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Authors: Sudhir Sornapudi 1 ; R. Joe Stanley 1 ; Jason Hagerty 2 and William V. Stoecker 3

Affiliations: 1 Missouri University of Science and Technology, United States ; 2 Missouri University of Science and Technology and Stoecker & Associates, United States ; 3 Stoecker & Associates, United States

ISBN: 978-989-758-225-7

ISSN: 2184-4321

Keyword(s): Segmentation, Clustering, Superpixels, Graph Theory, Region Adjacency Graph, Threshold Cut.

Related Ontology Subjects/Areas/Topics: Color and Texture Analyses ; Computer Vision, Visualization and Computer Graphics ; Image and Video Analysis ; Image Enhancement and Restoration ; Image Formation and Preprocessing ; Segmentation and Grouping ; Shape Representation and Matching

Abstract: Misidentified or unidentified prescription pills are an increasing challenge for all caregivers, both families and professionals. Errors in pill identification may lead to serious or fatal adverse events. To respond to this challenge, a fast and reliable automated pill identification technique is needed. The first and most critical step in pill identification is segmentation of the pill from the background. The goals of segmentation are to eliminate both false detection of background area and false omission of pill area. Introduction of either type of error can cause errors in color or shape analysis and can lead to pill misidentification. The real-world consumer images used in this research provide significant segmentation challenges due to varied backgrounds and lighting conditions. This paper proposes a color image segmentation algorithm by generating superpixels using the Simple Linear Iterative Clustering (SLIC) algorithm and merging the superpixels by thresholding the region adj acency graphs. Post-processing steps are given to result in accurate pill segmentation. The segmentation accuracy is evaluated by comparing the consumer-quality pill image segmentation masks to the high quality reference pill image masks. (More)

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Paper citation in several formats:
Sornapudi, S.; Joe Stanley, R.; Hagerty, J. and V. Stoecker, W. (2017). Real-world Pill Segmentation based on Superpixel Merge using Region Adjacency Graph.In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4 VISAPP: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-225-7, ISSN 2184-4321, pages 182-187. DOI: 10.5220/0006135801820187

@conference{visapp17,
author={Sudhir Sornapudi. and R. Joe Stanley. and Jason Hagerty. and William V. Stoecker.},
title={Real-world Pill Segmentation based on Superpixel Merge using Region Adjacency Graph},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4 VISAPP: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={182-187},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006135801820187},
isbn={978-989-758-225-7},
}

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4 VISAPP: VISAPP, (VISIGRAPP 2017)
TI - Real-world Pill Segmentation based on Superpixel Merge using Region Adjacency Graph
SN - 978-989-758-225-7
AU - Sornapudi, S.
AU - Joe Stanley, R.
AU - Hagerty, J.
AU - V. Stoecker, W.
PY - 2017
SP - 182
EP - 187
DO - 10.5220/0006135801820187

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