Description: Thresholding is the process of establishing a threshold for data analysis, which allows for the classification or segmentation of information based on specific criteria. This method is fundamental in various applications of image processing and anomaly detection, where the goal is to identify patterns or values that deviate from the expected. By defining a threshold, normal and anomalous data can be distinguished, facilitating automated decision-making. Thresholding can be binary, where data is divided into two categories, or multi-class, where multiple levels of classification can be established. This process is particularly relevant in the context of data science, artificial intelligence, and machine learning, as it helps optimize models and improve accuracy in detecting significant events. Additionally, thresholding can be applied in edge computing environments, where efficient and rapid data processing is required. In summary, thresholding is a key technique that helps simplify and structure large volumes of information, allowing for better interpretation and analysis of data.
