the iris region

is first unwrapped into a rectangular region using simple trigonometry. This allows the

iris decoding algorithm to address pi%els in simple *row, column+ format.

4.2 ASYMMETRY OF THE EYE:

Although the pupil and iris circles appear to be perfectly concentric, they rarely

are. #n fact, the pupil and iris regions each have their own bounding circle radius and

center location. This means that the unwrapped region between the pupil and iris

bounding does not map perfectly to a rectangle. This is easily ta(en care of with a little

trigonometry. There is also the matter of the pupil, which grows and contracts its area to

control the amount of light entering the eye. Between any two images of the same

personOs eye, the pupil will li(ely have a different radius. ;hen the pupil radius changes,

the iris stretches with it li(e a rubber sheet. "uc(ily, this stretching is almost linear, and

can be compensated bac( to a standard dimension before further processing.

4.3 THE UN7RAPPING ALGORITHM:

#n fig >.), points p and i are the detected centers of the pupil and iris

respectively. ;e e%tend a wedge of angle dP starting at an angle P, from both points p

and i, with radii -p and -i, respectively. The intersection points of these wedges with

the pupil and iris circles form a s(ewed wedge polygon ) 2 3 >. The s(ewed wedge is

subdivided radially into bloc(s and the image pi%el values in each bloc( are averaged

to form a pi%el *j,(+ in the unwrapped iris image, where j is the current angle number and

( is the current radius number.

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5ig >.)I Algorithm

for unwrapping the

iris region.

5or

this project, the

standard

dimensions of the

e%tracted iris

rectangle are )2G rows and G columns *see 5igure >.)+. This corresponds to M)2G

wedges, each of angle , with each wedge divided radially into G sections. The

equations below define the important points mar(ed in 5igure ). oints a through d are

interpolated along line segments ) 3 and 2 >.

4.4 CONTRAST AD>USTMENT:

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5ig >.2I #mage before ontrast Adjustment

Fig 4.3: Image after Contrast Adjustment

otice that figures >.2 and >.3 appear to be better contrasted than figure >.)

These images have been equali$ed in contrast to ma%imi$e the range of luminance values

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in the iris image. This ma(es it numerically easier to encode the iris data. The equali$ing

is done by acquiring a luminance histogram of the image and stretching the upper and

lower boundaries of the histogram to span the entire range of luminance values ' 2&&.

5igure >.) demonstrates an e%ample of this process.

4.5 FEATURE E?TRACTION:

Q ne of the most interesting aspects of the world is that it can be considered

to be made up of patterns. A pattern is essentially an arrangement. #t is characteri$ed by

the order of the elements of which it is made, rather than by the intrinsic nature of these

elementsR * obert ;iener+. This definition summari$es our purpose in this part. #n fact,

this step is responsible of e%tracting the patterns of the iris ta(ing into account the

correlation between adjacent pi%els. 5or this we use wavelets transform, and more

specifically the Q7abor 5ilteringR.

CHAPTER @ 5

7AVELET ANALYSIS

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5.1 GABOR FILTERING:

To understand the concept of 7abor filtering, we must first start with 7abor

wavelets. 7abor wavelets are formed from two components, a comple% sinusoidal carrier

and a 7aussian envelope.

g *%, y+ M s *%, y+ wr *%, y+

The comple% carrier ta(es the formI

s *%, y+ M e j*2S*u' %Nv' y+N +

;e can visuali$e the real and imaginary parts of this function separately as

shown in this figure. The real part of the function is given byI

-e *s *%, y++ M cos *2S *u' % N v' y+ N +

*&.&.)+ *&.&.2+

5ig &.) #!A7/ 8 - /

&.&.)+-eal art &.&.2+#maginary art

and the imaginaryI

#m *s *%, y++ M sin *2 *u'% N v'y+ N +

The parameters u' and v' represent the frequency of the hori$ontal and vertical

sinusoids respectively. represents an arbitrary phase shift. The second component of a

7abor wavelet is its envelope. The resulting wavelet is the product of the sinusoidal

carrier and this envelope. The envelope has a 7aussian profile and is described by the

following equationI

g *%, y+ M :eS*a2*%U%' +r 2Nb2*yUy' +r 2+

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;hereI *% U %' +r M *% U %' + cos *P+ N *y U y' + sin *P+

*y U y'+r M U*% U %' + sin *P+ N *y U y' + cos *P+

The parameters used above areI

: a scaling constant

*a, b+ /nvelope a%is scaling constants,

P envelope rotation constant,

*%' , y' + 7aussian envelope pea(.

5ig &.2 7A 88#A / @/" /

To put it all together, we multiply s *%, y+ by wr *%, y+. This produces a wavelet li(e this

oneI

5ig &.3 7AB - ;A@/"/T

5.2 GENERATING AN IRIS CODE:

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After performing unwrapping algorithm it maps the image to artesian coordinates so we

have something li(e the followingI

5ig &.> - ""/9 #-#8

;hat we want to do is somehow e%tract a set of unique features from this iris

and then store them. That way if we are presented with an un(nown iris, we can compare

the stored features to the features in the un(nown iris to see if they are the same. ;eOllcall this set of features an V#ris ode.V

Any given iris has a unique te%ture that is generated through a random process

before birth. 5ilters based on 7abor wavelets turn out to be very good at detecting

patterns in images. ;eOll use a fi%ed frequency )9 7abor filter to loo( for patterns in our

unrolled image.

5irst, weOll ta(e a one pi%el wide column from our unrolled image and

convolve it with a )9 7abor wavelet. Because the 7abor filter is comple%, the result willhave real and imaginary parts which are treated separately. ;e only want to store a small

number of bits for each iris code, so the real and imaginary parts are each quanti$ed. #f a

given value in the result vector is greater than $ero, a one is stored0 otherwise $ero is

stored. nce all the columns of the image have been filtered and quanti$ed, we can form

a new blac( and white image by putting all of the columns side by side. The real and

imaginary parts of this image *a matri%+, the iris code, are shown hereI



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*&.&.)+ *&.&.2+

5ig &.& #!A7/ 5 #-#8 9/&.&.)+ -/A" A-T &.&.2+ #!A7# A-E A-T

5.3 TEST OF STATISTICAL INDEPENDANCE:

This test enables the comparison of two iris patterns. This test is based on

the idea that the greater the 6amming distance between two feature vectors, the greater

the difference between them. Two similar irises will fail this test since the distance

between them will be small. #n fact, any two different irises are statistically QguaranteedRto pass this test as already proven. The 6amming distance *69+ between two Boolean

vectors is defined as followsI

;here, A and B are the coefficients of two iris images and is the si$e of the feature

vector *in our case M D'2+. The is the (nown Boolean operator that gives a binary )

if the bits at position j in A and B are different and ' if they are similar.

5ig &.& 6A!!# 7 9#8TA /8 A" "AT# I



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CHAPTER - ;

MATLAB

;.1 INTRODUCTION: !AT"AB W is a high performance language for technical computing. #t

integrates computation, visuali$ation, and programming in an easy to use environment

where problems and solutions are e%pressed in familiar mathematical notation. Typical

uses include

!ath and computation Algorithm development 9ata acquisition !odeling, simulation, and prototyping 9ata analysis, e%ploration, and visuali$ation 8cientific and engineering graphics Application development, including graphical user interface

building.

!AT"AB is an interactive system whose basic data element is an

array that does not require dimensioning. This allows you to solve many technical

computing problems, especially those with matri% and vector formulations, in a

fraction of the time it would ta(e to write a program in a scalar non interactive

language such as or 5 -T-A .

The name !AT"AB stands for matrix laboratory . !AT"AB was

originally written to provide easy access to matri% software developed by the

"# A : and /#8A : projects. Today, !AT"AB engines incorporate the"A A : and B"A8 libraries, embedding the state of the art in software for

matri% computation.

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!AT"AB features a family of add on application specific solutions called

toolbo%es. @ery important to most users of !AT"AB, toolbo%es allow you to and apply

speciali$ed technology. Toolbo%es are comprehensive collections of !AT"AB functions

*! files+ that e%tend the !AT"AB environment to solve particular classes of problems.

Areas in which toolbo%es are available include signal processing, control systems, neural

networ(s, fu$$y logic, wavelets, simulation, and many others.

;.2 THE MATLAB SYSTEM:

The !AT"AB system consists of five main partsI

( DEVELOPMENT ENVIRONMENT :

This is the set of tools and facilities that help you use !AT"AB functions

and files. !any of these tools are graphical user interfaces. #t includes the !AT"AB

des(top and ommand ;indow, a command history, an editor and debugger, and

browsers for viewing help, the wor(space, files, and the search path.

, THE MATLAB MATHEMATICAL FUNCTION LIBRARY : This is a vast collection of computational algorithms ranging from elementary

functions, li(e sum, sine, cosine, and comple% arithmetic, to more sophisticated functionsli(e matri% inverse, matri% /igen values, Bessel functions, and fast 5ourier transforms.

$ THE MATLAB LANGUAGE:

This is a high level matri%1array language with control flow statements,

functions, data structures, input1output, and object oriented programming features. #t

allows both Vprogramming in the smallV to rapidly create quic( and dirty throw away

programs, and Vprogramming in the largeV to create large and comple% application programs.

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0 GRAPHICS:

!AT"AB has e%tensive facilities for displaying vectors and matrices as

graphs, as well as annotating and printing these graphs. #t includes high level functions

for two dimensional and three dimensional data visuali$ation, image processing,

animation, and presentation graphics. #t also includes low level functions that allow you

to fully customi$e the appearance of graphics as well as to build complete graphical user

interfaces on your !AT"AB applications.

! THE MATLAB APPLICATION PROGRAM INTERFACE:

This is a library that allows you to write and 5 -T-A programs that

interact with !AT"AB. #t includes facilities for calling routines from !AT"AB

*dynamic lin(ing+, calling !AT"AB as a computational engine, and for reading andwriting !AT files.

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SOURCE CODE

function XB9, B;TY Mbandtrace *B;+B9 M XY0

B;T M B;0

8T M fnd st *B;T+0

dir M X ), )Y0

oint M 8T0

B9 M XB908TY0

e%t M oint N dir0

while * e%t *)+4 e%t *2+MM'+ L * e%t *)+ZMsi$e*B;T,)++ L

* e%t *2+ZMsi$e*B;T,2++

dir M dir ne%t*dir,'+0

e%t M oint N dir0

end

B;T* oint*)+, oint*2++ M '0

totle M sum*sum*B;T++0

i M )0j M '0while *i[Mtotle+ \ ** e%t *)+]M8T*)++ L * e%t *2+]M8T*2+++

if B;T* e%t *)+, e%t *2++ MM '

if j ZM G

e%t M oint0

brea(0

end

j M j N )0 dir M dir ne%t*dir,'+0

e%t M oint N dir0

else

j M '0

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B9 M XB90 e%t Y0

B;T* e%t *)+, e%t *2++ M '0

oint M e%t 0

dir M dir ne%t*dir,)+0

e%t M oint N dir0

i M i N )0

end

while * e%t *)+4 e%t *2+MM'+ L * e%t *)+ZMsi$e*B;T,)++ L

* e%t *2+ZMsi$e*B;T,2++

dir M dir ne%t*dir,'+0

e%t M oint N dir0

endend

function XCYMcir quad*#,%c,yc,rc,t+

#t M #0

for r M ) I si$e*#,)+

for c M ) I si$e*#,2+

9ist*r,c+ M sqrt**r yc+^2N*c %c+^2+0

if abs*r yc+ Z abs*c %c+

#t*r,c+ M '0

end

end

end

C M sum*#t*find**9ist[MrcNt12+\*9istZMrc t12++++0

function varargout M ircl9etect*varargin+

F #- "9/T/ T ! file for ircl9etect.fig

gui 8ingleton M )0

gui 8tate M struct*Ogui ameO, mfilename, ...

Ogui 8ingletonO, gui 8ingleton, ...

Ogui pening5cnO, _ ircl9etect pening5cn, ...

T-- ollege of /ngineering 2G


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Ogui utput5cnO, _ ircl9etect utput5cn, ...

Ogui "ayout5cnO, XY , ...

Ogui allbac(O, XY+0

if nargin \\ ischar*varargin`) +

gui 8tate.gui allbac( M str2func*varargin`) +0

end

F /%ecutes just before ircl9etect is made visible.

function ircl9etect pening5cn*h bject, eventdata, handles, varargin+

F This function has no output args, see utput5cn.

F h bject handle to figure

F eventdata reserved to be defined in a future version of !AT"AB

F handles structure with handles and user data *see 7 #9ATA+F varargin command line arguments to ircl9etect *see @A-A-7# +

F hoose default command line output for ircl9etect

#mage M repmat*logical*uintG*'++,2'',2''+0


F handles empty handles not created until after all reate5cns called

F 6intI edit controls usually have a white bac(ground on ;indows.

F 8ee #8 and ! T/-.

if ispc

set*h bject,OBac(ground olorO,OwhiteO+0

else

set*h bject,OBac(ground olorO,get*',Odefault icontrolBac(ground olorO++0

end

F /%ecutes on slider movement.

function -ad8et allbac(*h bject, eventdata, handles+

F h bject handle to -ad8et *see 7 B +


F handles structure with handles and user data *see 7 #9ATA+

F 6intsI get*h bject,O@alueO+ returns position of slider

F get*h bject,O!inO+ and get*h bject,O!a%O+ to determine range of slider

T-- ollege of /ngineering 2J


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31/60

F 6intsI get*h bject,O8tringO+ returns contents of -ad9et as te%t

F str2double*get*h bject,O8tringO++ returns contents of -ad9et as a double

F /%ecutes during object creation, after setting all properties.

function -ad9et reate5cn*h bject, eventdata, handles+

F h bject handle to -ad9et *see 7 B +




F 8ee #8 and ! T/-.

if ispc


else set*h bject,OBac(ground olorO,get*',Odefault icontrolBac(ground olorO++0

end

F /%ecutes on button press in pb8et.

function pb8et allbac(*h bject, eventdata, handles+

F h bject handle to pb8et *see 7 B +



handles.set M str2double*get*handles. ent ,O8tringO++0

handles.setE M str2double*get*handles. entE,O8tringO++0

handles.set- M get*handles.-ad8et,O@alueO+0

handles.#mage M handles.#mage4'0

output coord M plot circle*handles.set ,handles.setE,handles.set-+0

for iM)Isi$e*output coord,)+

handles.#mage*round*output coord*i,2++,round*output coord*i,)+++ M )0

end

a%es*handles.a%es)+0

imshow*handles.#mage+0

guidata*h bject, handles+0

function X e%tdirYMdir ne%t*dir,step+

T-- ollege of /ngineering 3)


32/60


33/60

h M waitbar*',O9istance omputationO+0

switch nargin

case )

Xm),n)YMsi$e*dataset)+0

m2Mm)0

9M$eros*m),m2+0

for iM)Im)

waitbar*i1m)+

for jM)Im2

if iMMj

9*i,j+M a 0

else 9*i,j+Msqrt**dataset)*i,)+ dataset)*j,)++^2N*dataset)*i,2+

dataset)*j,2++^2+0

end

end

end

case 2

Xm),n)YMsi$e*dataset)+0

Xm2,n2YMsi$e*dataset2+0

9M$eros*m),m2+0

for iM)Im)

waitbar*i1m)+

for jM)Im2

9*i,j+Msqrt**dataset)*i,)+ dataset2*j,)++^2N*dataset)*i,2+

dataset2*j,2++^2+0

end

end

otherwise

error*Oonly one or two input argumentsO+

end



34/60

close*h+

function X8TY M fnd st *B;+

s$ M si$e*B;+0

r M '0c M '0

sign M '0

for i M ) I s$*)+

for j M ) I s$*2+

if B;*i,j+ MM )

r M i0c M j0

sign M )0

brea(0

end end

if sign MM ),brea(,end

end

if *rMM'+ L *cMM'+

error*OThere is no white point in VB;V.O+0

else

8T M Xr,cY0

/nd

function X ,6!YM6oughcircle*B;,-p+

F6 76 #- "/ detects circles in a binary image.

# M B;0

Xsy,s%YMsi$e*#+0

Xy,%YMfind*#+0

totalpi% M length*%+0

6! tmp M $eros*sy4s%,)+0

b M )Isy0

a M $eros*sy,totalpi%+0

if nargin MM )

- min M )0

T-- ollege of /ngineering 3>


35/60

- ma% M ma%*ma%*%+,ma%*y++0

else

- min M -p*)+0

- ma% M -p*2+0

end

y M repmat*yO,Xsy,)Y+0

% M repmat*%O,Xsy,)Y+0

6 M '0

for - M - min I - ma%

-2 M -^20

b) M repmat*bO,X),totalpi%Y+0

b2 M b)0 a) M *round*% sqrt*-2 *y b)+.^2+++0

a2 M *round*% N sqrt*-2 *y b2+.^2+++0

b) M b)*imag*a)+MM' \ a)Z' \ a)[s%+0

a) M a)*imag*a)+MM' \ a)Z' \ a)[s%+0

b2 M b2*imag*a2+MM' \ a2Z' \ a2[s%+0

a2 M a2*imag*a2+MM' \ a2Z' \ a2[s%+0

ind) M sub2ind*Xsy,s%Y,b),a)+0

ind2 M sub2ind*Xsy,s%Y,b2,a2+0

ind M Xind)0 ind2Y0

val M ones*length*ind+,)+0

dataMaccumarray*ind,val+0

6! tmp*)Ilength*data++ M data0

6!2 tmp M reshape*6! tmp,Xsy,s%Y+0

Fimshow*6!2,XY+0

ma%val M ma%*ma%*6!2 tmp++0

if ma%valZ6

6 M ma%val0

6! M 6!2 tmp0

-c M -0

T-- ollege of /ngineering 3&


36/60

end

end

XB,AY M find*6!MM6 +0

M Xmean*A+,mean*B+,-cY0

function varargout M hough7 #*varargin+

F 6 767 # ! file for hough7 #.fig

F 6 767 #, by itself, creates a new 6 767 # or raises the e%isting

F singleton4.

F Begin initiali$ation code 9 T /9#T

gui 8ingleton M )0


Ogui 8ingletonO, gui 8ingleton, ... Ogui pening5cnO, _hough7 # pening5cn, ...

Ogui utput5cnO, _hough7 # utput5cn, ...


Ogui allbac(O, XY+0



end

if nargout

Xvarargout`)Inargout Y M gui mainfcn*gui 8tate, varargin`I +0

else

gui mainfcn*gui 8tate, varargin`I +0

end

F /nd initiali$ation code 9 T /9#T

F /%ecutes just before hough7 # is made visible.

function hough7 # pening5cn*h bject, eventdata, handles, varargin+

F This function has no output args, see utput5cn.




T-- ollege of /ngineering 3?


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F varargin command line arguments to hough7 # *see @A-A-7# +

F hoose default command line output for hough7 #

handles.output M h bject0

8 M repmat*logical*uintG*'++,>'',>''+0

output coord M plot circle*2'',2'',D&+0

for iM)Isi$e*output coord,)+

8*round*output coord*i,2++,round*output coord*i,)+++ M )0

end


imshow*8+0

handles.8 M 80

F pdate handles structureguidata*h bject, handles+0

F #;A#T ma(es hough7 # wait for user response *see #-/8 !/+

F uiwait*handles.figure)+0

F utputs from this function are returned to the command line.

function varargout M hough7 # utput5cn*h bject, eventdata, handles+

F varargout cell array for returning output args *see @A-A-7 T+0




F 7et default command line output from handles structure

varargout`) M handles.output0

F /%ecutes on button press in pb"oad.

function pb"oad allbac(*h bject, eventdata, handles+

F h bject handle to pb"oad *see 7 B +



Xfilename, pathnameY M uigetfile*`O4.bmpO0O4.jpgO0O4.tifO +0

8 M imread*Xpathname filenameY+0

handles.8 M 80

T-- ollege of /ngineering 3D


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imshow*8+0



F /%ecutes on button press in pb9etect.

function pb9etect allbac(*h bject, eventdata, handles+

F h bject handle to pb9etect *see 7 B +



- M get*handles.sld-,O@alueO+0

X ,6!Y M 6oughcircle*handles.8,X-,-Y+0

Xma%val,ma%indY M ma%*ma%*6!++0a%es*handles.a%es2+0

imshow*6!+0

a%es*handles.a%es3+0

imshow*handles.8+0hold on0

output coord M plot circle* *)+, *2+, *3++0

plot*output coord*I,)+,output coord*I,2++0

plot* *)+, *)+,Or4O+0hold off0

set*handles.t%t ,OstringO, *)++0

set*handles.t%tE,OstringO, *2++0

set*handles.t%t@al,OstringO,ma%val+0

F /%ecutes on slider movement.

function sld- allbac(*h bject, eventdata, handles+

F h bject handle to sld- *see 7 B +



F 6intsI get*h bject,O@alueO+ returns position of slider

F get*h bject,O!inO+ and get*h bject,O!a%O+ to determine range of slider

set*handles.t%t-,OstringO,get*h bject,O@alueO++0


T-- ollege of /ngineering 3G


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function sld- reate5cn*h bject, eventdata, handles+

function t%t@al allbac(*h bject, eventdata, handles+

F h bject handle to t%t@al *see 7 B +



F 6intsI get*h bject,O8tringO+ returns contents of t%t@al as te%t

F str2double*get*h bject,O8tringO++ returns contents of t%t@al as a double


function t%t@al reate5cn*h bject, eventdata, handles+

F h bject handle to t%t@al *see 7 B +


F handles empty handles not created until after all reate5cns calledF 6intI edit controls usually have a white bac(ground on ;indows.

F 8ee #8 and ! T/-.

if ispc \\ isequal*get*h bject,OBac(ground olorO+,

get*',Odefault icontrolBac(ground olorO++


end

F /%ecutes on button press in pb-eturn.

function pb-eturn allbac(*h bject, eventdata, handles+

F h bject handle to pb-eturn *see 7 B +



F lose urrent figure window.

close*gcf+0

function XB;YMim2bw t*#,t+

if nargin MM )

t M t autoset*#+0

end

s$Msi$e*#+0

B;Mrepmat*logical*uintG*'++,s$*)+,s$*2++0

T-- ollege of /ngineering 3J


40/60

for r M 3 I s$*)+ 2

for c M 3 I s$*2+ 2

if #*r,c+[t

if *#*r ),c )+[t+\*#*r ),c+[t+\*#*r ),cN)+[t+\*#*r,c

)+[t+\*#*r,cN)+[t+\*#*rN),c )+[t+\*#*rN),c+[t+\*#*rN),cN)+[t+

B;*r,c+ M '0

else

B;*r,c+ M )0

end

else

B;*r,c+ M '0

end end

end

for r M ) I s$*)+

B;*r,)I2+ M '0

B;*r,*si$e*B;,2+ )+Isi$e*B;,2++ M '0

end

for c M ) I s$*2+

B;*)I2,c+ M '0

B;**si$e*B;,)+ )+Isi$e*B;,)+,c+ M '0

end

function varargout M #ris"ocat*varargin+

F #-#8" AT ! file for #ris"ocat.fig

F

gui 8ingleton M )0


Ogui 8ingletonO, gui 8ingleton, ...

Ogui pening5cnO, _#ris"ocat pening5cn, ...

Ogui utput5cnO, _#ris"ocat utput5cn, ...


T-- ollege of /ngineering >'


41/60

Ogui allbac(O, XY+0



end

if nargout

Xvarargout`)Inargout Y M gui mainfcn*gui 8tate, varargin`I +0

else

gui mainfcn*gui 8tate, varargin`I +0

end

F /%ecutes just before #ris"ocat is made visible.

function #ris"ocat pening5cn*h bject, eventdata, handles, varargin+

guidata*h bject, handles+0F #;A#T ma(es #ris"ocat wait for user response *see #-/8 !/+

F uiwait*handles.figure)+0

F utputs from this function are returned to the command line.

function varargout M #ris"ocat utput5cn*h bject, eventdata, handles+

F varargout cell array for returning output args *see @A-A-7 T+0




F 7et default command line output from handles structure

varargout`) M handles.output0

function edit3 allbac(*h bject, eventdata, handles+

F h bject handle to edit3 *see 7 B +



F 6intsI get*h bject,O8tringO+ returns contents of edit3 as te%t

F str2double*get*h bject,O8tringO++ returns contents of edit3 as a double


function edit3 reate5cn*h bject, eventdata, handles+

F h bject handle to edit3 *see 7 B +

T-- ollege of /ngineering >)


42/60


43/60

F 6intsI get*h bject,O8tringO+ returns contents of edit? as te%t

F str2double*get*h bject,O8tringO++ returns contents of edit? as a double


function edit? reate5cn*h bject, eventdata, handles+

F h bject handle to edit? *see 7 B +




F 8ee #8 and ! T/-.

if ispc


else set*h bject,OBac(ground olorO,get*',Odefault icontrolBac(ground olorO++0

end

function edit& allbac(*h bject, eventdata, handles+

F h bject handle to edit& *see 7 B +



function edit& reate5cn*h bject, eventdata, handles+

F h bject handle to edit& *see 7 B +




F 8ee #8 and ! T/-.

if ispc


else


end

function edit> allbac(*h bject, eventdata, handles+

F h bject handle to edit> *see 7 B +

T-- ollege of /ngineering >3


44/60



F 6intsI get*h bject,O8tringO+ returns contents of edit> as te%t

F str2double*get*h bject,O8tringO++ returns contents of edit> as a double


function edit> reate5cn*h bject, eventdata, handles+

F h bject handle to edit> *see 7 B +




F 8ee #8 and ! T/-.

if ispc set*h bject,OBac(ground olorO,OwhiteO+0

else


end

F /%ecutes on button press in pushbutton).

function pushbutton) allbac(*h bject, eventdata, handles+

F h bject handle to pushbutton) *see 7 B +



Xfilename, pathnameY M uigetfile*`O4.bmpO0O4.jpgO0O4.tifO +0

8 M imread*Xpathname filenameY+0

if isrgb*8+

8 M rgb2gray*8+0

end

F8Mresi$e u2*8,'.G,'.G+0

handles.8 M 80


imshow*8+0


T-- ollege of /ngineering >>


45/60


F /%ecutes on button press in pushbutton2.

function pushbutton2 allbac(*h bject, eventdata, handles+

F h bject handle to pushbutton2 *see 7 B +



B; M im2bw t*handles.8+0

B; M bandtrace*B;+0

tic0

M 6oughcircle*B;+0

t) M toc0

set*handles.te%t T),OstringO,t)+0handles. M 0

set*handles.edit),OstringO, *)++0

set*handles.edit2,OstringO, *2++0

set*handles.edit3,OstringO, *3++0


imshow*handles.8+0 hold on0

plot* *)+, *2+,O%rO+0

output coord M plot circle* *)+, *2+, *3++0

plot*output coord*I,)+,output coord*I,2++0

hold off0

M handles.

B; M ]*im2bw*handles.8++0

enter M X *)+0 *2+Y0

for i M & I &

for j M & I &

ent M X *)+Ni0 *2+NjY0

enter M X enter, entY0

end

end

T-- ollege of /ngineering >&


46/60

tic0

) M /locate*B;, enter, *3++0

t2 M toc0

set*handles.te%t T2,OstringO,t2+0

handles. ) M )0

set*handles.edit>,OstringO, )*)++0

set*handles.edit&,OstringO, )*2++0

set*handles.edit?,OstringO, )*3++0


imshow*handles.8+0 hold on0

plot* )*)+, )*2+,O4gO+0

output coord M plot circle* )*)+, )*2+, )*3++0 plot*output coord*I,)+,output coord*I,2++0

hold off0



F /%ecutes on button press in pushbutton>.

function pushbutton> allbac(*h bject, eventdata, handles+

F h bject handle to pushbutton> *see 7 B +



fig M hough7 #0

fig2 handles M guihandles*fig+0

handles.figure2 M fig2 handles0



F /%ecutes on button press in pushbutton?.

function pushbutton? allbac(*h bject, eventdata, handles+

F h bject handle to pushbutton? *see 7 B +



T-- ollege of /ngineering >?


47/60

a%es*handles.a%es)+0imshow*X2&&Y+0

a%es*handles.a%es2+0imshow*X2&&Y+0

a%es*handles.a%es3+0imshow*X2&&Y+0

set*handles.edit),OstringO,OO+0

set*handles.edit2,OstringO,OO+0

set*handles.edit3,OstringO,OO+0

set*handles.edit>,OstringO,OO+0

set*handles.edit&,OstringO,OO+0

set*handles.edit?,OstringO,OO+0

set*handles.te%t T),OstringO,OO+0

set*handles.te%t T2,OstringO,OO+0

if isfield*handles,O8O+ handles.8 M XY0

end

if isfield*handles,O O+

handles. M XY0

end

if isfield*handles,O )O+

handles. ) M XY0

end



F /%ecutes on button press in pushbuttonD.

function pushbuttonD allbac(*h bject, eventdata, handles+

F h bject handle to pushbuttonD *see 7 B +



if isfield*handles,Ofigure2O+

if ishandle*handles.figure2.figure)+

close*handles.figure2.figure)+0

T-- ollege of /ngineering >D


48/60

end

end

if isfield*handles,Ofigure3O+

if ishandle*handles.figure3.figure)+

close*handles.figure3.figure)+0

end

end

close*gcf+0

F /%ecutes on button press in pushbutton&.

function pushbutton& allbac(*h bject, eventdata, handles+

F h bject handle to pushbutton& *see 7 B +

F eventdata reserved to be defined in a future version of !AT"ABF handles structure with handles and user data *see 7 #9ATA+

fig M ircl9etect0

fig3 handles M guihandles*fig+0

handles.figure3 M fig3 handles0



function edit) allbac(*h bject, eventdata, handles+

F h bject handle to edit) *see 7 B +



F 6intsI get*h bject,O8tringO+ returns contents of edit) as te%t

F str2double*get*h bject,O8tringO++ returns contents of edit) as a double

F /%ecutes on button press in pushbutton)).

function pushbutton)) allbac(*h bject, eventdata, handles+

F h bject handle to pushbutton)) *see 7 B +



recogni$e

F /%ecutes on button press in pushbutton)2.

T-- ollege of /ngineering >G


49/60

function pushbutton)2 allbac(*h bject, eventdata, handles+

F h bject handle to pushbutton)2 *see 7 B +



B; M im2bw t*handles.8+0

B; M bandtrace*B;+0

tic0

M 6oughcircle*B;+0

8T M fnd st *B;+0

prompt M `O/nter file nameIO 0

dlg title M O#nput for -adii e%portO0

num lines M )0def M `O9avidO 0

answer M inputdlg*prompt,dlg title,num lines,def+0

saveradii*answer`) , ,8T+0


function a%es3 reate5cn*h bject, eventdata, handles+

F h bject handle to a%es3 *see 7 B +



F 6intI place code in pening5cn to populate a%es3

function XB9im,B9YM bandtrace*B;+

B9 M XY0

B;T tm M B;0

B9imMrepmat*logical*uintG*'++,si$e*B;,)+,si$e*B;,2++0

while sum*sum*B;T tm++ ]M '

XB9 tm,B;T tmY M bandtrace*B;T tm+0

if length*B9+ [ length*B9 tm+

B9 M B9 tm0

end

T-- ollege of /ngineering >J


50/60

end

for i M ) I length*B9+

B9im*B9*i,)+,B9*i,2++ M )0

/nd

function X Y M /locate*B;, enter,-+

M XY0

C M '0

totle M sum*sum*B;++0

delta r M X)Imin*si$e*B;++Y0

delta r M min*si$e*B;++.1delta rNdelta r0

delta r M find*delta r MM min*delta r++0

try -min M -*)+0

catch

-min M )0

end

try

-ma% M -*2+0

catch

-ma% M si$e*B;,)+0

end

for i M ) I si$e* enter,2+

%c M enter*),i+0

yc M enter*2,i+0

r M -minNdelta r120

while *ycNr delta r [M si$e*B;,)++ \ *%cNr delta r [M si$e*B;,2++ FL )

Ct M cir quad*B;,%c,yc,r,delta r+0

if C [ Ct

C M Ct0

M X%c,yc,rY0

T-- ollege of /ngineering &'


51/60

if C ZM '.&4totle

brea(0

end

end

r M r N delta r0

end

end

C M '0

for r M *3+ delta r12 I *3+Ndelta r12

Ct M cir quad*B;, *)+, *2+,r,)+0

if C [ Ct

C M Ct0 *3+ M r0

Fbrea(0

end

end

F

F #nputs I

F The coordinates of the center of this circle

F E The E coordinates of the center of this circle

F -adius The radius of this circle ZM '

F intervals *optional+ The number of evenly spaced points along the a%is

F inclusive of the start and end coordinates

F *needed by OintervalO algorithm+

function output coord M plot circle* ,E,-adius,varargin+

output M XY0

outputE M XY0

temp mod M )0

cal mode M OangleO0 Fdefault

intervals M a 0

T-- ollege of /ngineering &)


52/60

F

MMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMM

F hec( optional inputs

F


if *length*varargin+ Z '+

for a M )I)Ilength*varargin+

if isstr*vararginà + MM )

if **strcmp*vararginà ,OangleO+ MM )+L...

*strcmp*vararginà ,OintervalO+ MM )+L...

*strcmp*vararginà ,ObresenhamO+ MM )+L... *strcmp*vararginà ,Obresenham8olidO+ MM )+...

+

cal mode M vararginà 0

end

elseif isfinite*vararginà + MM )

intervals M vararginà 0

else

error*O#nvalid argumentsO+0

end

end

end

F


F #nitiali$ation of OintervalO

F


if strcmp*cal mode,OintervalO+ MM )

T-- ollege of /ngineering &2


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F 8atisfies requirement for calculation by the given intervals

if isnan*intervals+ MM )

error*O o interval givenO+0

end

temp mod M mod*intervals,2+0

interval M *intervals temp mod+ 1 20

interval si$e M *-adiusN-adius+1intervals0

value M '0

if temp mod MM )

output M X'Y0

outputE M X-adiusY0

else value M * )12+4interval si$e0

end

cal mode M OintervalO0

end


F alculate the )st quardrant

F


F 444444444444444444444444444444444444444444444444444444444444

F by the angle, scaled by the given radius

F 444444444444444444444444444444444444444444444444444444444444

if strcmp*cal mode,OangleO+ MM )

increment angle M pi4*)1-adius+0

curr angle M pi120

loopM'0

while curr angleZ'

loopMloopN)0

output M Xoutput *I+0sqrt*-adius^2 *-adius4sin*curr angle++^2+Y0

outputE M XoutputE*I+0sqrt*-adius^2 *-adius4cos*curr angle++^2+Y0

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curr angle M curr angle increment angle0

end

output M Xoutput *I+0-adiusY0

outputE M XoutputE*I+0'Y0

elseif strcmp*cal mode,OintervalO+ MM )

F 444444444444444444444444444444444444444444444444444444444444

F by the given % interval

F 444444444444444444444444444444444444444444444444444444444444

value M value N interval si$e0

while value [ -adius

output M Xoutput *I+0value Y0

outputE M XoutputE*I+0sqrt**-adius4-adius+ *value 4value ++Y0 value M value N interval si$e0

end

output M Xoutput *I+0-adiusY0

outputE M XoutputE*I+0'Y0

elseif *strcmp*cal mode,ObresenhamO+ MM )+ L *strcmp*cal mode,Obresenham8olid

)+

M round* +0

E M round*E+0

-adius M round*-adius+0

F 444444444444444444444444444444444444444444444444444444444444

% i M '0

y i M -adius0

theta iM24*) -adius+0

"imit M '0

while y i ZM "imit

F 8et i%el

if *strcmp*cal mode,ObresenhamO+ MM )+

F lot the circumference

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output M Xoutput *I+0% iY0

outputE M XoutputE*I+0y iY0

elseif *strcmp*cal mode,Obresenham8olidO+ MM )+

F reate a solid circle

tempE M X'I)Iy iYO0

temp M tempE0

temp *I+ M % i0

output M Xoutput *I+0temp Y0

outputE M XoutputE*I+0tempEY0

else

error*O#nvalid optionO+0

end F determine if case ) or 2, > or &, or 3

if theta i [ '

delta M 24*theta i N y i+ )0

F determine whether case ) or 2

if delta [M '

F move hori$ontally

% i M % i N )0

theta i M theta i N *24% i+ N )0

else

F move diagonally

% i M % i N )0

y i M y i )0

theta i M theta i N *24*% i y i++ N 20

end

elseif theta i Z '

end

elseif theta i MM '

F move diagonally

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% i M % i N )0

y i M y i )0

theta i M theta i N *24*% i y i++ N 20

end

end

end


F alculate the 2nd quardrant

F

MMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMlength output M length*output +0

if temp mod MM )

F Avoids duplicate coordinates

output M Xoutput *Xlength output I )I2Y+4* )+0output *I+Y0

outputE M XoutputE*Xlength output I )I2Y+0outputE*I+Y0

else

output M Xoutput *Xlength output I )I)Y+4* )+0output *I+Y0

outputE M XoutputE*Xlength output I )I)Y+0outputE*I+Y0

end


F 8hift the circle to the desired center

F


output M output N 0

outputE M outputENE0

output coord M Xoutput ,outputEY0

F #f a solid is as(ed for ma(e sure there are no duplicates

if *strcmp*cal mode,Obresenham8olidO+ MM )+

output coord M unique*output coord,OrowsO+0

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end

function recogni$e

dat)Minput*Oenter the path of te%t file )O+0

dat2Minput*Oenter the path of te%t file 2O+0

a M te%tread*dat) , OFsO, OwhitespaceO, O O +0

b M te%tread*dat2 , OFsO, OwhitespaceO, O O +0

FdM/uclidian*a,b+0

if *cell2mat*a*J,)++]Mcell2mat*b*J,)+++

disp*O#-#8 does not matches with the current personO+0

else if *cell2mat*a*)',)++]Mcell2mat*b*)',)+++

disp*O#-#8 does not matches with the current personO+ 0

else disp*O#-#8 9 /8 !AT 6/8 ;#T6 T6/ --/ T /-8 O+0

end

end

function saveradii*name, ,8T+

nameMstrrep*name,O O,O O+0

dateMdatestr*now,2J+0

5ile ameMXname O O date O.t%tOY0

fileMfopen*5ile ame,OwtO+0

fprintf*file,OF2.'f nO,si$e* ,)++0

fprintf*file,OFsO,O-adiusI O+0

fprintf*file,OF2.'f nO,X8TY+0

fprintf*file,OFs nO,O O+0

fprintf*file,OFs nO,O O+0

fclose*file+0

function XTYMt autoset*#+

F#Mimread*Oeye.jpgO+

graycountMimhist*#+0

s$Msi$e*#+0

TM'0

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graycountMfilter*X'.& ' '.&Y,),graycount+0

Ffigure,imhist*#+0

diff cntMdiff*graycount+0

pre ma% M graycount*)+0

for i M 2 I *length*diff cnt+ )+

if *graycount*i+Zgraycount*i )++\*graycount*iN)+[graycount*i++\*diff cnt*i

)+Z'+\*diff cnt*iN)+['+

F

pre ma%Mma%*graycount*i+,pre ma%+0

continue0

end

if *graycount*i+[graycount*i )++\*graycount*iN)+Zgraycount*i++\*diff cnt*i)+['+\*diff cnt*iN)+Z'+\*graycount*i+['.D&4pre ma%+\*pre ma%1*s$*)+4s$*2++Z'.'

'2&+

TMi0

brea(0

end

end

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CONCLUSION

;e have successfully developed a new #ris -ecognition system capable of

comparing two digital eye images. This identification system is quite simple requiring

few components and is effective enough to be integrated within security systems that

require an identity chec(. The errors that occurred can be easily overcome by the use of

stable equipment. udging by the clear distinctiveness of the iris patterns we can e%pect

iris recognition systems to become the leading technology in identity verification.

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BIBLOGRAPHY

QThe Biometrics technologyR Third /dition.

by 7uodong 7uo. Q#-#8 -ecognitionR 8econd /dition.

by -oy :aushi( and rabir Bhattacharya. QThe #-#8 -ecognition 8ystemR

by (shvi( 9ouglas. QBiometric systemsR

by ;ayle ;olf

www.alibris.com

www.adaptiveoptics.org www.icdri.org

www.sans.org

www.google.co.in

www.wi(ipedia.com
http://www.alibris.com/http://www.adaptiveoptics.org/http://www.icdri.org/http://www.sans.org/http://www.google.co.in/http://www.wikipedia.com/http://www.alibris.com/http://www.adaptiveoptics.org/http://www.icdri.org/http://www.sans.org/http://www.google.co.in/http://www.wikipedia.com/

20051739 Iris Recongition (1)

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Transcript of 20051739 Iris Recongition (1)