Color Feature-Based Pillbox Image Color Recognition
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%.
P. Guo et al., "Color Feature-Based Pillbox Image Color Recognition," Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2017, Porto, Portugal), vol. 4, pp. 188-194, SciTePress, Feb 2017.
The definitive version is available at https://doi.org/10.5220/0006136001880194
Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2017 (2017: Feb. 27-Mar. 1, Porto, Portugal)
Electrical and Computer Engineering
Keywords and Phrases
Computer graphics; Computer vision; Image processing; Pelletizing; Support vector machines; Classifier training; Color recognition; Feature descriptors; Image processing technique; National library of medicines; Pillbox Image; Red, green and blues; Reference image; Color; Support vector machine
International Standard Book Number (ISBN)
Article - Conference proceedings
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01 Feb 2017