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
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
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..
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