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1. AN ENHANCED CIRCULAR DETECTION TECHNIQUE - RPSW
USING CIRCULAR HOUGH TRANSFORM
Anisha Ravikumar J
Sree Buddha College of Engineering
Abstract— Earth Surface consist of so many circular features
such as craters, volcanoes and other man-made structures. To
detect these circular features , so many detection techniques
where proposed. All that techniques are based on supervised
learning, unsupervised learning, machine learning etc. Here we
are going to propose a new automatic technique for the detection
of this circular features. Our method is actually based on the
multiplication operation of the original image with the rotated
image. We are combining the RPSW with circular Hough
transform. This method can detect the simple circles and can
also detect the more complex circular features. The method can
also find the area of the circular features.
Keywords- Feature extraction, Image Rotation , Pixel Swapping
Object detection, Remote sensing.
1. INTRODUCTION
The circular features, like impact craters or volcanoes, or
geological domes as well as the man-made
structures/symbols, are found on the satellite images. This
circular features are studied to get the relative age of the
planetary surface. Manually finding the circular features are
very difficult and it need more human skill. So in-order to
avoid that problem, so many automatic detection algorithms
are there. Image based and tomography based detection are
the two main approaches in the field of circular feature
detection.
Hough Transform and Wavelet transforms are the commonly
used techniques used for the image based detection
algorithms. Watershed and Terrin Derivatives are using in
topology based detection. Unsupervised detection of circular
features on the images depend on pattern recognition.
The main issues in lunar research is to find the age of lunar
surface by estimating the density of craters per unit area.so it
should detect the circular features clearly and also the shape
and size of the circular features should found clearly.
Detecting this type of circular features are very important for
finding the age of moon surface or other planetary surface.
So many automatic feature detection techniques have been
developed on the basis of pattern-matching tor Hough
Transform [1]–[2]. But in all the existing methods so many
problems are there. Main problem is about the accuracy of
the algorithm as well as the need of additional pre-processing
before applying the detection algorithms.
In this paper, we propose a new automatic method for the
detection and extraction of circular features from the satellite
images. This method is an image based detection technique.
We are doing this in black-and-white images (b binary image)
. This method is based partly on the pixel swapping (PSW)
for pattern extraction. [3].Here we are combining the
Rotational pixel swapping algorithm with the Hough
Transform. We are performing rotation of images as well as
the multiplication of images. The method can detect the
simple circular features as well as the more complex circular
features.
II. RPSW WITH HOUGH TRANSFORM FOR
DETECTION OF CIRCULAR FEATURES IN
SATELLITE IMAGES
With the combined RPSW and Hough Transform we can
accurately detect the circular features from the planetary
images.
A. RPSW
It means Rotational pixel swapping. The PSW algorithm
will extract the patterns by an inter-picture operation between
the original image with the translated image. In PSW
algorithm, as a rule of the translation operation target pattern
ISBN:978-1535061506
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Proceedings of ICRMET-2016
©IAETSD 201621

2. with in the shift table is used. In this method the pattern of a
similar size in the shift table can be extracted. So, while using
this PSW, we should prepare patterns with all the possible
sizes for the shift table. To avoid that problem Rotational
Pixel Swapping (RPSW) algorithm has come. RPSW extracts
rotational symmetric pattern by multiplying the original
image with the rotated images.
Let A(x, y) be the original image. We can apply this RPSW
algorithm only in binary images, so we want to covert the
image A(x, y) in to a binary image. If the pixel value A(x, y)=
0 then it is a black pixel. If the pixel value A(x, y)=1,thenit
will be a white image. x, y are the horizontal axes and the
vertical axes. (0,0) is the top left point of the image. We will
produce so many images from the original image A(x, y) by
rotating it upon different angles. The resultant rotated image
is represented as Bxn,yn,φ(x, y). Here φ(x, y) is the rotation
angle. After finding the rotated images, Mφ(x, y) will find for
each φ for producing he extracted image and let it be C.
Mφ(x, y) = A(x, y) · Bxn,yn,φ(x, y) (1)
After this operation we can see an enhancement in the
circular , all the other shapes are eliminated. Now we need
to find the centre of the circle .Below equation is used for
that.
R(x, y) = ∑ ∑ Bx,y,φ = k·Δφ(i, j) (2)
where (x, y) is the rotation centre for the calculation of the
rotated image Bx,y,φ, Δφ is an incremental angle of rotation,
and N is the total number of rotation images. If the rotation
centre (x, y) is near the CRSP, R is large. If the rotation centre
is far away from the CRSP, R is small. Thus, a relatively high
R value at a point indicates that the point is the centre of a
circular feature.
We are calculating R(x, y) for following region (i, j) only:
lmin < √(i − x)2 + (j − y)2 < lmax (3)
lmax and lmin are the maximum local RPSW radii and
minimum local RPSW radii By using lmax and lmin we can
reduce the processing time T for (x,y).
Fig 2.1: A colour composite image.
When we implement the calculation of R practically, N
rotation images should generate first by Δφ around the center
of the image and shifted them to the location needed for
different centres of the rotation. All the selected points (xn,
yn) whose R values are bigger than threshold value f · Rmax,
here f is threshold fraction and Rmax is maximum value in
R. Then we calculate Cn(x, y) for (xn, yn) for each Rotational
Symmetric Pattern whose R value is nth highest in R .It is
done by using following equation.
N
Cn(x, y) =∑ A(x, y) · Bxn,yn,φ = k·Δφ(x, y).
k=1
Here also we are calculating Cn(x, y) for the region (i, j) only
defined by the equation (3) and after that summed up all Cn(x,
y) for the Rotational Symmetric Pattern s which having R >
f · Rmax,
i.e., C(x, y) = ∑n Cn(x, y).
In RPSW method some problems are there that we identified.
We cannot find the area of the circular feature, we can find
only the centre of the circular features. To solve this problem,
we proposed a circular feature detection algorithm. In this
algorithm, it should produce the rotational images, and then
it should perform the multiplication and then perform
summation. After doing this, it should apply the Circular
Hough transform on the resultant image. Here, we are the
combining the RPSW algorithm with the Hough Transform.
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Proceedings of ICRMET-2016
©IAETSD 201622

3. Fig 2.3: : RPSW combined with Hough Transform
Circular Hough Transform is used to find the circular objects
in the noisy images. The method is based on the conversion
of images to binary images. Edge detection techniques such
as sobel operator or canny operators are using in the Circular
Hough Transform. A voting procedure is used in Circular
Hough Transform. Circular Hough Transform is based on the
equation of circles
r² = (x – a)² + (y – b)² (4)
a and b centre coordinates and r is the radius of the circle.
x = a + r*cos(θ) (5)
y = b + r*sin(θ) (6)
In CHT radius is set to a constant or we are providing a
particular rage of values. So that we can reduce the
complexity of using the algorithm.
A circle is drawn at each edge point with that edge point as
the origin point and radius is r. An array(3D) is used with
first 2Ds represents the coordinates of circle and last third
represents the radius. Whenever a circle is drawn with the
desired radii over each of the edge point, values in
accumulator array will increased. Accumulator, keeps the
count of how many circles are passing through the
coordinates of each of the edge points, and it will produce
a vote to find highest count. The coordinates of the centre
of the circles in the images are the coordinates with the
highest count.
Fig 2.3 : Sample image to do the process
Fig 2.4: Centre detected only in the circular region (output of
RPSW)
Fig 2.2: Block Diagram For The RPSW
ISBN:978-1535061506
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Proceedings of ICRMET-2016
©IAETSD 201623

4. Fig: 2.5: radius of the circle is detected and all other shapes are
eliminated.(After applying Circular Hough Transform)
When we apply this Circular Hough Transform in the
resultant RPSW output, we can find the correct radius as
well as the area of the particular circle. Using RPSW we can
find the centre of the circle and then can find the radius by
apply Circular Hough Transform.
III. CONCLUSION
In this paper, we are introducing a new method for finding
the centre, radius and area of the circular features in the
satellite images. Here both the RPSW Algorithm and the
Circular Hough Transform is combined to get the result
Acknowledgment
I thank almighty for giving me the strength and courage to
take a good area and to do research in that area. I am thankful
to my project guide for his suggestion sand help me by giving
all the useful facilities like Internet access as well as books,
which is more important to me. I am also very thankful to all
staffs in the Department of Computer Science & Engineering
of Sree Buddha College of Engineering, pattoor, Alappuzha
References
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ISBN:978-1535061506
www.iaetsd.in
Proceedings of ICRMET-2016
©IAETSD 201624