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Automated Coin Grader for Rare Coins - Technical Report

Explore the development of an automated system to grade, appraise, and authenticate rare coins, addressing challenges faced in manual grading and the dynamic nature of the collectibles market. The project utilizes Java, JavaScript, HTML, C++, and imaging processing for creating a database, scanning images, and outputting grades. Features edge detection, histograms, and a web interface. Learn about the benefits, architectural design, and future goals of the Automated Coin Grading System.

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Automated Coin Grader for Rare Coins - Technical Report

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Presentation Transcript

  1. Automated Coin Grader Ping Gallivan Xiang Gao Eric Heinen Akarsh Sakalaspur

  2. Overview • Introduction • Technical report -histograms -edge Detection -web Interface • Conclusion • Demo

  3. Long Term Goal of Project • Develop a system that will be used to grade, appraise and authenticate valuable collectibles items such as rare coins providing consistent and repeatable results.

  4. The Need for anAutomated Coin Grader • Unreliable results from manual grading • Value of the coins • Grading judgment changes from person to person • Fakes are plentiful • Many different denominations of coins • The rare coin market is dynamic and with significant changes occurring every week or so

  5. Goals for our project • Develop an Automated Coin Grading System • Web based Coin Grading Quiz

  6. Tools Used • Java • Java Script • HTML • C++ • Imaging Processing Packages

  7. Architectural Designof System Overview Scanner Image Processor DB Extracts features Scans Output System Display Grades

  8. Creating Database • Obtain a Coin Image (.gif) • 36 Coins Histograms • 36 Coin Edge Detection Images • Distance Measurements

  9. Image Processing • Hue: the color reflected from or transmitted through an object. • Saturation: Saturation- the strength or purity of a color • Brightness: Brightness- the relative lightness or darkness of a color

  10. Image Processing Measure HistogramObtain statistical data on the scanned pixels in the image in terms of the Hue, Saturation & Brightness vectors

  11. Distance MatrixThe statistical data collected in step 2 allows us to determine which coins are similar to others in our database in terms of known grade.

  12. Histrogram Analysis

  13. Coin Grade Processing Results

  14. Web Based Coin Grading Quiz Site

  15. Benefits of the Quiz Site • Educate and attract new collectors with a fun and interactive web interface • Acclimate the public and the coin grading industry to the idea of electronic grading

  16. Image Processing Edge Detection Edge Detection allows us to look at a coin in a 3D view and pickup additional features.

  17. Web Site Results Page

  18. Analysis • “..nothing can compare to examining a coin in person. “ • Four distinct factors • Surface Preservation • Strike • Lustre • Eye-Appeal

  19. Surface Preservation - This includes the presence of bagmarks, hairlines from cleaning or mishandling, and other imperfections, whether mint caused or man made. • Strike - Refers to the sharpness and completeness of detail, with the normal characteristics of that particular type, date and mint mark taken into account. • Lustre - This encompasses the brilliance, sheen and contrast of the coin, again taking the normal characteristics of the particular issue into account • Eye-Appeal - That certain aesthetic appeal that results from the combination of all of the coin's qualities.

  20. Process • Single image of the coin under defined lighting conditions should be captured in digital form using a high resolution camera. • Various portions of the captured images are to be computer enhanced to bring out important features of the coin. • The key regions of the coin need to be examined in great detail to identify, classify, measure, and score all flaws. • A light flow and reflectance analysis should be used to precisely measure the mirror as well as the inherent lustre of the coin.

  21. Future Work • Expand image processing to include advanced feature recognition beyond HSB and Edge Detection. • Increase the database to include a larger sample set and other denominations. • Design an intuitive user interface for scanning and grading. • Move closer towards automated grading • Secure funding to cover the costs of equipment & software required

  22. Future Work • Key components of the coin including obverse and reverse marks, strike, lustre, eye appeal, mirror, toning, and exceptional conditions need to be considered to arrive at a set of ”expert rules”. • Expert Rules – Final Grade

  23. Conclusion • what does the future have in store for the grading of coins? • Aid the human graders in making a final determination of the grade of the coin • Computer grading systems can be highly consistent, accuracy of about 90% • Image archiving will store one or more images of the coin for future reference • Reduces turn around time and cost

  24. Questions???&Demonstration

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