2015
DOI: 10.5424/sjar/2015131-6181
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An improved contour symmetry axes extraction algorithm and its application in the location of picking points of apples

Abstract: The key problem for picking robots is to locate the picking points of fruit. A method based on the moment of inertia and symmetry of apples is proposed in this paper to locate the picking points of apples. Image pre-processing procedures, which are crucial to improving the accuracy of the location, were carried out to remove noise and smooth the edges of apples. The moment of inertia method has the disadvantage of high computational complexity, which should be solved, so convex hull was used to improve this pr… Show more

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Cited by 14 publications
(8 citation statements)
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“…Therefore, it was very necessary to use image processing to extract the ROI (bruised and decayed parts). How to accurately segment the target object from the image was always a major challenge ( Wang et al, 2015). The RGB-based color model depends on the device, and pixel values could change significantly with the use of different imaging sensors.…”
Section: Discussionmentioning
confidence: 99%
“…Therefore, it was very necessary to use image processing to extract the ROI (bruised and decayed parts). How to accurately segment the target object from the image was always a major challenge ( Wang et al, 2015). The RGB-based color model depends on the device, and pixel values could change significantly with the use of different imaging sensors.…”
Section: Discussionmentioning
confidence: 99%
“…Consequently, an intelligent learning-based approach was proposed by many scholars. [134][135][136][137][138][139][140][141][142][143] Specifically, the clustering, modeling, and decision trees at given color spaces or components have been adopted to found fruits rather than the simple method of threshold segmentation. 134,135 For example, the K-means clustering algorithm with a color space transformation model was used to distinguish ripe apples.…”
Section: Single-feature Vision Methodsmentioning
confidence: 99%
“…For instance, Wang et al transformed the RGB color space to Lab color space and then adopted K-means clustering algorithm to recognize the occluded apples. 20,21 Xiong et al combined the improved fuzzy clustering method (FCM) and random signal histogram to remove the background of the nocturnal image in YIQ color model and then used the Otsu algorithm to identify the fruit from the stem base. 22,23 Chaivivatrakul and Dailey proposed a study of texture-based fruit detection for green fruits (bitter melon and pineapple) on plants in the field and recognized the green fruits in natural environment based on feature classification and region extration.…”
Section: Introductionmentioning
confidence: 99%