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Electronic Letters on Computer Vision and Image Analysis 5(4):47-63, 2006 Fast 3D-Vision System to Classify Metallic Coins by their Embossed Topography

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Title: Electronic Letters on Computer Vision and Image Analysis 5(4):47-63, 2006 Fast 3D-Vision System to Classify Metallic Coins by their Embossed Topography
Authors: Michael Hossfeld, Weiyi Chu, Markus Adameck Manfred
Contributors: The Pennsylvania State University CiteSeerX Archives
Source: http://elcvia.cvc.uab.es/public/articles/0504/a2005056-3-art.pdf.
Publication Year: 2005
Collection: CiteSeerX
Subject Terms: Key Words, Object Recognition, Structural Pattern Analysis, Specular Metallic Surfaces, Three-Color Selective Stereo Gradient Method (Three-Color SSGM
Description: This paper presents a security-related machine-vision solution for real-time classification of moving objects with highly reflective metallic surfaces and complex 3D-structures. As an application example of our so called Three-Color Selective Stereo Gradient Method (Three-Color SSGM) a classification system for three main coin denominations of Euro coins is presented. Such coins are quickly moving in a coin validation system. The objective is to decide only from comparison of specially measured and processed 3D-surface information with characteristic topographical data stored in a database whether a coin belongs to one of the reference classes or has to be rejected as a foreign or counterfeit coin. Under illumination from a three-color light emitting diode equipped ring a single image of the moving coin is captured by a digital color camera. Exploiting the spectral properties of the illumination sources, which correspond to the special spectral characteristics of the camera, three independent subimages can be extracted. Comparison between these subimages leads to a discrimination between a coin with real 3D-surface and a counterfeit coin based on a photographic image of a coin of the same type. After the coin has been located and segmented, grey value based rotation and translation invariant features are extracted from a normalized image. In combination with template matching methods, a coin can be classified. Classification results will be reported for the three main coin denominations of Euro coins.
Document Type: Thesis
File Description: application/pdf
Language: English
Relation: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.104.4020
Availability: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.104.4020
http://elcvia.cvc.uab.es/public/articles/0504/a2005056-3-art.pdf
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Accession Number: edsbas.452B5FBB
Database: BASE
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  Data: Electronic Letters on Computer Vision and Image Analysis 5(4):47-63, 2006 Fast 3D-Vision System to Classify Metallic Coins by their Embossed Topography
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  Data: This paper presents a security-related machine-vision solution for real-time classification of moving objects with highly reflective metallic surfaces and complex 3D-structures. As an application example of our so called Three-Color Selective Stereo Gradient Method (Three-Color SSGM) a classification system for three main coin denominations of Euro coins is presented. Such coins are quickly moving in a coin validation system. The objective is to decide only from comparison of specially measured and processed 3D-surface information with characteristic topographical data stored in a database whether a coin belongs to one of the reference classes or has to be rejected as a foreign or counterfeit coin. Under illumination from a three-color light emitting diode equipped ring a single image of the moving coin is captured by a digital color camera. Exploiting the spectral properties of the illumination sources, which correspond to the special spectral characteristics of the camera, three independent subimages can be extracted. Comparison between these subimages leads to a discrimination between a coin with real 3D-surface and a counterfeit coin based on a photographic image of a coin of the same type. After the coin has been located and segmented, grey value based rotation and translation invariant features are extracted from a normalized image. In combination with template matching methods, a coin can be classified. Classification results will be reported for the three main coin denominations of Euro coins.
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