Ms. Kelly Johanna Tamayo Zapata Profile

Kelly Johanna Tamayo Zapata

at Universidad EIA

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Computer vision

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Proceedings Article | 6 September 2019 Presentation + Paper

Banknotes classification system through image processing and pattern recognition for people with visual impairment

Gustavo Andres Moreno H., Manuel Forero, Kelly Tamayo Z.

Proceedings Volume 11137, 111371O (2019) https://doi.org/10.1117/12.2529752

KEYWORDS: Image processing, Image filtering, Data modeling, Classification systems, Neurons, Visualization, Pattern recognition, Cameras, Light sources and illumination, Digital filtering

Read Abstract +

In Colombia, the recognition of banknotes by blind people is increasingly difficult, because it requires extensive training and is increasingly difficult due to the emergence of new bills, aging, and circulation of counterfeit bills. To contribute to this recognition process, a classification system was carried out for eleven denominations of Colombian banknotes applying image processing and pattern recognition techniques. A prototype was developed, consisting of a frontal lighting system to eliminate shadows and detail textures. For the recognition of the banknotes, two stages were developed: The detection was carried out by means of image processing, the background was eliminated by means of binarization. Then, uniform interest points were taken over the entire bill, and a set of descriptors was obtained in a different color space. For identification, a database of 1100 samples was created, 100 of each bill, which was used to train different neural networks MLP, structuring a pipeline to vary the configuration parameters to obtain the model with greater accuracy. 70% of the data was used for training and 30% for verification. An initial accuracy of 85% was obtained by cross-validation. A significant improvement was achieved by adding new features and increasing the number of samples to 7700 by manipulating them, modifying the brightness and rotating the bills. With the new data, it was possible to increase the accuracy to 95% by cross-validation. The system was mounted on a Raspberry PI 3 for its practical application. A final test was done with 240 images captured in real time, 20 images from each banknote, making each prediction in 0.9 seconds and getting a general accuracy of 97%.

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