Outlier detection is a critical task in time series analysis, essential to maintaining data quality and allowing for accurate subsequent analysis. The Hampel filter, a decision filter that replaces outliers in a data window with the median, is widely used for outlier detection in time series due to its simplicity and effectiveness. While effective, its computational complexity, primarily due to the calculation of the Median Absolute Deviation (MAD), poses limitations for large-scale and real-time applications. This paper proposes a novel Hampel filter variant that replaces the MAD with an original estimator (mMAD) that retains statistical robustness but is computationally more efficient. This reduces the filter’s computational complexity from $O(N·wlogw)$ to $O(N·w)$, where N is the data length and w the window size. The proposed variant significantly lowers processing time and resource consumption, making it especially suitable for large-scale and real-time data processing while preserving robust outlier detection performance.

In industrial imaging, accurately detecting and distinguishing surface defects from noise is critical and challenging, particularly in complex environments with noisy data. This paper presents a hybrid framework that integrates both statistical feature selection and classification techniques to improve defect detection accuracy while minimizing false positives. The motivation of the system is based on the generation of scalar scores that represent the likelihood that a region of interest (ROI) is classified as a defect or noise. We present around 55 distinguished features that are extracted from industrial images, which are then analyzed using statistical methods such as Fisher separation, chi-squared test, and variance analysis. These techniques identify the most discriminative features, focusing on maximizing the separation between true defects and noise. Fisher's criterion ensures robust, real-time performance for automated systems. This statistical framework opens up multiple avenues for application, functioning as a standalone assessment module or as an a posteriori enhancement to machine learning classifiers. The framework can be implemented as a black-box module that applies to existing classifiers, providing an adaptable layer of quality control and optimizing predictions by leveraging intuitive feature extraction strategies, emphasizing the rationale behind feature significance and the statistical rigor of feature selection. By integrating these methods with flexible machine learning applications, the proposed framework improves detection accuracy and reduces false positives and misclassifications, especially in complex, noisy environments.

Noise contamination in three-dimensional data remains a persistent challenge in reconstruction pipelines, with critical implications for emerging technologies such as autonomous navigation, cultural heritage preservation, and industrial inspection. This work presents an innovative three-axis taxonomy that unifies the classification and mitigation of noise in 3D point clouds. Our framework systematically categorizes noise sources into: (1) statistical models, (2) scene interferences, and (3) reconstruction imperfections. Through critical analysis, we reveal fundamental limitations, practical challenges, and adaptive strategies employed in specific scenarios. Among the unresolved challenges are real-time processing of dynamic scenes, separation of overlapping noise types, and biases in learning models toward specific sensor geometries. This research consolidates fragmented knowledge into actionable guidelines, equipping professionals to design robust 3D systems and guiding researchers toward impactful innovations in noise-adaptive sensing.

Noise contamination in three-dimensional data remains a persistent challenge in reconstruction and measurement pipelines, with critical implications for dimensional inspection and industrial metrology, as well as for autonomous navigation and cultural heritage documentation. Whenever a point cloud is used to measure rather than merely to visualize, the noise it carries propagates directly into the reported dimension and into its associated uncertainty. This work presents an innovative three-axis taxonomy that unifies the classification and mitigation of noise in 3D point clouds. Our framework systematically categorizes noise sources into (1) statistical models, (2) scene interferences, and (3) reconstruction imperfections, and it is designed so that any newly encountered noise type can be assigned consistently by means of three explicit criteria (origin, modeling tractability and spatio-temporal dependency). Through theoretical analysis along these three axes, we discuss the fundamental limitations, practical challenges, and adaptive strategies of each method and summarize the resulting cross-category applicability in a comparative table. Among the unresolved challenges are real-time processing of dynamic scenes, separation of overlapping noise types, and biases in learning models toward specific sensor geometries. We further argue that, in a measurement context, a denoising operator must be assessed not only by the noise it suppresses but also by the systematic surface displacement—the bias—it introduces, which aggregate distance metrics tend to conceal. This research consolidates fragmented knowledge into actionable guidelines, equipping metrology and inspection practitioners to design robust 3D measurement systems and guiding researchers toward impactful innovations in noise-adaptive sensing.