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首页> 外文期刊>Multimedia Tools and Applications >Recognition and localization of occluded apples using K-means clustering algorithm and convex hull theory: a comparison
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Recognition and localization of occluded apples using K-means clustering algorithm and convex hull theory: a comparison

机译：基于K-means聚类算法和凸包理论的遮挡苹果识别与定位：比较

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摘要

For apple harvesting robot, it is difficult to acquire the coordinates of occluded apples accurately in natural scenes, which is important in implementing picking tasks. In this paper, a method on automatic recognition and localization of occluded apples was proposed. Firstly, an apple recognition algorithm based on K-means clustering theory was described. Secondly, convex hull information which was obtained by following the contours of extracted apple regions was used to extract the real apple edges. Finally, three points from these real edges were selected to estimate the centers and radius of apples. This algorithm was tested and compared with traditional Hough transform method (HT method) and contour curvature method (CC method) and 125 apple images were used to test the effectiveness of these methods. Four parameters including Segmentation Error (SE), False Positive Rate (FPR), False Negative Rate (FNR) and Overlap Index (OI) were used to evaluate the performance of these methods. Experimental results showed that SE of the presented method was decreased by 14.399 and 30.782 % when compared to CC method and HT method respectively, FPR by 7.234 and 11.728 % and OI was increased by 18.644 and 30.938 %. FNR of the proposed method was 0.912 % lower than CC method, while it was 5.869 % higher than HT method. The experimental results indicated that the proposed method could get much better localization rate than Hough transform method and contour curvature method, thus it could be concluded that the algorithm is an efficient means for the recognition and localization of occluded apples.
机译：对于苹果收割机器人来说，很难在自然场景中准确地获取被遮挡的苹果的坐标，这对于执行采摘任务非常重要。本文提出了一种对被遮挡的苹果进行自动识别和定位的方法。首先，描述了一种基于K均值聚类理论的苹果识别算法。其次，通过遵循提取的苹果区域的轮廓获得的凸包信息用于提取真实的苹果边缘。最后，从这些真实边缘中选择了三个点来估计苹果的中心和半径。对算法进行了测试，并与传统的Hough变换方法（HT方法）和轮廓曲率方法（CC方法）进行了比较，并使用125张苹果图像测试了这些算法的有效性。四个参数包括分割误差（SE），误报率（FPR），误报率（FNR）和重叠指数（OI）被用来评估这些方法的性能。实验结果表明，与CC法和HT法相比，该方法的SE分别降低了14.399％和30.782％，FPR分别提高了7.234和11.728％，OI分别提高了18.644和30.938％。该方法的FNR比CC方法低0.912％，而比HT方法高5.869％。实验结果表明，与霍夫变换法和轮廓曲率法相比，所提方法具有更好的定位率，可以认为该算法是一种有效的遮挡苹果识别和定位方法。

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