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 $$L^*a^*b^*$$ 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.
Highlights
Automatic inspection of fruits is the subject of many grading and sorting systems to decrease production costs and increaseV
This makes the task of detection of defects present on fruit peel the target of many researches such as automatic citrus skin defect detection in [3] using a multivariate image analysis; detection of blemish in potatoes in [4], or in-line detection of apple defects in [5]
This paper has introduced a split and merge approach for image segmentation that aims to detect defects on fruit peel
Summary
Automatic inspection of fruits is the subject of many grading and sorting systems to decrease production costs and increaseV. R. Lee. The goal of many fruits’ inspection systems based on computer vision is to extract features of the fruits of interest and relate them with the quality which is normally associated with the absence of defects on fruit peel. The goal of many fruits’ inspection systems based on computer vision is to extract features of the fruits of interest and relate them with the quality which is normally associated with the absence of defects on fruit peel This makes the task of detection of defects present on fruit peel the target of many researches such as automatic citrus skin defect detection in [3] using a multivariate image analysis; detection of blemish in potatoes in [4], or in-line detection of apple defects in [5]. The core technique in this task is always related to image analysis and processing which is largely dependent on the segmentation procedure
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