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Authors: Waqar S. Qureshi 1 ; Shin'ichi Satoh 2 ; Matthew N. Dailey 3 and Mongkol Ekpanyapong 3

Affiliations: 1 National University of Science & Technology and Asian Institute of Technology, Pakistan ; 2 National Institute of Informatics, Japan ; 3 Asian Institute of Technology, Thailand

ISBN: 978-989-758-004-8

Keyword(s): Super-pixels, Dense Classification, Visual Word Histograms.

Related Ontology Subjects/Areas/Topics: Applications ; Computer Vision, Visualization and Computer Graphics ; Image and Video Analysis ; Pattern Recognition ; Robotics ; Segmentation and Grouping ; Software Engineering

Abstract: Autonomous monitoring of fruit crops based on mobile camera sensors requires methods to segment fruit regions from the background in images. Previous methods based on color and shape cues have been successful in some cases, but the detection of textured green fruits among green plant material remains a challenging problem. A recently proposed method uses sparse keypoint detection, keypoint descriptor computation, and keypoint descriptor classification followed by morphological techniques to fill the gaps between positively classified keypoints. We propose a textured fruit segmentation method based on super-pixel oversegmentation, dense SIFT descriptors, and and bag-of-visual-word histogram classification within each super-pixel. An empirical evaluation of the proposed technique for textured fruit segmentation yields 96.67% detection rate, a per-pixel accuracy of 97.657%, and a per frame false alarm rate of 0.645%, compared to a detection rate of 90.0%, accuracy of 8 4.94%, and false alarm rate of 0.887% for the baseline sparse keypoint-based method. We conclude that super-pixel oversegmentation, dense SIFT descriptors, and bag-of-visual-word histogram classification are effective for in-field segmentation of textured green fruits from the background.. (More)

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Paper citation in several formats:
Qureshi, W.; Satoh, S.; Dailey, M. and Ekpanyapong, M. (2014). Dense Segmentation of Textured Fruits in Video Sequences.In Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014) ISBN 978-989-758-004-8, pages 441-447. DOI: 10.5220/0004689304410447

@conference{visapp14,
author={Waqar S. Qureshi. and Shin'ichi Satoh. and Matthew N. Dailey. and Mongkol Ekpanyapong.},
title={Dense Segmentation of Textured Fruits in Video Sequences},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014)},
year={2014},
pages={441-447},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004689304410447},
isbn={978-989-758-004-8},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014)
TI - Dense Segmentation of Textured Fruits in Video Sequences
SN - 978-989-758-004-8
AU - Qureshi, W.
AU - Satoh, S.
AU - Dailey, M.
AU - Ekpanyapong, M.
PY - 2014
SP - 441
EP - 447
DO - 10.5220/0004689304410447

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