Publikationen

The comprehensive understanding of outdoor scenes is a necessary requirement for a wide variety of applications. For example, semantic segmentation enables applications such as outdoor robot navigation, image stylization, weather fore-casting, or climate monitoring. However, existing outdoor scene understanding models are often less reliable in challenging situations such as changing weather conditions or low light. Additionally, current approaches mainly focus on sky and ground separation and do not incorporate valuable information provided by weather conditions and cloud coverage. To overcome these challenges, we present SkyCloudNet, a multitask neural network architecture that extracts high-level attributes from the input image and utilizes them to improve the robustness of the network to environmental influences. Furthermore, it allows for the segmentation of cloud segments in natural outdoor images. While existing cloud segmentation approaches are limited to cropped sky-only images, our model enables the segmentation from entire landscape images with arbitrary resolution. Next to that, SkyCloudNet achieves state-of-the-art performance in environmental attribute estimation and sky segmentation. As cloud segmentation from natural images has not been addressed in previous literature, we also release the SkyCloud data set consisting of 350 high-resolution outdoor images with dense labels of sky and cloud segments.

This paper presents the initial concept for an evaluation framework to systematically evaluate productive teaming (PT). We consider PT as adaptive human-machine interactions between human users and augmented technical production systems. Also, human-to-human communication as part of a hybrid team with multiple human actors is considered, as well as human-human and human-machine communication for remote and mixed remote- and co-located teams. The evaluation comprises objective, performance-related success indicators, behavioral metadata, and measures of human experience. In particular, it considers affective, attentional and intentional states of human team members, their influence on interaction dynamics in the team, and researches appropriate strategies to satisfyingly adjust dysfunctional dynamics, using concepts of companion technology. The timescales under consideration span from seconds to several minutes, with selected studies targeting hour-long interactions and longer-term effects such as effort and fatigue. Two example PT scenarios will be discussed in more detail. To enable generalization and a systematic evaluation, the scenarios’ use cases will be decomposed into more general modules of interaction.

Psychological, and individual factors like group identity influence social presence in collaborative virtual settings. We investigated the impact of social identity labels, which reflect a user's nation and academic affiliation, on collaborative behavior. In an experiment, N=18 dyads played puzzle games while seeing or not seeing such labels. There were no significant differences regarding their social presence, trust, group identification or enjoyment. We argue that social identity labels in dyadic interactions do not change collaborative virtual behavior. We advance the field of sociotechnical applications by highlighting the relationship between psychological characteristics and cooperative behavior in collaborative virtual settings.

Integrating taste in AR/VR applications has various promising use cases - from social eating to the treatment of disorders. Despite many successful AR/VR applications that alter the taste of beverages and food, the relationship between olfaction, gustation, and vision during the process of multisensory integration (MSI) has not been fully explored yet. Thus, we present the results of a study in which participants were confronted with congruent and incongruent visual and olfactory stimuli while eating a tasteless food product in VR. We were interested (1) if participants integrate bi-modal congruent stimuli and (2) if vision guides MSI during congruent/incongruent conditions. Our results contain three main findings: First, and surprisingly, participants were not always able to detect congruent visual-olfactory stimuli when eating a portion of tasteless food. Second, when confronted with tri-modal incongruent cues, a majority of participants did not rely on any of the presented cues when forced to identify what they eat; this includes vision which has previously been shown to dominate MSI. Third, although research has shown that basic taste qualities like sweetness, saltiness, or sourness can be influenced by congruent cues, doing so with more complex flavors (e.g., zucchini or carrot) proved to be harder to achieve. We discuss our results in the context of multimodal integration, and within the domain of multisensory AR/VR. Our results are a necessary building block for future human-food interaction in XR that relies on smell, taste, and vision and are foundational for applied applications such as affective AR/VR.

How do Virtual Reality (VR) and Augmented Reality (AR) systems recognize the actions of users? How does a VR or AR system know where the user is? How can a system track objects in their movement? What are proven input devices for VR and AR that increase immersion in virtual or augmented worlds? What are the technical possibilities and limitations? Based on fundamentals, which explain terms like degrees of freedom, accuracy, repetition rates, latency and calibration, methods are considered that are used for continuous tracking or monitoring of objects. Frequently used input devices are presented and discussed. Finally, examples of special methods such as finger and eye tracking are discussed.