A Novel Method for Classification of Ancient Coins Based on Image Textures

Wei, Kaiping; He, Bin; Wang, Fang; Zhang, Tao; Ding, Quanjun

doi:10.1109/dmamh.2007.4414528

Cited by 2 publications

(2 citation statements)

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“…and Ant Colony optimization, which is a technique for selecting a portion of an image with a flexible shape [20]. Jain et al [8] assessed the ability of edge-based segmentation and Hough transformation methods for ancient coins to differentiate.…”

Section: Image Processingmentioning

confidence: 99%

An Autoencoding Method for Detecting Counterfeit Coins

Bavandsavadkouhi

Khazaee²,

Suen³

2022

Lecture Notes in Computer Science

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An Autoencoding-Based Anomaly Method for Detecting Counterfeit Coins Iman BavandsavadkouhiIn our daily lives, we use coins to pay for goods and services. However, the market for antique and historical coins is another place where coin quality and genuinity are important. Since counterfeiting has become more common as a result of technological advancements, dealing with fake coins is unavoidable. As a result, researchers have considered various methods in coin detection studies. In recent years, image-based coin detection has made extensive use of 2-D and 3-D image processing approaches. We propose a method for detecting counterfeit coins based on image content in this paper. We used SIFT, SURF, and MSER to determine the degree of similarity between our datasets. Then, using statistical analysis, we determine which descriptor is the most effective criterion for counterfeit coin detection. SIFT was chosen as the most reliable algorithm for the Danish and Canadian coin image dataset according to the Experiments' results. The autoencoder is then trained to detect anomalies in the coin images. A coin image is fed to the trained autoencoder as input and outputs a new image. Using the chosen criterion, the output image is compared to a baseline image. If the similarity between these two images is greater than a certain threshold, the coin is genuine. For training, most counterfeit coin detection methods require fake data. Our autoencoding-based anomaly method can eliminate this. Our proposed method for distinguishing genuine coins from counterfeit coins yielded promising results.In addition, we present a method for increasing the speed of counterfeit coin detection. We conducted our research on the Mint Mark of Canadian toonies coin images iv and we were able to achieve acceptable results by combining the edge detection technique with GAN and autoencoder. vAcknowledgment I would like to express my deepest appreciation to my supervisor Dr. Ching Yee Suen, who made this work possible. It has been an honor to work with him and I am grateful to him for believing in my abilities. His valuable guidance, helpful advice, and encouragement carried me through all the stages of writing my thesis. Throughout these years at university, I have always been proud to work with Dr. Ching Yee Suen as a superstar professor in image processing and pattern recognition. This endeavor would not have been possible without his support. I would like to thank all my colleagues, friends, and staff at CENPARMI at Concordia University for their assistance and motivation. I would like to extend my sincere thanks to Dr. Saeed Khazaee and Dr. Maryam Sharifi Rad for their support and helpful insight into my work. Their kindness is unforgettable. Special thanks go to Mr. Nicola Nobile, CENPARMI's research manager for his excellent technical support. Also, thanks to Ms. Phoebe Chan for all her help and consideration.I would also like to express my gratitude to my committee for reading and evaluating this thesis. Their comments and feedbacks are valuable an...

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Section: Image Processingmentioning

confidence: 99%

An Autoencoding Method for Detecting Counterfeit Coins

Bavandsavadkouhi

Khazaee²,

Suen³

2022

Lecture Notes in Computer Science

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“…Kaiping Wei et al [14] in 2007 presented a novel approach for classification of ancient coins based on image textual information. For extracting textual information Tree-Structured Wavelet Transform (TWT) and Ant Colony Optimization (ACO) algorithm is used.…”

Section: Approaches For Ancient 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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A Novel Method for Classification of Ancient Coins Based on Image Textures

Cited by 2 publications

References 0 publications

An Autoencoding Method for Detecting Counterfeit Coins

An Autoencoding Method for Detecting Counterfeit Coins

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

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