Image segmentation is an important issue in many industrial processes, with high potential to enhance the manufacturing process derived from raw material imaging. For example, metal phases contained in microstructures yield information on the physical properties of the steel. Existing prior literature has been devoted to develop specific computer vision techniques able to tackle a single problem involving a particular type of metallographic image. However, the field lacks a comprehensive tutorial on the different types of techniques, methodologies, their generalizations and the algorithms that can be applied in each scenario. This paper aims to fill this gap. First, the typologies of computer vision techniques to perform the segmentation of metallographic images are reviewed and categorized in a taxonomy. Second, the potential utilization of pixel similarity is discussed by introducing novel deep learning-based ensemble techniques that exploit this information. Third, a thorough comparison of the reviewed techniques is carried out in two openly available real-world datasets, one of them being a newly published dataset directly provided by ArcelorMittal, which opens up the discussion on the strengths and weaknesses of each technique and the appropriate application framework for each one. Finally, the open challenges in the topic are discussed, aiming to provide guidance in future research to cover the existing gaps.

图像分割是许多工业过程中的一个重要问题，具有增强源自原材料成像的制造过程的巨大潜力。例如，微观结构中包含的金属相会产生有关钢的物理性能的信息。现有的先前文献致力于开发能够解决涉及特定类型金相图像的单个问题的特定计算机视觉技术。然而，该领域缺乏关于不同类型的技术、方法、它们的概括以及可应用于每个场景的算法的综合教程。本文旨在填补这一空白。首先，在分类法中审查和分类用于执行金相图像分割的计算机视觉技术的类型。第二，通过引入利用此信息的新型基于深度学习的集成技术，讨论了像素相似性的潜在利用。第三，在两个公开可用的现实世界数据集中对审查的技术进行了彻底的比较，其中一个是由 ArcelorMittal 直接提供的新发布的数据集，这开启了对每种技术的优缺点和适当的讨论每个应用程序框架。最后，讨论了该主题的开放挑战，旨在为未来的研究提供指导，以弥补现有的差距。其中之一是由 ArcelorMittal 直接提供的新发布的数据集，它开启了对每种技术的优缺点以及适用于每种技术的应用框架的讨论。最后，讨论了该主题的开放挑战，旨在为未来的研究提供指导，以弥补现有的差距。其中之一是由 ArcelorMittal 直接提供的新发布的数据集，它开启了对每种技术的优缺点以及每种技术的适当应用框架的讨论。最后，讨论了该主题的开放挑战，旨在为未来的研究提供指导，以弥补现有的差距。