Rick Bassett - 2002

Automated Coin Grader

Dissertation Status Update

Last Updated: April 21, 2023

Rick Bassett - 2002

Problem Statement

• Develop a model for a system that is capable of consistently grading (or determining the condition) of rare collectibles through the use of image analysis technology.

Rick Bassett - 2002

Rationale

• Accurately identify, grade and then determining the authenticity of rare collectible items such as coins, stamps, cards and comic books is a subjective non-automated process conducted by human Appraisers or Graders.

• Determining condition (grade) and authenticity are the two major factors that sellers can fake, with grading being by in large the most widely abused factor. The value of a collectible item can be substantially more or less (often by thousands of dollars) than its true value if the condition or grade of a collectible is improperly represented.

Rick Bassett - 2002

The Approach

• INPUT: A user will scan a collectible into a predefined format, the scanned image will serve as input into the Automated Grader

• PROCESS: The Automated Grader will process the image and determine the denomination, series, year and mintmark and the grade.

• OUTPUT: The System will return either summarized or detailed output information to the user.

Rick Bassett - 2002

Overview of Grading Model

Summary – grade result, series, mintmark and

year

Detail – Summary info plus grade result with reasoning, h istory on series and value

Input

Process Output Options

Scanned Image

Image Analysis Technology

Rick Bassett - 2002

Grading ModelInput Component

• A user will scan a collectible (or submit a scan) into a predefined format (*.gif) and the scanned image will serve as input into the Automated Grader.

Rick Bassett - 2002

Grading ModelProcess Component

3 Phase Approach

1. Histogram Distance Measurements

2. Edge Detection

3. Detailed Feature Extraction Engine

Rick Bassett - 2002

Grading Model – Process Component

# 1 Histogram Distance Measurements

• Determine the distance of the ENTIRE scanned image against other stored scanned images.

• The results are a measurement of how close this image is to other images (with respect to grade).

• Obtain statistical data on the scanned pixels in the image in terms of the Hue, Saturation & Brightness vectors.

Rick Bassett - 2002

Grading Model – Process Component

# 2 Edge Detection

Edge Detection allows the examination of an object in a 3D view to pickup additional features.

Edge Detection centers on the localization of significant variations of the grey level image and the identification of the physical phenomena that originated them.

Rick Bassett - 2002

Grading Model – Process Component

# 3 Detailed Feature Extraction• Rare coins series (and other

collectibles) have approximately 10 – 20 features each of which should be examined when determining a grade.

• Within the coin-collecting domain each of these features can fall into an established 1 to 70 grade point scale whereas 1 is the lowest and 70 and this highest. Some of the features carry more significance (weight) than others so ranking and weighted averaging would need to be incorporated.

• Human graders seldom examine all of the features in all of the grades due to the # of possible outcomes. (20**70 in the case of Lincoln Cents)

Interaction with the Series Domain Database (SDD) is at the heart of the Feature Extraction Engine

Rick Bassett - 2002

Features which should be examined when

determining the grade of a Lincoln Cent

1. Lettering – top “In God We Trust” aka Motto2. Lettering – left “Liberty”3. Lincoln’s Outline4. Date5. Mintmark6. Coat - Folds and detail in upper part of coat7. Coat - Folds and detail in lower part of coat into rim8. Facial - Eye9. Facial - Hair10. Facial – Cheek11. Facial – Forehead12. Facial - Jaw13. Facial – Ear14. Facial - Ear Lobe15. Facial - Mouth 16. Facial - Nose

1. Wheat stalks

2. Lettering – top “E Pluribus Unum”

3. Lettering – mid center “ONE CENT”

4. Lettering – bottom center “United States of

America”

Obverse Reverse

Rick Bassett - 2002

Series Domain Database (SDD) Specific Features at predefined X/Y coordinates

2000

0 2000

2000

0 2000

• Helps to identify the grade expectation particulars of each series of collectibles.

• Identifies anomalies within given series so that the feature recognition engine can examine more closely and be in the unique position to identify defects, alterations and counterfeits that are known to particular series/date/mintmark varieties.

Rick Bassett - 2002

A Look at the SDD