Object segmentation is a common task in bin-picking. Region-growing-based methods have been proven to be applicable for ordinary tasks, but they are not suitable for closely adjacent and stacked scenes. In this paper, we propose an inward-region-growing-based accurate partitioning method for bin-picking. A boundary bud generation algorithm is proposed for detecting the boundary initial points of closely adjacent objects for region growing. Then, a simplified growing algorithm, namely, the oriented unrestrained growing algorithm, is proposed for limiting the growing direction to the inward direction and accelerating the growing process. These experimental results demonstrate that the proposed method can achieve higher accuracy and speed than existing methods, especially in closely adjacent scenes.

中文翻译：

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基于向内区域增长的紧密堆叠对象的精确分区，用于垃圾箱拣选

对象分割是垃圾桶拣选中的常见任务。已经证明基于区域增长的方法适用于普通任务，但不适用于紧密相邻和堆叠的场景。在本文中，我们提出了一种基于向内区域增长的精确分拣方法。提出了一种边界芽生成算法，用于检测紧邻的物体的边界起始点以进行区域生长。然后，提出了一种简化的生长算法，即定向无约束生长算法，用于将生长方向限制为向内方向并加速生长过程。这些实验结果表明，与现有方法相比，该方法可以达到更高的精度和速度，尤其是在邻近场景中。