In automated vehicles, beeps are widely used as alarms and feedback. However, as automation advances, there is a need to explore subtler, contextually sound-based notifications for non-urgent situations. While auditory interfaces for take-over requests have been studied, limited attention has been given to using soundscapes for such alerts. This paper designed and evaluated soundscapes using existing driving-related sounds – amplified road noise and/or dimmed background music – for scheduled take-over situations. A driving simulator study showed that these soundscapes enhanced reaction time, situation awareness, and acceptance without causing annoyance. Particularly, the combined condition (music dimming and road noise amplifying) supported higher driver awareness and responsiveness. These findings suggest that soundscapes can offer safer, more intuitive take-over alerts by embedding information into familiar audio cues. This study contributes to developing soundscapes as novel alert mechanisms that integrate seamlessly with the driving environment to enhance both safety and user experience in automated vehicles.

Correlation clustering (CC) offers an effective approach to analyze scalar field ensembles by detecting correlated regions and consistent structures, enabling the extraction of meaningful patterns. However, existing CC methods are computationally expensive, making them impractical for both interactive analysis and large-scale scalar fields. We introduce the Local-to-Global Correlation Clustering (LoGCC) framework, which accelerates pivot-based CC by leveraging the spatial structure of scalar fields and the weak transitivity of correlation. LoGCC operates in two stages: a local step that uses the neighborhood graph of the scalar field's spatial domain to build highly correlated local clusters, and a global step that merges them into global clusters. We implement the LoGCC framework for two well-known pivot-based CC methods, Pivot and CN-Pivot, demonstrating its generality. Our evaluation using synthetic and real-world meteorological and medical image segmentation datasets shows that LoGCC achieves speedups-up to 15 × for Pivot and 200 × for CN-Pivot-and improved scalability to larger scalar fields, while maintaining cluster quality. These contributions broaden the applicability of correlation clustering in large-scale and interactive analysis settings.

Abstract The contour depth methodology enables non‐parametric summarization of contour ensembles by extracting their representatives, confidence bands, and outliers for visualization (via contour boxplots) and robust downstream procedures. We address two shortcomings of these methods. Firstly, we significantly expedite the computation and recomputation of Inclusion Depth (ID), introducing a linear‐time algorithm for epsilon ID, a variant used for handling ensembles with contours with multiple intersections. We also present the inclusion matrix, which contains the pairwise inclusion relationships between contours, and leverage it to accelerate the recomputation of ID. Secondly, extending beyond the single distribution assumption, we present the Relative Depth (ReD), a generalization of contour depth for ensembles with multiple modes. Building upon the linear‐time eID, we introduce CDclust, a clustering algorithm that untangles ensemble modes of variation by optimizing ReD. Synthetic and real datasets from medical image segmentation and meteorological forecasting showcase the speed advantages, illustrate the use case of progressive depth computation and enable non‐parametric multimodal analysis. To promote research and adoption, we offer the contour‐depth Python package.

We present a systematic approach for training and testing structural texture similarity metrics (STSIMs) so that they can be used to exploit texture redundancy for structurally lossless image compression. The training and testing is based on a set of image distortions that reflect the characteristics of the perturbations present in natural texture images. We conduct empirical studies to determine the perceived similarity scale across all pairs of original and distorted textures. We then introduce a data-driven approach for training the Mahalanobis formulation of STSIM based on the resulting annotated texture pairs. Experimental results demonstrate that training results in significant improvements in metric performance. We also show that the performance of the trained STSIM metrics is competitive with state of the art metrics based on convolutional neural networks, at substantially lower computational cost.

Ensembles of contours arise in various applications like simulation, computer-aided design, and semantic segmentation. Uncovering ensemble patterns and analyzing individual members is a challenging task that suffers from clutter. Ensemble statistical summarization can alleviate this issue by permitting analyzing ensembles' distributional components like the mean and median, confidence intervals, and outliers. Contour boxplots, powered by Contour Band Depth (CBD), are a popular non-parametric ensemble summarization method that benefits from CBD's generality, robustness, and theoretical properties. In this work, we introduce Inclusion Depth (ID), a new notion of contour depth with three defining characteristics. First, ID is a generalization of functional Half-Region Depth, which offers several theoretical guarantees. Second, ID relies on a simple principle: the inside/outside relationships between contours. This facilitates implementing ID and understanding its results. Third, the computational complexity of ID scales quadratically in the number of members of the ensemble, improving CBD's cubic complexity. This also in practice speeds up the computation enabling the use of ID for exploring large contour ensembles or in contexts requiring multiple depth evaluations like clustering. In a series of experiments on synthetic data and case studies with meteorological and segmentation data, we evaluate ID's performance and demonstrate its capabilities for the visual analysis of contour ensembles.

We focus on the evaluation of texture similarity metrics for structurally lossless or nearly structurally lossless image compression. By structurally lossless we mean that the original and compressed images, while they may have visible differences in a side-by-side comparison, they have similar quality so that one cannot tell which is the original. This is particularly important for textured regions, which can have significant point-by-point differences, even though to the human eye they appear to be the same. As in traditional metrics, texture similarity metrics are expected to provide a monotonic relationship between measured and perceived distortion. To evaluate metric performance according to this criterion, we introduce a systematic approach for generating synthetic texture distortions that model variations that occur in natural textures. Based on such distortions, we conducted subjective experiments with a variety of original texture images and different types and degrees of distortions. Our results indicate that recently proposed structural texture similarity metrics provide the best performance.