Dense Segmentation of Textured Fruits in Video Sequences

Waqar S. Qureshi, Shin'ichi Satoh, Matthew N. Dailey, Mongkol Ekpanyapong

2014

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 84.94%, 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..

Paper Citation

in Harvard Style

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 2: VISAPP, (VISIGRAPP 2014) ISBN 978-989-758-004-8, pages 441-447. DOI: 10.5220/0004689304410447

in Bibtex Style

@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 2: VISAPP, (VISIGRAPP 2014)},
year={2014},
pages={441-447},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004689304410447},
isbn={978-989-758-004-8},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: 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