Thinning is a frequently used technique capable of producing all kinds of skeleton-like shape features in a topology-preserving way. It is an iterative object reduction: some border points of binary objects that satisfy some topological and geometrical constraints are deleted, and the entire process is repeated until stability is reached. In the conventional implementation of thinning algorithms, the deletability of all border points in the actual picture is to be investigated. That is why, we introduced the concept of k-attempt thinning (\(k\ge 1\)) in our previous work (presented in the 20th International Workshop on Combinatorial Image Analysis, IWCIA 2020). In the case of a k-attempt algorithm, if a border point ‘survives’ at least k successive iterations, it is ‘immortal’ (i.e., it cannot be deleted later). In this paper, we give a computationally efficient implementation scheme for 1-attempt thinning, and a 1-attempt 2D parallel thinning algorithm is reported. The advantage of the new implementation scheme over the conventional one is also illustrated.

细化是一种常用技术，能够以拓扑保留的方式生成各种骨架状的形状特征。这是一个迭代的对象简化：删除满足某些拓扑和几何约束的二元对象的某些边界点，并重复整个过程，直到达到稳定性为止。在稀疏算法的常规实现中，将研究实际图片中所有边界点的可删除性。因此，我们在之前的工作（在IWCIA 2020第20届国际组合图像分析研讨会上提出）中引入了k尝试稀疏（\（k \ ge 1 \））的概念。在采用k尝试算法的情况下，如果边界点“幸存”了至少k连续的迭代，它是“不朽的”（即，以后不能删除）。在本文中，我们给出了一种适用于1尝试细化的高效计算实现方案，并报告了1尝试2D并行细化算法。还说明了新实施方案相对于传统方案的优势。