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  • Rice University ELEC 301 Projects

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Pocket Change: Results

Module by: Tyler J.W. Barth, Aaron D. Cottle, John P. Stallcup, Christopher J. Vaucher. E-mail the authors

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Summary: This module describes the results from testing the Pocket Change coin recognition system.

We are very pleased with the results of our project. Passing in a composite image where the coins are identical to those in the database but are translated and rotated provides us with a perfect match for every coin. Where the accuracy starts to suffer is when we have to deal with the issues brought on by real world conditions. We ran a series of tests with various numbers of coins to determine an average accuracy rating of the system. Generally, it seems to be approximately 75% accurate.

Figure 1: Accuracy of coin recognition across multiple trials. Y-axis values are percentages.
Pocket Change Results
Pocket Change Results (Results Small.png)

These results are promising. While these do not match the perfect accuracy rates of the composite images, we do approach 90% accuracy in some tests. However, it is important to note that we ran the tests using various amounts of coins to test runtime. The number of coins used should not impact accuracy.

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Definition of a lens

Lenses

A lens is a custom view of Connexions content. You can think of it as a fancy kind of list that will let you see Connexions through the eyes of organizations and people you trust.

What is in a lens?

Lens makers point to Connexions materials (modules and collections), creating a guide that includes their own comments and descriptive tags about the content.

Who can create a lens?

Any individual Connexions member, a community, or a respected organization.

What are tags? tag icon

Tags are descriptors added by lens makers to help label content, attaching a vocabulary that is meaningful in the context of the lens.

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