Harris-Hessian Algorithm for Coin Apprehension

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

Using the Optical Mouse Sensor as a Two-Euro Counterfeit Coin Detector

In this paper, the sensor of an optical mouse is presented as a counterfeit coin detector applied to the two-Euro case. The detection process is based on the short distance image acquisition capabilities of the optical mouse sensor where partial images of the coin under analysis are compared with some partial reference coin images for matching. Results show that, using only the vision sense, the counterfeit acceptance and rejection rates are very similar to those of a trained user and better than those of an untrained user

Ancient Roman coin retrieval : a systematic examination of the effects of coin grade

Ancient coins are historical artefacts of great significance which attract the interest of scholars, and a large and growing number of amateur collectors. Computer vision based analysis and retrieval of ancient coins holds much promise in this realm, and has been the subject of an increasing amount of research. The present work is in great part motivated by the lack of systematic evaluation of the existing methods in the context of coin grade which is one of the key challenges both to humans and automatic methods. We describe a series of methods – some being adopted from previous work and others as extensions thereof – and perform the first thorough analysis to date.Postprin

Towards computer vision based ancient coin recognition in the wild — automatic reliable image preprocessing and normalization

As an attractive area of application in the sphere of cultural heritage, in recent years automatic analysis of ancient coins has been attracting an increasing amount of research attention from the computer vision community. Recent work has demonstrated that the existing state of the art performs extremely poorly when applied on images acquired in realistic conditions. One of the reasons behind this lies in the (often implicit) assumptions made by many of the proposed algorithms — a lack of background clutter, and a uniform scale, orientation, and translation of coins across different images. These assumptions are not satisfied by default and before any further progress in the realm of more complex analysis is made, a robust method capable of preprocessing and normalizing images of coins acquired ‘in the wild’ is needed. In this paper we introduce an algorithm capable of localizing and accurately segmenting out a coin from a cluttered image acquired by an amateur collector. Specifically, we propose a two stage approach which first uses a simple shape hypothesis to localize the coin roughly and then arrives at the final, accurate result by refining this initial estimate using a statistical model learnt from large amounts of data. Our results on data collected ‘in the wild’ demonstrate excellent accuracy even when the proposed algorithm is applied on highly challenging images.Postprin

Evaluating machine learning techniques for archaeological lithic sourcing: a case study of flint in Britain

The original version of this Article contained errors in the legends of Figure 8 and 9. The legend of Figure 8: “Learning curve shows F1 score for train and test data against number of observations in training data.” now reads: “Box Plot of F1 Scores for each model, showing good equality of variances.

Selection of Robust Features for Coin Recognition and Counterfeit Coin Detection

Tremendous numbers of coins have been used in our daily life since ancient times. Aside from being a medium of goods and services, coins are items most collected worldwide. Simultaneously to the increasing number of coins in use, the number of counterfeit coins released into circulation is on the rise. Some countries have started to take different security measures to detect and eliminate counterfeit coins. However, the current measures are very expensive and ineffective such as the case in UK which recently decided to replace the whole coin design and release a new coin incorporating a set of security features. The demands of a cost effective and robust computer-aided system to classify and authenticate those coins have increased as a result. In this thesis, the design and implementation of coin recognition and counterfeit coin detection methods are proposed. This involves studying different coin stamp features and analyzing the sets of features that can uniquely and precisely differentiate coins of different countries and reject counterfeit coins. In addition, a new character segmentation method crafted for characters from coin images is proposed in this thesis. The proposed method for character segmentation is independent of the language of those characters. The experiments were performed on different coins with various characters and languages. The results show the effectiveness of the method to extract characters from different coins. The proposed method is the first to address character segmentation from coins. Coin recognition has been investigated in several research studies and different features have been selected for that purpose. This thesis proposes a new coin recognition method that focuses on small parts of the coin (characters) instead of extracting features from the whole coin image as proposed by other researchers. The method is evaluated on coins from different countries having different complexities, sizes, and qualities. The experimental results show that the proposed method compares favorably with other methods, and requires lower computational costs. Counterfeit coin detection is more challenging than coin recognition where the differences between genuine and counterfeit coins are much smaller. The high quality forged coins are very similar to genuine coins, yet the coin stamp features are never identical. This thesis discusses two counterfeit coin detection methods based on different features. The first method consists of an ensemble of three classifiers, where a fine-tuned convolutional neural network is used to extract features from coins to train two classifiers. The third classifier is trained on features extracted from textual area of the coin. On the other hand, sets of edge-based measures are used in the second method. Those measures are used to track differences in coin stamp’s edges between the test coin and a set of reference coins. A binary classifier is then trained based on the results of those measures. Finally, a series of experimental evaluation and tests have been performed to evaluate the effectiveness of these proposed methods, and they show that promising results have been achieved