An image segmentation approach for fruit defect detection using k-means clustering and graph-based algorithm. Pham, V. H. & Lee, B. R. Vietnam Journal of Computer Science, 2(1):25–33, 2015. ![An image segmentation approach for fruit defect detection using k-means clustering and graph-based algorithm [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Machine vision has been introduced in variety of industrial applications for fruit processing, allowing the automation of tasks performed so far by human operators. Such an important task is the detection of defects present on fruit peel which helps to grade or to classify fruit quality. Image segmentation is usually the first step in detecting flaws in fruits and its result mainly affects the accuracy of the system. A diversity of methods of automatic segmentation for fruit images has been developed. In this paper, a hybrid algorithm, which is based on split and merge approach, is proposed for an image segmentation that can be used in fruit defect detection. The algorithm firstly uses k-means algorithm to split the original image into regions based on Euclidean color distance in $$L\textasciicircum*a\textasciicircum*b\textasciicircum*$$ L * a * b * space to produce an over-segmentation result. Then, based on a graph representation, a merge procedure using minimum spanning tree is then taken into account to iteratively merge similar regions into new homogenous ones. This combination is an efficient approach to employ the local and global characteristic of intensities in the image. The experiment showed good results in the terms of human observation and in processing time.
@article{Van_Huy_Pham_70119896,
title = {An image segmentation approach for fruit defect detection using k-means clustering and graph-based algorithm},
volume = {2},
issn = {2196-8888},
url = {http://doi.org/10.1007/s40595-014-0028-3},
doi = {10.1007/s40595-014-0028-3},
abstract = {Machine vision has been introduced in variety of industrial applications for fruit processing, allowing the automation of tasks performed so far by human operators. Such an important task is the detection of defects present on fruit peel which helps to grade or to classify fruit quality. Image segmentation is usually the first step in detecting flaws in fruits and its result mainly affects the accuracy of the system. A diversity of methods of automatic segmentation for fruit images has been developed. In this paper, a hybrid algorithm, which is based on split and merge approach, is proposed for an image segmentation that can be used in fruit defect detection. The algorithm firstly uses k-means algorithm to split the original image into regions based on Euclidean color distance in \$\$L{\textasciicircum}*a{\textasciicircum}*b{\textasciicircum}*\$\$ L * a * b * space to produce an over-segmentation result. Then, based on a graph representation, a merge procedure using minimum spanning tree is then taken into account to iteratively merge similar regions into new homogenous ones. This combination is an efficient approach to employ the local and global characteristic of intensities in the image. The experiment showed good results in the terms of human observation and in processing time.},
number = {1},
journal = {Vietnam Journal of Computer Science},
author = {Pham, Van Huy and Lee, Byung Ryong},
year = {2015},
pages = {25--33},
}
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