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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. [Display omitted] • We create a metallography dataset from additive manufacturing of steels (MetalDAM). • We provide an updated taxonomy of segmentation methods. • We propose a new DL-based ensemble specialized in the semantic segmentation task. • We compare state-of-the-art models and the new ensembles with UHCS and MetalDAM. • We present a thorough analysis of the current difficulties and challenges. [ABSTRACT FROM AUTHOR]

Anomaly detection aims to identify observations that differ significantly from the majority of the data. Time series, which are data with a temporal component, is often used for anomaly detection. Identifying anomalies is not perfect and may produce many false positives, which labels standard data as anomalous. In this context, false positive mitigation is the task of reducing the number of false positives tagged by the anomaly detector, and thus both problems are closely linked. Moreover, current techniques for false positive mitigation are ad-hoc solutions for specific data sets. In this paper, we propose a novel two-stage methodology for Multivariate Anomaly Detection for Time Series and False Positive Mitigation, namely F A D F P M methodology, which creates the fusion of two learning models. The first stage is a multivariate anomaly detection stage. The second stage consists of training a new classifier on the false and true positives from the anomaly detector, which refines the observations labeled as anomalous by the anomaly detector to obtain more accurate and higher-quality results. Experiments using two benchmark data sets, as well as a real-world case study have shown the performance and validity of the proposal. • Proposed methodology decreases impact from FPs in anomaly detection. • A thorough comparison with latest SOA methods is performed. • We also provide a series of hints for applying the methodology. • High sensitivity methods are more benefited from the proposal. • A real-world case of study provided by ArcelorMIttal is analyzed. [ABSTRACT FROM AUTHOR]