In recent years, various studies have focused on developing and refining image-processing pipelines for RGB-IR imagery. In this paper, we address a central bottleneck in such pipelines: the removal of infrared (IR) contamination from the RGB channels. The IR crosstalk leakage leads to colour degradation, desaturation, and hue shifts, especially under high IR illumination. We study the spatial characteristics of this crosstalk in controlled laboratory conditions across diverse illumination intensities, and introduce a lightweight U-Net-based convolutional model with a task-specific loss that prioritizes spatially challenging, IR-contaminated pixels. Building on the standard linear contamination hypothesis, our model predicts per-channel coefficients that can integrate with conventional ISP stages, while adapting the correction to spatial outliers such as radial vignetting and edge artefacts.
In remote deception detection scenarios, where data are scarce, modalities are restricted to audio and video, and facial cues are subtle and highly individual, modeling expressions via Action Units (AUs) offers a focused and interpretable approach. This paper investigates why many methods still rely on OpenFace for AU prediction and compares the performance of CNN-based AU prediction across different scenarios. For this study, we acquired a new dataset collected in a controlled setting where participants were instructed to deceive during mock online sales meetings. For comparison, we also use the Real-life Trial dataset. We first demonstrate that our CNN-based approach outperforms OpenFace on the EmotioNet AU dataset. When used as a feature set for deception detection, the CNN approach also performs better on the challenging, in-the-wild data of the Real-life Trial. However, OpenFace, that covers also AUs not included within EmotioNet, performs better in the semi-controlled mock sales meeting scenario.
Interpretability is essential in security-critical applications such as Morph Attack Detection (MAD), where model transparency supports trust, auditability, and bias assessment. This work investigates the use of occlusion mapping to explain the decision process of a Vision Transformer (ViT)-based MAD system combined with a Support Vector Machine (SVM) classifier. Two complementary occlusion strategies were explored: landmark-based occlusion, targeting semantically relevant facial regions (eyes, nose, mouth), and grid-based occlusion, dividing images into spatial cells. Experiments were conducted on cropped and aligned versions of the FRLL-Morphs and FFHQ-Morphs datasets. The results, visualized through heatmaps, reveal that the model consistently assigns highest importance to the nose and mouth regions, with strong sensitivity to depth transition areas prone to morphing artifacts. These findings provide actionable insights for improving the robustness and bias mitigation of MAD systems.
As Virtual Reality technologies become increasingly accessible, their application within the educational and cultural heritage sectors is rapidly advancing. This study presents a multiuser Social Extended Reality application designed for guided virtual tours of a historically reconstructed site: the presbytery of the Church of Santa Maria Antiqua in Rome. The experience integrates spatial audio, avatars, and multilingual narration to promote immersive and social learning. Fifty-two participants took part in the tour in groups of two or three, accompanied by a guide. Using a mixed methods approach, we evaluated simulator sickness, usability, social presence, learning outcomes, and spatial behavior based on proxemics theory. The results show that the users maintained interpersonal distances consistent with the social zone, aligning with real-world patterns. The comprehension scores did not vary significantly with language familiarity, suggesting that visual and spatial cues effectively supported understanding. Overall, the application was well received, with favorable usability and presence ratings, highlighting the potential of multilingual Social Extended Reality as a tool for accessible and engaging cultural education.
The usage of advanced computational methods in clinical diagnostic procedures is a long-studied field. In this area, deep learning models show the highest degree of performance, but they require large amounts of annotated training data to achieve that performance. However, the process of annotating biomedical data requires time and is expensive since it must be done by medical professionals. For example, synchrotron X-ray tomographic imaging has been used for endomyocardial biopsy (EMB) sample scanning, which greatly increases resolution and clarity of acquired samples compared to other tomographic imaging techniques. The problem is that in this case, the amount of annotated data is several orders of magnitude smaller than the amount of raw data. This is especially evident in the area of image segmentation, where class annotations need to be accurately done for each pixel in the image, greatly increasing the annotation effort needed. Recent advances in foundational segmentation models offer us a way to partially automate the segmentation using limited oversight, allowing for the creation of partially supervised segmentation masks, especially in volumetric data such as CT or synchrotron X-ray images. In this work, we present a method for robust tissue segmentation in synchrotron X-ray images of endomyocardial biopsy samples in the case of a sparse set of tissue annotations. We employ a large foundational segmentation model from the Segment Anything 2 family to create a set of annotations on a small subset of tissues, using only limited supervision on a few key frames. We then use those frames to propagate annotations through a tissue volume, allowing us to get a large set of initial noisy annotations. These annotations are then used to train a smaller deep segmentation model. The final results are compared against the small set of annotations done by medical professionals to determine model performance. These results show that this approach greatly improves performance when compared to traditional image processing techniques or to models trained only on a small set of professionally annotated images.
