Connected Components Labeling is an essential step of many Image Processing and Computer Vision tasks. Since the first proposal of a labeling algorithm, which dates back to the sixties, many approaches have optimized the computational load needed to label an image. In particular, the use of decision forests and state prediction have recently appeared as valuable strategies to improve performance. However, due to the overhead of the manual construction of prediction states and the size of the resulting machine code, the application of these strategies has been restricted to small masks, thus ignoring the benefit of using a block-based approach. In this paper, we combine a block-based mask with state prediction and code compression: the resulting algorithm is modeled as a Directed Rooted Acyclic Graph with multiple entry points, which is automatically generated without manual intervention. When tested on synthetic and real datasets, in comparison with optimized implementations of state-of-the-art algorithms, the proposed approach shows superior performance, surpassing the results obtained by all compared approaches in all settings.

中文翻译：

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意大利面条标签：基于块的连接组件标签的有向无环图。

连接组件的标签是许多图像处理和计算机视觉任务的重要步骤。自可以追溯到六十年代的标记算法的第一个提议以来，许多方法已经优化了标记图像所需的计算量。特别是，决策森林和状态预测的使用最近已成为提高性能的有价值的策略。但是，由于人工构建预测状态的开销以及生成的机器代码的大小，因此这些策略的应用仅限于较小的掩码，因此忽略了使用基于块的方法的好处。在本文中，我们将基于块的掩码与状态预测和代码压缩相结合：将生成的算法建模为具有多个入口点的有向有向无环图，它是自动生成的，无需人工干预。当在合成数据集和真实数据集上进行测试时，与最新算法的优化实现相比，该方法具有更好的性能，超过了所有环境中所有比较方法所获得的结果。