A Robust Coin Recognition Method with Rotation Invariance

Thumwarin, P.; Malila, S.; Janthawong, P.; Pibulwej, W.; Matsuura, T.

doi:10.1109/icccas.2006.284690

Cited by 18 publications

(6 citation statements)

References 2 publications

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“…77 out of 80 variably rotated coin images were correctly identified. In 2006 [12] P.Thumwarin et al presented a robust method for coin recognition among rotation invariance. In this paper the variations on surface of coin such as light reflection effect are also considered.…”

Section: A Approaches For Indian Modern Coinsmentioning

confidence: 99%

Coin Recognition and Classification: A Review

Waghulde¹

2017

IJRASET

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Coins are a primary need of human life. They are used in everyone's daily routine, like banks, transport, market and these spare changes also has various other uses than realization traded in for cash similar to measurement purpose, in games (toss), in organizations for research point, etc. So, it holds a vast significance that coins can be detected with high accuracy. Main aim of a coin recognition system is to classify high volumes of coins with high accuracy within short time duration. In this paper we discussed a various techniques of coin recognition systems in respective terms of their accuracy. Also focused on different coin recognition approaches have been planned by different researchers based on image recognition method. Categorization is based on images from both sides as well as a radius of the coin.

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Section: A Approaches For Indian Modern Coinsmentioning

confidence: 99%

Coin Recognition and Classification: A Review

Waghulde¹

2017

IJRASET

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“…In 2006 [12] P.Thumwarin et al presented a robust method for coin recognition with rotation invariance. In this method the rotation invariance feature is represented by the absolute value of Fourier coefficients of polar image of coin on circles with different radii.…”

Section: Approaches For Modern Coinsmentioning

confidence: 99%

Image Processing based Systems and Techniques for the Recognition of Ancient and Modern Coins

Modi¹,

Bawa²

2012

IJCA

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Coins are frequently used in everyday life at various places like in banks, grocery stores, supermarkets, automated weighing machines, vending machines etc. So, there is a basic need to automate the counting and sorting of coins. For this machines need to recognize the coins very fast and accurately, as further transaction processing depends on this recognition. Three types of systems are available in the market: Mechanical method based systems, Electromagnetic method based systems and Image processing based systems. This paper presents an overview of available systems and techniques based on image processing to recognize ancient and modern coins.

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“…Invariance P. Thumwarin et al (2006) had an intention to build a system for recognizing Thai currency coins. The point is the dataset they used.…”

Section: A a Robust Coin Recognition Methods With Rotationmentioning

confidence: 99%

“…The coefficient values are rotation-invariant features that can be matched by using Euclidean distance equation. For the test, they compared 69 coins to a reference amulet coin, and the system could classify between matched and mismatched type for 100 percent [1].…”

Section: A a Robust Coin Recognition Methods With Rotationmentioning

confidence: 99%

Thai Buddhist Amulet Recognition System

2014

International Academy of Engineers (IA-E) Dec. 30-31, 2014 Bangkok, Thailand

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Abstract-The objective of this research is to recognize digital images of Thai Buddhist amulets by using image processing techniques and artificial neural network (ANN). The scope is to recognize 100 kinds of powder Buddhist amulet. To extract features from collected samples, the extraction process considers on shape, color, and texture. In total, 54 feature values are calculated fed into recognition process. To recognize those features, there are two ANN models applied, which are perceptron and multi-layer perceptron. In the experiment, those two models are tested with a test set. On average, the perceptron can recognize the amulet images correctly at 97.67 percent, and the multi-layer perceptron can produce the best classification result at 100 percent. The average computational time for image preprocessing and feature extraction step is about 7.18 seconds per image. To proceed recognition step, the system approximately takes 0.19 second per image for the perceptron and 0.18 second per image for multi-layer perceptron on average.Index Terms-Buddhist amulet, pattern recognition, image processing, artificial neural network.

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A Robust Coin Recognition Method with Rotation Invariance

Cited by 18 publications

References 2 publications

Coin Recognition and Classification: A Review

Coin Recognition and Classification: A Review

Image Processing based Systems and Techniques for the Recognition of Ancient and Modern Coins

Thai Buddhist Amulet Recognition System

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