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Pocket Change: Coin Identification

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

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Summary: A description of the actual comparison process of FFT from the input image vs. image in the database. Also, construction of the database.

So, now we have an FFT of the coin. What are we going to compare it to? A pre-created database of course! The database creation steps are pretty straightforward, but there are a number of reasons for developing a streamlined approach to it.

Database Creation

You could create the database manually by taking subsets of the input picture matrix and performing the unwrapping on them manually. Manual database editing might be feasible if you only had 10 coins to recognize. However, considering that the United States has minted coins for all fifty states in addition to entries for dirty coins and different lighting conditions, you’ll probably want to simplify the process. What we did was basically take our full code and hack it off before the unwrapping process. The radii and centers go into a function that displays the image of the coin to the user. The user then enters in values for the metadata associated with that image. In our case we used heads/tails, value in USD, coin name, and abbreviated coin descriptor (for debugging). We created a graphical interface in Matlab for loading images and loading and saving database files.

Figure 1: The coin input GUI we developed.
Coin input GUI
Coin input GUI (coingui.png)

Comparison

The resulting database should contain the metadata and the precomputed FFTs of the already unwrapped coins. The code then takes each input FFT and compares it to each stored FFT. There are many different algorithms to determine the “closeness” of two FFTs.

Dot Product

You can normalize each line of the FFT, and then do a vector dot product with the corresponding line in the other FFT. Then you take the average value of the resulting one-line vector. If the two FFTs are identical, they will return a value of 1. Depending on how different they are, the number will be something between 0 and 1.

Sum of Differences

You can take the FFTs, subtract them from each other, take the absolute value, and sum all of the resulting differences. Two identical matrices will return a value of 0. If the matrices are at all different, the sum will be greater than zero.

In our implementation we found the dot product approach to work best, though any number of matrix comparison algorithms could work. Once all of the FFTs have been identified, they should return the metadata to be displayed to the user.

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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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