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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 : ankita.pande2@gmail.com E-mail :abd_07@rediffmail.com
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 online@www.ijcta.com
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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
Ankita V Pande et al, Int.J.Computer Technology & Applications,Vol 5 (2),730-734
IJCTA | March-April 2014 Available online@www.ijcta.com
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ISSN:2229-6093
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.
Ankita V Pande et al, Int.J.Computer Technology & Applications,Vol 5 (2),730-734
IJCTA | March-April 2014 Available online@www.ijcta.com
732
ISSN:2229-6093
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.
Ankita V Pande et al, Int.J.Computer Technology & Applications,Vol 5 (2),730-734
IJCTA | March-April 2014 Available online@www.ijcta.com
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ISSN:2229-6093
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.
Ankita V Pande et al, Int.J.Computer Technology & Applications,Vol 5 (2),730-734
IJCTA | March-April 2014 Available online@www.ijcta.com
734
ISSN:2229-6093
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