implementation on a self adaptive technique for image ... on a self adaptive technique for image...

Implementation on A Self Adaptive technique for Image Registration

working on variable Image parameters

Ankita V. Pande Anand B. Deshmukh

Assistant Professor Assistant Professor

Amravati, Maharashtra, India. Amravati, Maharashtra,

E-mail : [email protected] E-mail :[email protected]

Abstract Registration is a fundamental task in image

processing used to match two or more pictures taken,

for example, at different times, from different sensors,

or from different viewpoints. Virtually all large systems

which evaluate images require the registration of

images, or a closely related operation, as an

intermediate step. In the last decade, a new family of

registration algorithms based on evolutionary

principles has been contributed in order to overcome

the latter drawbacks. So proposed technique deals with

a new self-adaptive system that solves IR problems

consisting of different image parameters such as image

rotation, scaling, translation, Resize, enhancement,

inversion and Shearing. Keywords:

Image registration, Image Mapping,

Parameter evolution, Self Adaptive System.

1. INTRODUCTION

1.1 Basic Concepts

1.1.1 Color Image A color image is a digital image that includes color

information for each pixel. For visually acceptable

results, it is necessary to provide three samples for each

pixel, which are interpreted as coordinates in some

color space.

Fig 1.1.1 Basic pixel component representation

1.1.2 Gray Scale Image

A gray scale digital image is an image in which the

value of each pixels is a signal sample that is carries only intensity information. Images of this sort also

known as black and white are composed exclusively of

shades of gray varying from black at the weakest

intensity to white at the strongest.

1.2 Basic Concept of Image Registration

IMAGE REGISTRATION (IR) [1] is a

fundamental task in computer vision aimed at finding

either a spatial transformation (e.g., rotation,

translation) or a correspondence (matching of similar

image features) among two or more images acquired

under different conditions. Over the years, IR has been

applied to tackle many real-world problems ranging

from remote sensing to medical imaging, artificial vision, and computer-aided design (CAD). Image

registration (IR) [2] is a challenging topic in both the

computer vision and pattern recognition fields; its main

aim is to find the optimal transformation to provide the

best overlay or fitting between two or more images.

1.3 Image registration methodology

Terms Used :-

Target (or reference): the image that

is kept unchanged and is used as a basis for the

warping.

Source (or sensed): the image that is

geometrically transformed to be aligned with

the target image.

Transformation (or warping): the

function used to modify the source towards the

target image.

.

Ankita V Pande et al, Int.J.Computer Technology & Applications,Vol 5 (2),730-734

IJCTA | March-April 2014 Available [email protected]

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ISSN:2229-6093

Fig 1.2.1.Block Diagram of Image Registration

Image registration[3] can be defined as a mapping

between two images both spatially and with respect to

intensityi. If we define these images as two 2D arrays of

a given size denoted by I1 and I2 where I1(x,y) and

I2(x,y) each map to their respective intensity values,

then the mapping between images can be expressed as:

I2(x,y) = g(I1(f(x,y)))

Where f is a 2D spatial coordinate transformation

and g is 1D intensity or radiometric transformation. The

registration problem involves finding the optimal

spatial and intensity transformations so that the images

are aligned. The intensity transformation is frequently

not necessary, except, for example, in case where there

is a change in sensor type or where a simple look up

table determined by sensor calibration techniques is

sufficient.

Finding the spatial or geometric transformation is

generally the most important task to any registration

problems, which is frequently expressed as two

monotonous functions, fx, fy so that:

I2(x,y) = I1(fx(x,y), fy(x,y))…………. ....(1)

2. Analysis of Problem In the previous work following problems were

discovered:-

1] During the Image Registration process, the

detailed pixel by pixel mapping is not done.

A geometric transformation that

modifies the position of the pixels in the

pictures. This transformation results in a dense

deformation field between a reference image

and a target image. Our contributions related

to image registration mainly deal with this

kind of transformations, especially with non-

rigid ones.

A photometric transformation that

modifies the color of the pixels. This transformation models the illumination

changes. This type of transformation is not

always used when registering two images.

This is especially the case when the images are

registered using features which are invariant to

illumination changes.

2] Image registration is an area that works specific

towards only one technique at a time.

3] Another main drawback is that after completion

of image registration the resultant image is not accurately mapped with original image.

4] In the previous work, the concept of Auto Image

Registration is implemented but the concept of Cross

Image Registration is not yet implemented.

3. Implementation Details

To our knowledge, this is the first time a self-

adaptive approach [4,5] has been used for technique

which will combine work on the various parameters of

image. In order to overcome the drawbacks of previous

system, following system is proposed. It will not only

work on the problem of image registration but also

provides one system combining technique such as Scaling,Translation,Rotation,Enhancement,Inversion,S

hearing and Resize.

3.1 Algorithm of Implemented Technique:- The implemented system [6] consisting of an

framework which defines steps which will work as

follows.

1] First step is to select an input image or source

image or sensed image. Source image can be of any

type i.e. JPEG, PNG, TFF, GIF or BMP.

2] Next step is to convert a color image into an

grayscale image.

3] Next step is to apply various filters to remove

noise in the image and enhance the image. The filters

used to remove the noise are Gabor filter, Haar

Wavelet, Wavelet Db and Gaussian filter.

4] Then next steps is to evaluate parameter change

i.e. select an input image parameter such as scaling of



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an image, rotation of an image, inversion, rotation,

enhancement and shearing of an image.

5] Then the next step is to search pattern related to

the reference image.

6] Then next step is to perform point by point

mapping i.e. Image Registration Step, so as to find out the correspondence between the source image with

reference or target image. i.e. Search and locate pixels

of reference image with the source image.

7] Then next step is to display mapped pixels.

8] Then load registered image after the process of

image registration.

9] Then last step is to display recovered image and

convert that gray scale image into the color image.

10] Stop.

3.1 Flowchart of Implemented System

3.2.1 Flowchart of locating pixels of

reference image into source image.

No

Yes

Start

Select Source Image for Image Registration.

Select Target or Reference Image for Registration.

Apply various filters to remove noise in the

image.

.

.

Search and Locate the points of reference

image into the source image.

.

.

Display the Registered Image.

.

.

Stop

Start

If The size

of Input

Image>

Target Image

Input Source Image.

Input Target Image.

Compare the size of Input Image and

Target

Create Sub Matrix of both the Image.

Target

Compare the value of both the

matrix.

Target

Match found in both the matrix.

Object found

Display the Result.



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In the implemented method we create a system that

is useful to find the object that is present inside the

image. In this we first take a image as an input for

which we have to find the object. After selecting the

input image we have to focus on the target image. After

selecting the both input image and target image we

compare the size of both the image. If the size of input

image is greater than target image then we proceed with

our system otherwise we are going to focus on the input

image. If the above criteria get satisfied then we create

the sub matrixes of both the images for example we

create 3 X 3 matrixes. After creating the sub matrixes

we compare size of both the sub matrixes of both the

image.

This matrix can be created with the help of pixels

present in both the images. If matching found then we

conclude that the given object is found in the other

image. The object is shown as an output by creating red

boxes on that object. The output object image is

displayed and the co ordinates of that object that is the

height and width from top and bottom in the form of

coordinates is shown with the help of im tool.

Implemented System proposes two techniques

which are as follows.

1] Auto Image Registration:-The process of two

images mapping which are part of each other is called

as auto-image registration.

2] Cross Image Registration:-The image mapping

between two images which are not part of each other is

called as cross image registration. An image

registration process time is depends on pixels on an

Images and it’s hit ratio.

Th ∝(H x W)……………………………....Eq...3.1

Where

H=Height of Image1.

W=Width of an Image 2.

An Image registration in implemented method is an

entirely new approach than existing method where an

Image mapping base is it’s color, size and other

parameters.

Implemented approach mappes an images without

considering an signification of an image parameters. A

Deviation factor between two images can be expressed

as Lfr=| Li – Lf|…………………….………....Eq..3.1

Where

3.2 Flowchart for Image Recovery

In implemented methodology, an image recovery

process from registered image can be done as follows.

Fill Reference color image to Gray Scale object.

Gray Scale Object Color Object

In an implemented methodology, we replace color

content of gray scale image object with color content of

reference color image. Gray scale image of any type

(i.e. JPEG, BMP, PNG etc). If an image is of large

dimensions, we must need to resize it into a dimension

of 200 X 200.The reason behind this is that, number of

pixels are directly proportional to time required for

color conversion.

Pi ∝ Ti………………………………….Eq...3.2.

Where,

Pi= Number of pixels of input gray scale.

Ti= Time required for color conversion.

The reference image should be a color RGB image.

The reason behind is that, we are creating a dummy

image with color contents of reference color image.



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4. Experiment Result

Image

Type

Size Mean Intensity Entropy

JPEG 100X100 0.5 17.35

BMP 200X200 0.4 17.352

TIFF 300X300 0.6 18.68

GIF 200X250 0.5 17

Recovered Color

Image size

Mean Intensity Entropy

100X100 0.43 17.62

200X200 0.478 18.23

300X300 0.5 17.987

200X250 0.6234 18.464

Image

size 1

Image size 2 Mapped

Points

Time for

Registration(Sec)

100X100 100 x 100 10 58

200X200 100 x 100 11 85

300X300 100 x 100 12 95

200X250 100 x 100 08 93

CONCLUSION

In the implemented system, fast simple technique

is used to registered image and color recovery using

reference image. Original image is pre-processed to

illuminate noise and then conversion of gray scale

image into an color image process is done. By using

this technique a large number of auto and cross image

registration are done between color images with very

less effort.

Experimental results show that the implemented

scheme gives satisfactory performance for Image

Registration. The contribution and characteristics of the implemented approach are summarized below:

EFFICIENCY: Easy to implement and

fast to compute.

HIGHER PSNR: Implemented approach

provides higher PSNR and Accuracy as

compared to the previous work.

REFERENCES

[1]Jos´e Santamar´ıa “Self-Adaptive Evolution Toward

New Parameter Free Image Registration Methods IEEE

Trans. on Evolutionary Computation, VOL. 17, NO. pp 545-

547,4 AUGUST 2013.

[2]Barbara Zitova, Jan Flusser, “Image registration

methods: a survey”, Image and Vision Computing,

ELSEVIER,pp.977–1000, 21August,2003.

[3] G. E. Christensen and H. J. Johnson, “Consistent

Image Registration”, IEEE Transaction on medical imaging,

Vol. 20, No. 7, pp. 568-579, July 2001.

[4] Morgan McGuire, “An image registration technique

for recovering rotation, scale and translation Parameters”,

NEC Institute , 19Feb 2010.

[5]Jan Flusser, “An adoptive method for image

registration”, Institute of Information Theory, Vol 2,pp.45-

49,december 2007.

[6]Lisa Gottesfeld Brown,“A survey of image

registration techniques”, ACM computing surveys, vol. 24,

no. 4, pp. 325-376, 1992.



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