Color Feature-based Pillbox Image Color Recognition

Peng Guo, Ronald J. Stanley, Justin G. Cole, Jason Hagerty, William V. Stoecker

Abstract

Patients, their families and caregivers routinely examine pills for medication identification. Key pill information includes color, shape, size and pill imprint. The pill can then be identified using an online pill database. This process is time-consuming and error prone, leading researchers to develop techniques for automatic pill identification. Pill color may be the pill feature that contributes most to automatic pill identification. In this research, we investigate features from two color planes: red, green and blue (RGB), and hue saturation and value (HSV), as well as chromaticity and brightness features. Color-based classification is explored using MatLab over 2140 National Library of Medicine (NLM) Pillbox reference images using 20 feature descriptors. The pill region is extracted using image processing techniques including erosion, dilation and thresholding. Using a leave-one-image-out approach for classifier training/testing, a support vector machine (SVM) classifier yielded an average accuracy over 12 categories as high as 97.90%.

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Paper Citation


in Harvard Style

Guo P., J. Stanley R., G. Cole J., Hagerty J. and V. Stoecker W. (2017). Color Feature-based Pillbox Image Color Recognition . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-225-7, pages 188-194. DOI: 10.5220/0006136001880194


in Bibtex Style

@conference{visapp17,
author={Peng Guo and Ronald J. Stanley and Justin G. Cole and Jason Hagerty and William V. Stoecker},
title={Color Feature-based Pillbox Image Color Recognition},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={188-194},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006136001880194},
isbn={978-989-758-225-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017)
TI - Color Feature-based Pillbox Image Color Recognition
SN - 978-989-758-225-7
AU - Guo P.
AU - J. Stanley R.
AU - G. Cole J.
AU - Hagerty J.
AU - V. Stoecker W.
PY - 2017
SP - 188
EP - 194
DO - 10.5220/0006136001880194