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首页> 外文会议>IFIP TC 5/SIG 5.1 conference on computer and computing technologies in agriculture;CCTA 2011 >A Separating Method of Adjacent Apples Based on Machine Vision and Chain Code Information
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A Separating Method of Adjacent Apples Based on Machine Vision and Chain Code Information

机译：基于机器视觉和链码信息的相邻苹果分离方法

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摘要

Fruit location is an important parameter for apple harvesting robot to conduct picking task. However, it is difficult to obtain coordinates of each apple under natural conditions. One of the major challenges is detecting adjacent fruits accurately. Previous studies for adjacent detection have shortcomings such as vast computation, difficulty in implementation and over-segmentation. In this paper, we propose a novel and effective separating method for adjacent apples recognition based on chain code information and obtain the centroid coordinates of each fruit. Firstly, those valid regions of fruit are extracted by pre-processing the initial image. Secondly, chain code information is obtained by following the contour of extracted regions. Thirdly, through observing the changing law of chain code difference and adopting local optimum principle, concave points are found. Finally, the best point pairs are determined with different matching principles, and those adjacent apples are separated exactly. The experimental results show that the average rate of successful separation is greater than 91.2% with the proposed method, which can meet the requirements of applications in harvesting robots.
机译：水果的位置是苹果收获机器人进行采摘任务的重要参数。但是，在自然条件下很难获得每个苹果的坐标。主要挑战之一是准确检测相邻水果。先前对相邻检测的研究存在诸如大量计算，难以实施和过度分割等缺点。在本文中，我们提出了一种新的有效的基于链码信息的相邻苹果识别的分离方法，并获得了每种水果的质心坐标。首先，通过对初始图像进行预处理来提取那些有效的水果区域。其次，通过遵循提取区域的轮廓来获得链码信息。第三，通过观察链码差的变化规律，采用局部最优原理，找到凹点。最后，用不同的匹配原理确定最佳点对，并精确分离那些相邻的苹果。实验结果表明，所提方法的平均分离成功率大于91.2％，可以满足收割机器人应用的要求。

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