Abstract-Coins square measure integral a part of our day to day life. We tend to use coins everyplace like grocery market, banks, buses, trains etc. Therefore it is a basic want that coin is recognized and counted. The target of this paper is to classify the Indian coins of different denomination discharged recently. The objective is to notice the Indian coins and count its total worth. The system is projected to design coin recognition by applying Advanced HarrisHessian Algorithm, supported the parameters of Indian coins such as size, shape, weight, surface and so on . This paper presents a coin recognition methodology with rotation invariance. For circle detection use Hough Transform

R Pugazhenthi

Saranya Y M Das

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ISSN: 2278 – 1323 
International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) 
Volume 2, No 5, May 2013 
1689 
www.ijarcet.org 
 
Harris-Hessian Algorithm for Coin 
Apprehension 
 
Saranya das.Y.M
1
,R.Pugazhenthi 
2
  
 
1Dept.of ECE, Sri Lakshmi Aammal Engineering College, Chennai. 
2Dept.of ECE, Sri Lakshmi Aammal Engineering College, Chennai. 
 
 
 
Abstract— Coins square measure integral a part of our day to day 
life. We tend to use coins everyplace like grocery market, banks, 
buses, trains etc. Therefore it is a basic want that coin is recognized 
and counted. The target of this paper is to classify the Indian coins 
of different denomination discharged recently. The objective is to 
notice the Indian coins and count its total worth. The system is 
projected to design coin recognition by applying Advanced Harris-
Hessian Algorithm, supported  the parameters of Indian coins such 
as size, shape, weight, surface and so on . This paper presents a 
coin recognition methodology with rotation invariance. For circle 
detection use Hough Transform. 
 
Keywords-smoothing, Interest point detection, Hough Transform 
technique 
 
I.  INTRODUCTION 
 
We cannot imagine our life while not coins. We tend to use 
coins in our existence virtually every place like in banks, 
supermarkets, grocery stores etc. They need been the integral a part 
of our day to day life. Therefore there is basic want of extremely 
correct and economical automatic coin recognition system. Coin 
recognition systems can also be used for the analysis purpose by the 
institutes or organizations that alter the traditional coins. There are 
three types of coin recognition systems available in the market based 
on different methods: 
 Mechanical methodology primarily  based systems 
 Electromagnetic methodology primarily based systems 
 Image process primarily based systems 
The mechanical methodology primarily based systems use 
parameters like diameter or radius, thickness, heaviness and 
magnetism of the coin to differentiate between the coins. However 
these parameters cannot be wont to differentiate between the various 
materials of the coins. It means if we offer two coins one original and 
different pretend having same diameter, thickness, weight and 
magnetism however with completely different materials to 
mechanical methodology primarily based coin recognition system 
then it will treat each the coins as original coin so these systems can 
be fooled easily. 
The electromagnetic method based systems can 
differentiate between different materials because in these systems the 
coins are passed through an oscillating coil at a certain frequency and 
different materials bring different changes in the amplitude and 
direction of frequency. So these changes and the other characteristics 
like diameter, thickness, weight and magnetism can be used to 
differentiate between coins. The electromagnetic method based coin 
recognition systems improve the accuracy of recognition but still they 
can be fooled by some game coins. 
Nowadays banks usually use the bill counting machine to 
enumerate the money they have received. Obviously this is a more 
efficient method than counting the cash by hands. However, when the 
customer wants to pay a large number of cash into the bank, they still 
have to wait for quite a while. Particularly, there is large number of 
coins inside. And it is also easy to make mistakes for the bank staffs 
to calculate. Some coins from completely different foreign currency 
look similar. Therefore, generally it is tough to distinguish them by 
using human eyes, especially for large amount of coins. Moreover, 
attributable to the globalization, the banks usually receive foreign 
currency that the workers might not acknowledge. The charities face 
the constant state of affairs, because the donators come from all over 
the world. So it is necessary to develop a system that may help 
facilitate them to recognize and calculate the money that they receive.  
In 2002 there are twelve countries in Europe that modified 
their local currencies to the Euro. That means, great volumes of 
money had to be physically come back to the national banks of the 
member states. National banks want such a tool that can sort and 
recirculate these high volumes of coins. Therefore in 2003 a coin 
sorting device referred to as Dagobert was developed by ARC 
Seibersdorf analysis GmbH in step with the necessities of nation 
banks. The coins from more than 100 countries were sorted by 
Dagobert among two years. In total more than 2000 different coin 
faces of over 600 different coin types had to be trained and comprise 
the backbone of the recognition unit of Dagobert. This can be a 
extremely sensible starting point for developing automatic coins 
counting system (Nölle 2004, p.284). There are several similar 
methods around. One is coin images Seibersdorf-Benchmark method. 
The second one is called automatic coin counter that was designed by 
J. Provine, Mike McClintock, Kristen Murray, and Angela Chau, who 
were students from Rice University in Houston.  
The main goals of this project are:  
 Recognize the coins on the image.  
 Count the coins, and then get the total value.  
The sub-goals for the project are:  
 Group the coins according to their features (such as size, 
shapes).  
 Distinguish the coins in the same group according to the 
size or shape.  
 Count the coins within the group.  
 Calculate the total worth of the coins that are displayed on 
the image.  
 
 
II. RELATED WORK 
 
In 1992 [6] Minoru Fukumi et al. given a rotational 
invariant neural pattern recognition system for coin recognition. They 
performed experiments taking 500 yen coin and 500 won coin. 
During this work they need created a multilayered neural network 
and a preprocessor consisting of many slabs of neurons to supply 
rotation invariance. They additional extended their work in 1993 [7] 
and tried to attain 100% accuracy for coins. During this work they 
need used BP (Back Propagation) and GA (Genetic Algorithm) to 
ISSN: 2278 – 1323 
International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) 
Volume 2, No 5, May 2013 
1690 
www.ijarcet.org 
 
design neural network for coin recognition. Adnan Khashman et al.  
given associated an Intelligent Coin Identification System (ICIS) in 
2006. ICIS introduced neural network and pattern averaging for 
recognizing rotated coins at various degrees. It shows 96.3% correct 
identification i.e. 77 out of 80 variably rotated coin images were 
correctly identified. Mohamed Roushdy proposed Generalized Hough 
Transform to detect coins in image. Many serious problems like 
shape of the coin, peak detection in surface of the coins are attempted 
by Reinhold Huber, but, it does not yield much result. Hence, it is 
introduced to use Robert’s edge detection method, Laplacian of 
Gaussion edge detection method, Canny edge detection method and 
Multi-Level Counter Propagation Neural Network (ML-CPNN) using 
the coin parameters to recognize all the coin images with good 
precision. 
In our work we have combined Hough Transform and 
Harris-Hessian algorithm to extract features from image. Then, these 
features are used to recognize the coins. In section 3 implementation 
details are given. In section 4 we have presented conclusion and 
results. Then, in section 5 the future work provided.  
 
III. PROPOSED SOLUTION 
 
The set-up includes a low end digital video camera and a 
computer. The camera is placed at the distance from the coin so that 
the image of the coin can be captured easily. However, due to the 
positioning of the camera or that of the coin, the coin image is at 
times slightly distorted or only a portion of the coin is visible, but 
nevertheless it still resembles to a circle. Harris-Hessian Algorithm 
has the capability of extracting features and CHT (Circular Hough 
Transform) has the capability of identifying the coin even when only 
portion of the coin is visible. Figure 1 shows the block diagram of the 
detection and recognition system. 
 
 
Figure1: Block Diagram 
 
A. Pre-Processing: 
 
Various preprocessing techniques have been used to make 
the input image suitable for the algorithm. In order to reduce the 
noise from the image, Gaussian filter has been used to smooth the 
image, and then an appropriate gray level thresholding is done to 
obtain a binary image. Pre-processing methods use a small 
neighborhood of a pixel in an input image to get a new brightness 
value in the output image. Therefore, this type of pre-processing 
operations is also called filtration. Local pre-processing methods can 
be divided into the two groups according to the goal of the 
processing: Smoothing suppresses noise or other small fluctuations in 
the image; equivalent to the suppression of high frequencies in the 
frequency domain. Sadly, smoothing conjointly blurs all sharp edges 
that bear necessary info regarding the image, and then we tend to 
shall think about smoothing techniques which is edge protective. 
They are based on the general idea that the average is computed only 
from those points in the neighborhood which have similar properties 
to the processed point. Local image smoothing can effectively 
eliminate impulsive noise or degradations appearing as thin stripes, 
but does not work if degradations are large blobs or thick 
stripes.  
 
B. Coin Feature Extraction using Harris-Hessian (H-H) 
Detector: 
  
  Harris-Hessian detector is based on Harris-Hessian 
algorithm and it extracted features from the original image of coin 
and usually, interest points are used as the feature points. Figure 2 
represents the different denomination of Indian coins- five rupee 
coin, 10 rupee coin, 2 rupee coin, 1 rupee coin and 25 paisa coin 
  
Harris-Hessian Algorithm steps: 
 
For a given gray scale image of coin perform the following 
a. Candidate Points Detection 
i. Set pre-selected scales 2, 4, 6, 8, 12, 16, 20, 24, which 
denote successive levels of the scale-space. 
        ii  Perform histogram analysis and    store the point 
coordinates to the variable 
iii Calculate redirect intersect point of       the        database 
images 
 Perform scalng  
 Store it to database 
 Until pixel equals EOP perform step 1 
 Else return image 
 Store in data base 
b. False Detection Elimination and Scale Confirmation: 
i. Calculate determinant point of the given 
image (user images) by applying the 
following formula 
H =    (1) 
 
Where Ixx, Iyy,and Ixy are the second order derivatives. The 
determinant value of H is 
DET(H) =   (2) 
Determinant value should be maximum. Therefore it detect 
maximum point values as feature points. After the detection calculate 
the threshold value of feature points of coins. It depends on the 
parameters of coin shown in table 1. 
 
C. Circle detection using Hough Transform(HT): 
 
Let the task be to notice a circle of a 
illustrious radius r in an image. The technique starts 
with a quest for dark image pixel, when such pixel is 
found a locus potential center points of the circle 
related to it is determined. Such a locus potential 
center points forms a circle with the radius r. If the 
ISSN: 2278 – 1323 
International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) 
Volume 2, No 5, May 2013 
1691 
www.ijarcet.org 
 
loci of potential circle center are created for all dark 
pixel identified in the image, created for all dark 
pixels identified in the image, the frequency can be 
determined with  
 
 
 
 
 
 
 
                                                                                                                   
       Figure 2: Indian coins of different denominations- five rupee coin, 10 rupee coin, 2 rupee coin, 1 rupee coin and 25 paisa coin 
 
 
that every pixel of the image area happens as part of 
the circle-center loci. The middle point of the circle 
being sought is represented by the pixel with the 
highest frequency of occurrence in the circle-center 
loci. Thus, the middle of the searched circle is 
determined. From the known circle radius, the coin 
image segmentation is performed. 
 
A static r belongs to a 2-D parameter space 
A(a,b), a variable r belongs to a 3-D parameter space 
A(a,b,r). If we wish to seek out both light and dark 
circles, two sides of every edge must be viewed. If we 
consider two edges in an image then the number of 
possible (a,b,r) values are decrease strongly. The local 
maximum within the parameter space are easier to seek 
out. Within edge points in the image, there are n(n-1)/2 
pairs to be viewed. Boundaries on r and testing on the 
ϕ ’s can restrict the number of (a,b,r) values to be 
calculated. 
 
 
 
 
 
Figure 3: Catalogue of Hungarian 
denomination 
 
 
Algorithm for Curve detection using the HT 
 
Step 1. Quantize parameter space among the boundaries of 
parameters a. The dimensionality n of the parameter space is 
determined by the amount of parameters of the vector a. 
Step 2. Form an n-dimensional accumulator array A(a) with structure 
matching the quantization of parameter space; set all elements to 
zero. 
Step 3. For each image point (x1, x2) within the appropriately 
threshold gradient image, increase all accumulator cells A(a), 
if  f(x, a) = 0, A(a) = A(a) + ΔA  
for all a inside the boundaries used in step 1. 
Step 4. Local maxima in the accumulator array A(a) correspond to 
realizations of curves f(x, a) that are present in the original image. 
D. Classification: 
 
After circle detection, classify the coin according to the radius and 
threshold values. That is, each coin has its own radius. Then we count 
the total values of the coin. 
 
IV. CONCLUSION AND RESULTS 
 
 In this paper Harris-Hessian based coin recognition system 
has been developed using MATLAB. In this system, firstly pre-
processing of the image is done and then these pre-processed images 
are fed to the Harris-Hessian detector, it detect interest points. These 
features are fed to the Hough Transform it detect circles and calculate 
the radius of coins. 
The HT is extremely strong within the presence of extra structures in 
the image as further being robust to image noise. Moreover, it may 
search for several occurrences of particular shape during the 
processing. The conventional sequential approach requires a lot of 
storage and extensive computation. 
In this paper, the subsequent three points are taken into the 
consideration for decreasing the maintenance cost. The perfect image 
of a coin is employed for learning and recognition. The 
implementation to a real system ensures the following important 
points: 
a) The Recognition rate is close to 100 percent. 
b) It is a low cost system. 
c) Recognition time is very less. 
ISSN: 2278 – 1323 
International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) 
Volume 2, No 5, May 2013 
1692 
www.ijarcet.org 
 
 
V. FUTURE WORK 
 
There are so many approaches available for recognition of 
contemporary coins. New approaches/ techniques developed for 
recognition of stack of coins.  
 
ACKNOWLEDGEMENT 
We wish to express our sincere thanks to all the staff member of ECE 
Department, Sri Lakshmi Aammal Engineering College for their help 
and cooperation. 
 
 
 
Table 1: Parameters of Indian coin 
 
Coin Value in 
Paisa  
 
Type of Coin  
 
Coin 
Diameter/side 
in mm  
Coin Shape  
 
Coin Weight 
(grams)  
 
Coin area In 
Cm2  
 
Coin average 
gray value  
 
Coin 
Thickness In 
mm  
 
1 Old 12 Square 1.15 1.5000 250 0.7000 
2 Old 15 Octagon 1.55 1.9600 249 0.9000 
3 Old 17 Hexagonal 1.89 2.4100 245 1.0000 
5 Old 18 Square 2.00 2.4500 250 1.1000 
5 New 18 Square 2.00 2.4500 250 1.1000 
10 Old-1 26 Octagon  2.24 5.3114 225 1.0000 
10 Old-2 24 Polygon 1.73 4.5257 225 1.0000 
10 New 16 Circle 1.99 2.0114 75 1.0000 
20 Old 25 Hexagonal 2.21 4.0595  240 1.2000 
20 New 25 Circle 2.30 4.5590  240 1.2000 
25 Old 20 Circle 2.95 3.1428  90 1.0000 
25 New 21 Circle 2.82 3.1467  90 1.0000 
50 Old 24 Circle 4.98 4.5257  100 1.6000 
50 New 22 Circle 3.72 3.8028  100 1.6000 
100 Old 27 Circle 5.97 5.7278  100 1.7000 
100 New 26 Circle 4.93 5.3114  100 1.7000 
200 Old 27 Polygon 5.91 5.7278  110 1.8000 
200 New 27 Circle 7.72 6.1600  110 1.8000 
500 Old 24 Circle 9.03 4.5257  200 3.0000 
500 New 24 Circle 6.14 4.5257  200 1.5000 
1000 New 26 Circle 6.20 4.7257  210 1.7000 
 
 
V. REFERENCES 
 
[1] Efficient Feature Detection and Effective Post-Verification for 
Large Scale Near-Duplicate Image Search Hongtao Xie, Ke Gao, 
Yongdong Zhang, Member, IEEE, Sheng Tang, Jintao Li, and Yizhi 
Liu 
[2] Adameck. M, Hossfeld.M, and M. Eich, (2003), ―Three color 
selective stereo gradient method for fast topography recognition of 
metallic surfaces‖, Proceedings of Electronic Imaging, Science and 
Technology (Martin A. Hunt and Jeffery R. Price, eds.), Machine 
Vision Applications in Industrial Inspection XI, vol. SPIE 5011, pp. 
128–139.  
[3] Davidsson.P, (1996), ―Coin classification using a novel technique 
for learning characteristic decision trees‖ by controlling the degree of 
generalization, Gordon and Breach Science Publishers, pp. 403–412. 
[4] Thumwarin. P, S.Malila, P.Janthawang, W.Pibulwej, T.Matsura, 
(2006), „A Robust Coin Recognition method with rotation 
Invariance‟ , IEEE, pp. 520-523. 
[5] Fukumi M. and Omatu S.,(1992) "Rotation-Invariant Neural    
Pattem Recognition System with Application to Coin Recognition", 
IEEE Trans. Neural Networks, Vol.3, No. 2, pp. 272-279. 
[6] Fukumi M. and Omatu S.,(1993) "Designing A Neural Network 
For Coin Recognition By A Genetic Algorithm",Proceedings of 1993 
International Joint Conference on Neural Networks, Vol. 3, pp. 2109-
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[7] Khashman A., Sekeroglu B. and Dimililer K.,(2006) ―Intelligent 
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International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) 
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[8] Roushdy , M.,(2007) ―Detecting Coins with Different Radii based 
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Harris-Hessian Algorithm for Coin Apprehension

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Harris-Hessian Algorithm for Coin Apprehension

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