Rotation-invariant neural pattern recognition system with application to coin recognition

Fukumi, Minoru; Omatu, Sigeru; Takeda, Fumiaki; Kosaka, Tatsuro

doi:10.1109/ijcnn.1991.170532

Cited by 15 publications

(14 citation statements)

References 4 publications

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“…pattern recognition systems insensitive to transformation of an input pattern have now been presented. P. Thumwarin [9] presented the rotation invariance feature by the absolute value of Fourier coefficients of polar image of coin on circles with different radii. Minoru Fukumi [10] had proposed a neural pattern recognition system, which is insensitive to rotation of input pattern by various degrees.…”

Section: Manymentioning

confidence: 99%

Morphological based Segmentation and Recognition of Indian Coins

Devi¹,

Chandrakala²

2016

IJECE

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Coins have very much importance in human's day to day life, which are used in everyone's daily routine like banks, super markets, vending machines etc. So, there is a basic need to automate the counting and sorting of coins. Coin recognition applications play an important role in industry and computer vision. Many approaches developed for the coin detection and calculate its corresponding values. This paper recognizes Indian coins of different denomination The recognition process consists of three steps, 1) we present a simple and fast method for coin segmentation, based on morphological thresholding technique to remove noise and to enhance the quality of coin image, 2) we applied some simple descriptors like mean intensity, area, perimeter to extract the regional features of the coins used for recognition and sorting and 3) we performed edge detection using statistical operators. Only after detecting the edges in the image the number can be recognized. In this paper we describe the pattern recognition method used for identification of coins in the new coin recognition and sorting system.

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Section: Manymentioning

confidence: 99%

Morphological based Segmentation and Recognition of Indian Coins

Devi¹,

Chandrakala²

2016

IJECE

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“…Fukumi et al [8] were the first to use computer vision methods for coin classification. Their system can distinguish 500 yen from 500 won coins.…”

Section: Related Workmentioning

confidence: 99%

Improving Ancient Roman Coin Classification by Fusing Exemplar-Based Classification and Legend Recognition

Zambanini

Kavelar

Kampel

2013

New Trends in Image Analysis and Processing – ICIAP 2013

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In this paper we present an image-based classification method for ancient Roman Republican coins that uses multiple sources of information. Exemplar-based classification, which estimates the coins' visual similarity by means of a dense correspondence field, and lexiconbased legend recognition are unified to a common classification approach. Classification scores from both coin sides are further integrated to an overall score determining the final classification decision. Experiments carried out on a dataset of 60 different classes comprising 464 coin images show that the combination of methods leads to higher classification rate than using them separately.

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“…Also instead of historical coins, they trained their system on a set of modern European coins that were gathered after the euro released. (Fukumi, Omatu, Takeda, & Kosaka, 1992) have used a rotationinvariant neural pattern recognition system to classify the pictures of 500 yen and won coins. The Eigen space method used by (Huber, Ramoser, Mayer, Penz, & Rubik, 2005) is an another study in this field.…”

Section: Introductionmentioning

confidence: 99%

Classification of Historical Anatolian Coins with Machine Learning Algorithms

Ünlü

2019

Alphanumeric Journal

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To find out which period the historical coins belong to requires a number of scientific procedures that archaeologists or experts can do. These operations can often be time-consuming and demanding operations. From this point on, in this study, the automatically classification of historical coins by using machine learning methods is discussed. Being able to use machine learning methods to classify historical coins can help experts and can become an analysis tool without the need for scientific tests for non-experts. For this purpose, some physical properties of different coins used in Anatolian geography were collected and classified by various machine learning methods named SVM, Random Forest, Bagging, and Decision Trees. Also, two different missing values strategies are deployed in conjunction with each chosen method. Based on our findings, random forest method together with imputing missing values with mean gives an acceptable results with the accuracy rate of %71, although there are some limitations such as high rate of missing values and working with a small dataset.

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Rotation-invariant neural pattern recognition system with application to coin recognition

Cited by 15 publications

References 4 publications

Morphological based Segmentation and Recognition of Indian Coins

Morphological based Segmentation and Recognition of Indian Coins

Improving Ancient Roman Coin Classification by Fusing Exemplar-Based Classification and Legend Recognition

Classification of Historical Anatolian Coins with Machine Learning Algorithms

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