Deep reinforcement learning is a dynamic field that has been successfully applied in various engineering and scientific disciplines. However, like many areas of automated learning, it presents a significant challenge: understanding its models, which can hinder human trust in the decisions made by these algorithms. To address this issue, we conducted a focus group and interviews with experts in machine learning and reinforcement learning to gain insights into the perceptions and preferences surrounding interpretability techniques and tools in reinforcement learning systems. The discussion of the focus groups used an interactive dashboard to simplify the analysis of reinforcement learning environments. The focus group results showed many problems to tackle with the current approaches regarding RL agent training, like data manipulation, environment representation and reward misinterpretation. The focus group discussion also presented some key features of a visualization approach to interpretability in RL: contextual presentation of information, integration with existing pipelines, and ease of distribution.

Situated visualisation (SV), which embeds data representations directly within the context to which they refer, offers a paradigm for augmenting perception, understanding, and decision-making in real-world environments. Inspired by comic-books and previous work on visualisation, this article presents the relevance of SV in the form of superpowers. Each superpower is explained through the lens of augmented reality (AR). By mapping these capabilities, the article positions SV not just as a technical advancement, but as a new frontier in human-centred design, inviting researchers to rethink the role of visualisation in a world where data and reality increasingly coexist. Therefore, the paper also offers guidelines for its effective design and implementation. Finally, a structured evaluation questionnaire (the VIEWUS), specifically designed for AR-based Situated visualisation systems, is proposed. VIEWUS integrates key factors such as usability, engagement, situational awareness, visual fatigue, and workload, combining and adapting items from established instruments. It allows for a comprehensive assessment using a 5-point Likert scale and supports iterative improvement of SV systems based on user feedback.

This work explored the impact of expert representation during the (re)use of Augmented Reality (AR) instructions for remote assistance.A user study with sixteen participants compared three conditions: C1-no agent, C2-human agent, and C3-virtual agent, within a real-world maintenance scenario.Results indicate that reusing AR-instructions was well accepted, even when not perfectly aligned with the task context.While C2 reported higher levels of social presence and trust, C3 was considered the best option when reusable content was used and limited interaction was required.This allowed to focus more on the task and less on the agent representation.

Abstract Remote collaboration using eXtended Reality (XR) has been explored to establish a common ground between physically distributed individuals. To achieve usable and impactful solutions, it is paramount to understand how different content creation and display methods contribute to the work effort of each member (remote and on-site). Additionally, explore how various task scenarios influence the collaborative process, specifically examining how activities with unique characteristics and complexities affect remote work. Hence, the question arises, ’How do different display and interaction methods impact the collaborative process within specific task domains?’ In this paper, two user studies with 30 distinct participants each are described, focusing on different content creation support (Laptop Computer; Video Wall & Keyboard; Interactive Projector) and display methods (Hand-Held Device (HHD); HHD & Articulated Support; Head-Mounted Display (HMD)) for remote and on-site collaborators respectively, during scenarios of remote guidance. Plus, different physical tasks were considered for each study (Lego pieces assembly; Tangram puzzle assembly; Maintenance procedures; Resource management in a map; Training activity in a laboratory classroom), to understand which method stands out according to the characteristics of the said tasks. We report the results obtained, suggesting that for the remote part of the collaborative process, using a Video Wall & Keyboard appears to be more suitable for tasks associated with maintenance procedures, learning, and training activities. As for the on-site part, using HMD was considered the better option for maintenance, and learning tasks, closely followed by HHD & Articulated Support.

Abstract Reflective surfaces are notoriously difficult to detect and reconstruct, as they reflect light from surrounding objects. Thus, the quality of 3D reconstructions and the performance of downstream computer vision tasks, such as navigation, depth prediction, or object detection are severely impaired by the prevalence of mirrors in indoor environments. This paper proposes a novel reflection-aware method for 3D mirror segmentation and pose estimation, based on the reinterpretation of captured RGB-D data. It is a top-down approach, where information consensus, unsupervised learning, and ray casting are employed, formulating mirror pose estimation as an optimization problem. Experimental results show that the proposed approach significantly outperforms the existing benchmarks for RGB and RGB-D-based 3D mirror segmentation and pose estimation.

Inspection tasks in Industry 4.0 are crucial for ensuring product quality and operational efficiency. Yet human technicians often encounter challenges due to the complexity of modern machinery, the vast amount of data to be processed, and the need for rapid decision-making. These challenges underscore the necessity for innovative solutions that can provide technicians with enhanced data visualization and intuitive guidance. This work proposes a prototype using Pervasive Augmented Reality (AR) and Situated Visualization (SV) to support inspection procedures in industrial settings. This was created in collaboration with industrial partners following a Human-Centered Design (HCD) methodology. This process allowed to identify stakeholders' needs, and challenges, as well as define requirements to guide the development of the prototype. First impressions collected in laboratory environment are described, which helped improve an initial version of the prototype. Subsequently, a user study was conducted at the shop floor environment to evaluate usability, interaction, accuracy and acceptance, as well as collect feedback on how it can be improved to better support such context.

This paper presents an Augmented Reality (AR) application based on Situated Visualization (SV), aimed at increasing awareness of air pollution. Given that technological progress must be achieved through meticulous evaluation processes, the article also presents the assessment of the stated application. Therefore, this article begins by examining how AR applications are appraised. Building upon this review, and focusing particularly on those where SV is provided, a discussion is presented regarding the points that must be carefully considered when evaluating this type of AR application, as well as the factors that need to be measured, along with the most popular methods for evaluating these factors. However, it is noted that if a simultaneous evaluation of all the factors that matter to SV is intended, the number of questions to be asked could be significant. Therefore, a preliminary approach to enable such an appraisal is proposed and used in the mentioned application to demonstrate the merits of the proposed Questionnaire.

The use of Pervasive Augmented Reality (AR) in industrial settings, particularly in the context of Industry 4.0, is a growing area of research. Industry 4.0 is characterized by the integration of advanced technologies, including AR, to enable smart, connected, and autonomous systems in manufacturing and other industries. This study focuses on two systems that use Pervasive AR for logistics and data monitoring on assembly lines. Such systems were designed using cooperative Human-Centered Design (HCD) methodologies in collaboration with partners from the industry sector. We report lessons learned and guidelines on the development of AR systems for industrial environments. In addition, a user study based on a data monitoring laboratory realistic procedure with 8 participants is described. The study compares the usability and effectiveness of a dashboard on Pervasive AR and a web tablet. Overall, results emphasize that AR was better suited when requiring spatial awareness and interventions, while traditional web tablet was better for tasks that require more interaction with the content and faster data monitoring. These findings had implications for the design of industrial tools, such as prototype improvements and idealize new methods, which can guide future efforts to develop and deploy AR applications.

Augmented Reality (AR) is a key technology in the transition to Industry 4.0 and smart manufacturing, gaining reputation in a wide range of industrial fields One promising application is in another field of Industry 4.0, the data monitoring, where AR can be used to visualize and interact with complex and big data in real time, potentially improving the efficiency and accuracy of decision-making In this work, we propose a Pervasive AR tool for data monitoring, developed in collaboration with industry partners. An web application of the same data monitoring function is also created for comparison purposes A Human-Centered Design (HCD) methodology was used to identify the needs and requirements of industrial analysts, which led to the development of these systems Preliminary user studies, with 17 participants having distinct levels of expertise in industry, data analysis and computer science, were conducted to collect opinions and suggestions for improvements, as well as, quantitative data regarding the technologies considered A succeeding user study was then prepared, in which 12 participants used two conditions (C1 - Hands-free Pervasive AR tool for HMDs and C2 - Web tool for tablet devices) to fulfill a set of data monitoring tasks in an industry assembly line The result of these studies confirm the potential of Pervasive AR for data monitoring, as it engages users, promote environmental awareness, contextualizes data and allows fast self-localization.

In recent years, the market has seen the emergence of numerous affordable sensors, interaction devices, and displays, which have greatly facilitated the adoption of Virtual and Augmented Reality (VR/AR) across various applications. However, developing these applications requires a solid understanding of the field and specific technical skills, which are often lacking in current Computer Science and Engineering education programs. This work details an extended version from a Eurographics 2024 Education Paper, reporting a post-graduate-level course that has been taught for the past ten years to almost 200 students, across several Master’s programs. The course introduces students to the fundamental principles, methods, and tools of VR/AR. Its primary objective is to equip students with the knowledge necessary to understand, create, implement, and evaluate applications using these technologies. The paper provides insights into the course structure, key topics covered, assessment methods, as well as the devices and infrastructure utilized. It also includes an overview of various practical projects completed over the years. Among other reflections, we discuss the challenges of teaching this course, particularly due to the rapid evolution of the field, which necessitates constant updates to the curriculum. Finally, future perspectives for the course are outlined. • Description of main features of a VR/AR course attended by almost two hundred students. • illustration of course structure, key topics, assessment methods, devices and infrastructure utilized. • overview of various practical VR/AR projects completed over the years. • reflection on challenges of the past ten years of teaching the VR/AR course. • presentation of future perspectives for a VR/AR course.

Augmented Reality (AR) has the ability to display and interact with additional information aligned with the real world and to support context-driven visualization techniques. Situated Visualization (SV) is a method to represent data in a context that encompasses all the visualizations that change their appearance based on context. Therefore, since AR is typically used so that the user can visualize extra information regarding the surrounding real world environment, this technology is well suited for SV and presents high potential in many situations, as are the cases of collaborative/assistive tasks in the maintenance area and supporting decision-making. AR-based visualizations are commonly constrained to the users' unique egocentric view-point, which reduces their ability to explore all the richness of available information. This limitation is particularly evident in situations that require overviews of all information inside and out of the user's field of view (FOV). This article proposes multi-perspective renderings, transitional interfaces, and the use of multimodal information as methods to extend the user's egocentric viewpoint. The main idea is to coherently present and augment, in the same user's display, dynamic and static feeds of several real and virtual egocentric viewpoints, related to and relevant for the user's task.

This work proposed the use of 3D Reconstruction, Virtual Reality (VR), and a Large Display to enhance the education process of geosciences at the university level. This approach enables teachers to immerse themselves and interact with 3D virtual replicas of real-world locations. In turn, this is cast to a projector, allowing them to better explain specific concepts to an audience of students, which can acquire new knowledge and gain a deeper understanding. A user study was conducted with 20 participants (from geosciences and computer science engineering) to evaluate this proposal versus a traditional static image approach. We report the results obtained, suggesting the VR condition represents the predominant condition for a set of dimensions considered relevant for the tasks defined.

The region interpolation methods proposed in the moving objects databases literature impose restrictions that can have a significant impact on the representation of the evolution of moving regions, in particular, when a rotation occurs between two observations. In this paper, we propose a data model for moving regions that allows moving segments to rotate and change their length during their evolution between two observations and uses quadratic Bezier curves to define the trajectories of their endpoints. This introduces a new class of moving regions called rotating moving regions (rmregions). We present algorithms for operations involving rmregions and we propose a strategy to allow different interpolation methods to be used in the context of moving objects databases by approximating the interpolations they create using rmregions. We demonstrate our strategy using a reference implementation and compare results obtained when using the strategy presented here and the region interpolation methods and the spatiotemporal operations proposed in the state-of-the-art. Experimental results show that our strategy can be used to complement the region interpolation methods proposed in the moving objects databases literature.

This work describes an Augmented Reality Serious Game (ARSG) focused on facilitating the process of acquiring new skills in individuals with motor disabilities. In particular, this game aims to help them control a robotic wheelchair. A racing track was used as a game narrative, including restriction areas, static and dynamic objects, obstacles and various signs. A user study with 20 participants was conducted to compare different methods used to place virtual content on the real-world environment while the user interacts with the game and control the wheelchair in the physical space: C1 - motion tracking using cloud anchors; C2 - offline motion tracking. Results suggest condition C1 is more precise and robust, while condition C2 seems to be easier to configure.

Most efforts on Augmented Reality (AR) remote collaboration have been devoted to on-site individuals in need of assistance. Yet, since remote experts do not have access to the local environment, it is also important to explore how different methods affect the situation understanding and interaction. This work describes a user study with 16 participants aimed at comparing how remote individuals interact with standard (C1) and large-scale displays (C2) while providing assistance during a remote maintenance scenario. Results suggest condition C2 was preferred by the majority of participants, being considered more useful to analyze important details of the task context, as well as more easily create content.

Situated Visualization (SV), encompassing all the visualizations that change their appearance based on context, by considering the visualizations that are relevant to the physical context in which they are displayed [1], has been recognized as a method with potential in many situations, as is the case of supporting decision making [2]. Augmented and Mixed Reality (AR/MR) are well suited to assist in such scenarios, given its ability to display additional data regarding the real-world context and be supported by context-driven visualization techniques [3]. Though some perspectives on the SV model have been proposed, such as space, time, place, activity and community, an appropriate systematization, covering the main definitions and perspectives has yet to be established. Hence, there is an urge to obtain a more comprehensive description. The work presented in this paper characterizes the SV model, within the scope of AR/MR, shows a critical analysis of the existing knowledge, expanding the SV model and in turn hoping to elicit discussion within the research community.

Pervasive Augmented Reality (AR) has been explored to provide experiences that are continuous in space and time, while incorporating layers of contextual information. While most research efforts have been devoted to explore serious games, museums and smart environments, we argue that another interesting area of application is assistive production, in particular training novel workers by displaying instructions throughout the shop floor on what to do. This work describes a user study with 12 participants, aimed at comparing the use of three distinct conditions: C1 - Head-Mounted Display (HMD), C2 - Handheld Device (HHD) and C3 - Paper manual during a set of realistic training tasks, selected based on a collaboration with partners from the industry sector. Results suggest condition C1 was preferred by the majority of participants, being considered more useful to support the worker’s task as it is seems more efficient, hands-free and has a better tracking technology.

Although Mixed Reality (MR) has been explored to support scenarios of remote collaboration, the majority of studies reported in literature have focused on synchronous use-cases, where all team-members can interact in real-time. Hence, an opportunity exist to explore asynchronous situations, in which collaborative actions take place at different times. Despite the physical distance and different time zones, remote experts can generate MR-instructions to assist with emerging problems, suggesting to on-site collaborators where to act, and what to do. This work explored three distinct notification methods for such scenarios: C1 - visual cue; C2 - audio cue; C3 - tactile cue, aiming to comprehend which one captures the attention of on-site team-members easily. A user study with 12 participants was conducted to evaluate these conditions during an asynchronous scenario of remote maintenance. We report participants insights, suggesting tactile cues represent the predominant condition given the scenario context.

To support the nuances of collaborative work, many researchers have been exploring the field of Augmented Reality (AR), aiming to assist in co-located or remote scenarios. Solutions using AR allow taking advantage from seamless integration of virtual objects and real-world objects, thus providing collaborators with a shared understanding or common ground environment. However, most of the research efforts, so far, have been devoted to experiment with technology and mature methods to support its design and development. Therefore, it is now time to understand where the field stands and how well can it address collaborative work with AR, to better characterize and evaluate the collaboration process. In this article, we perform an analysis of the different dimensions that should be taken into account when analysing the contributions of AR to the collaborative work effort. Then, we bring these dimensions forward into a conceptual framework and propose an extended human-centered taxonomy for the categorization of the main features of Collaborative AR. Our goal is to foster harmonization of perspectives for the field, which may help create a common ground for systematization and discussion. We hope to influence and improve how research in this field is reported by providing a structured list of the defining characteristics. Finally, some examples of the use of the taxonomy are presented to show how it can serve to gather information for characterizing AR-supported collaborative work, and illustrate its potential as the grounds to elicit further studies.

Augmented Reality (AR) has been explored with the objective to assist in scenarios of co-located or remote collaboration. To help understand how well collaborative work can be addressed with AR, it is important to foster harmonization of perspectives and create a common ground for systematization and discussion. In this vein, understand relationships among existing dimensions of collaboration, as well as identify research opportunities, is of paramount importance and thus tools that allow visually exploring information associated with Collaborative AR may be most valuable. In this paper, we present a first effort towards the creation of such an interactive visualization tool for exploration and analysis of collaborative AR research. It allows visualize data of selected papers organized according to a human-centered taxonomy on collaborative AR. In order to get insights into whether the structure was understood and if the representation was clear and efficient to use, we evaluated the proposed tool through a user study with 40 participants. Results suggest the tool has potential towards the creation of a shared understanding and identification of existing patterns, trends and opportunities within the field of collaborative AR.

In many situations, performing maintenance procedures can be facilitated by providing on-site professionals with the possibility to request assistance from remote experts, thus performing the task collaboratively. Since remote experts don't have access to the local environment, they need additional mechanisms to provide instructions and guidance in such context. In this ongoing work, we present a Mixed Reality (MR) platform that explores the use of a shared model approach for collaboration scenarios. The platform uses predefined 3D models, which can be accessed by remote experts using Virtual Reality (VR) and by on-site users through Augmented Reality (AR). In addition, the remote expert can manipulate virtual models using different interaction methods and devices, as a mouse and a keyboard (computer), an interactive projector (touch) and controllers (Head Mounted Display). Our goal was to enrich his experience and improve collaborative awareness. We conducted a preliminary user study to evaluate collaboration, and different interaction methods, using as case study the assembly of Lego blocks.

Artificial Intelligent (AI) techniques, such as ma-chine learning (ML), have been making significant progress over the past decade. Many systems have been applied in sensitive tasks involving critical infrastructures which affect human well-being or health. Before deploying an AI system, it is necessary to validate its behavior and guarantee that it will continue to perform as expected when deployed in a real-world environment. For this reason, it is important to comprehend specific aspects of such systems. For example, understanding how neural networks produce final predictions remains a fundamental challenge. Existing work on interpreting neural network predictions for images via feature visualization often focuses on explaining predictions for neurons of one single convolutional layer. Not presenting a global perspective over the features learned by the model leads the user to miss the bigger picture. In this work we focus on providing a representation based on the structure of deep neural networks. It presents a visualization able to give the user a global perspective over the feature maps of a convolutional neural network (CNN) in a single image, revealing potential problems of the learning representations present in the network feature maps.

The FICA project - Tools for Identifying and Combating Dropout - started at the University of Aveiro in 2015 with the aim to help reduce and prevent dropouts and increase academic success among university students. Within the project a signicant amount of data is provided to different University stakeholders to monitor academic issues, however, these data are currently provided in large tables, a format difficult to analyze. In this paper, we present the main aspects of the data, the users and contexts of use. We also propose an approach to allow the visual and interactive exploration of the FICA project data to help monitor the path of the students and identify risk indicators and failure factors that can lead to critical situations such as dropout. A solution developed using the participatory design methodology is presented, detailing all stages of its creation process, from the requirements elicitation based on focus groups and interviews, design and prototype development in Power BI to its evaluation. Some suggestions for future work are also presented.

Mixed reality (MR) is well suited for situated visualization (SV), a method to represent data in a context, with potential in many situations. However, MR-based visualizations are commonly constrained to the users' single egocentric viewpoint reducing their ability to explore all the available information. This article discusses the main limitations and challenges of this approach based on the analysis of existing literature and identifies opportunities, as well as relevant aspects that must be considered when devising new methods aimed at overcoming those limitations.

Assembly procedures are a common task in several domains of application. Augmented Reality (AR) has been considered as having great potential in assisting users while performing such tasks. However, poor interaction design and lack of studies, often results in complex and hard to use AR systems. This paper considers three different interaction methods for assembly procedures (Touch gestures in a mobile device; Mobile Device movements; 3D Controllers and See-through HMD). It also describes a controlled experiment aimed at comparing acceptance and usability between these methods in an assembly task using Lego blocks. The main conclusions are that participants were faster using the 3D controllers and Video see-through HMD. Participants also preferred the HMD condition, even though some reported light symptoms of nausea, sickness and/or disorientation, probably due to limited resolution of the HMD cameras used in the video see-through setting and some latency issues. In addition, although some research claims that manipulation of virtual objects with movements of the mobile device can be considered as natural, this condition was the least preferred by the participants.

Research in general-purpose spatio-temporal databases has focused mainly on the development of data models and query languages. However, since spatio-temporal data are captured as snapshots, an important research question is how to compute and represent the spatial evolution of the data between observations in databases. Current methods impose constraints to ensure data integrity, but, in some cases, these constraints do not allow the methods to obtain a natural representation of the evolution of spatio-temporal phenomena over time. This paper discusses a different approach where morphing techniques are used to represent the evolution of spatio-temporal data in databases. First, the methods proposed in the spatio-temporal databases literature are presented and their main limitations are discussed with the help of illustrative examples. Then, the paper discusses the use of morphing techniques to handle spatio-temporal data, and the requirements and the challenges that must be investigated to allow the use of these techniques in databases. Finally, a set of examples is presented to compare the approaches investigated in this work. The need for benchmarking methodologies for spatio-temporal databases is also highlighted.

Spatio-temporal data on the evolution of real-world phenomena are normally acquired as snapshots in discrete time. The continuous evolution of a phenomenon between observations can be approximated using interpolation methods capable of generating deformable moving regions. Several region interpolation methods have been proposed in the spatio-temporal databases literature, each one with its own characteristics that can be more suited to represent the evolution of specific physical phenomena.

The decision-making process and the development of decision support systems (DSS) have been enhanced by a variety of methods originated from information science, cognitive psychology and artificial intelligence over the past years. Situated visualization (SV) is a method to present data representations in context. Its main characteristic is to display data representations near the data referent. As augmented reality (AR) is becoming more mature, affordable and widespread, using it as a tool for SV becomes feasible in several situations. In addition, it may provide a positive contribution to more effective and efficient decision-making, as the users have contextual, relevant and appropriate information to endorse their choices. As new challenges and opportunities arise, it is important to understand the relevance of intertwining these fields. Based on a literature analysis, this paper addresses and discusses current areas of application, benefits, challenges and opportunities of using SV through AR to visualize data in context and to support a decision-making process and its importance in future DSS.

Gamepads and 3D controllers are the main controllers used in most Virtual Environments. Despite being simple to use, these input devices have a number of limitations as fixed layout and difficulty to remember the mapping between buttons and functions. Mobile devices present interesting characteristics that might be valuable in immersive environments: more flexible interfaces, touchscreen combined with onboard sensors that allow new interaction and easy acceptance since these devices are used daily by most users. The work described in this article proposes a solution that uses mobile devices to interact with Immersive Virtual Environments for selection and navigation tasks. The proposed solution uses the mobile device camera to track the Head-Mounted-Display position and present a virtual representation of the mobile device screen; it was tested using an Immersive Virtual Museum as use case. Based on this prototype, a study was performed to compare controller based and mobile based interaction for navigation and selection showing that using mobile devices is viable in this context and offers interesting interaction opportunities.

Skull analysis is the main tool used in anthropology to identify several characteristics such as ancestry, sex, and variations between populations. Yet, skull analysis methods used by anthropologists still rely heavily on direct manipulation and measurement of the skulls producing significant inter and intra observer errors. Direct manipulation also involves risks of damaging the specimens while handling. In recent years computer methods for skull analysis that rely on 3D models of skulls acquired with a 3D scanner have been proposed. This approach gives the possibility to perform analysis otherwise not possible, simultaneously easing the overall process of skull analysis and reducing variability. This paper describes the development of automatic and semi-automatic methods for morphological analysis of 3D skull models through the extraction and classification of structures aiming to support the estimation of ancestry. Results with fifty specimens are presented

There are numerous technologies and tools to acquire data related to the evolution of spatial phenomena over time. These data are typically organized as sequences of 2D geometric shapes obtained from observations taken at different times. The transformation of such sequences of 2D geometric shapes into spatiotemporal data representations, which can be easily processed and interpreted, has the potential to enable novel applications in fields as diverse as environmental sciences, climate sciences, biology or medicine. This paper focuses on the representation of moving 2D geometric shapes acquired at discrete times using continuous models of time and space. Using morphing techniques based on compatible triangulations, issues regarding the representation of spatiotemporal data in databases, as well as the influence of different design strategies on the fidelity of the approximations with respect to the modelled phenomena, are investigated. An experimental study using synthetic and real data was performed. The findings show that the use of triangulation based interpolation is a promising approach, because it allows creating continuous spatiotemporal representations that are more realistic than those obtained using the solutions proposed in previous work. Open issues regarding the representation of spatiotemporal data in information systems are also highlighted.

Robotic soccer represents an innovative and appealing test bed for the most recent advances in multi-agent systems, artificial intelligence, perception and navigation and biped walking. The main sensorial element of a soccer robot must be its perception system, most of the times based on a digital camera, through which the robot analyses the surrounding world and performs accordingly. Up to this date, the validation of the vision system of a soccer robots can only be related to the way the robot and its team mates interpret the surroundings, relative to their owns. In this paper we propose an external monitoring vision system that can act as a ground truth system for the validations of the objects of interest of a robotic soccer game, mainly robots and ball. The system we present is made of two to four digital cameras, strategically positioned above the soccer field. We present preliminary results regarding the accuracy of the detection of a soccer ball, which proves that such a system can indeed be used as a provider for ground truth ball positions on the field during a robotic soccer game.

This work presents an ongoing work on a new approach to perform craniometric analysis based on contactless 3D modelling of skulls. Beside the acquisition process with a 3D range sensor and initial results in the semi-automatic detection of features in the skulls, we also present some results in the development of a 3D interactive interface that eases interaction for users with little experience on digital 3D manipulation. The final objective is to provide an easy to use 3D interface to allow semi-automatic detection of features in skulls. It is our belief that this system might be the first step towards a new methodology for craniometric analysis that can solve several of the current problems such as repeatability, wide access to skull information or bone damage during measurements.

Visualizing large family structures is becoming increasingly important, as more genealogical data becomes available. A space-filling h-tree layout pedigree has been recently proposed to make better use of the available space than traditional representations. In a previous paper we applauded the technique's usage of available space but remarked that it makes generation identification difficult and does not allow navigating to descendants of represented individuals. A set of extensions was proposed to help overcome these limitations and a preliminary evaluation suggested that those extensions enhance the original technique. This paper presents a more thorough evaluation carried out to assess if and how the proposed extensions improve the original h-tree layout pedigree technique. Results suggest that these extensions improve user performance on some tasks, effectively provide new functionality, and generally enhance user experience.

Geophysical data represent subsoil structure in a specific area and can be used to extract subsoil information for various purposes. In this work we used this data type to detect anomalies/contamination in the subsoil. Our case study was based on data acquired around a landfill and the main objective is identifying contaminated areas as a result of leakage in landfill. This involves the application of statistical methods to detect anomalous values taking into account the whole data set, subdividing it in sublevels in relation to the surface, instead of using a single threshold (as usual). This work combines in the same software package the anomaly statistical analysis and several 3D representations of the results to validate and also helps understanding the final results of the analysis. Given that the original data used in the analysis, resistivity sections, is normally very sparse, a kriging geostatistical process was used to interpolate data in order to provide a volumetric representation of the subsoil in the area, providing a continuous spatial visualization.

The constant growth of available genealogical information has encouraged the research of visualization techniques capable of representing the corresponding large amount of data. An H-Tree Layout has been recently proposed to represent pedigree data as a way to overcome some of the limitations of traditional representations. However, this new method has its own limitations which may hinder its adoption. In this paper, we propose some enhancements to the H-Tree Layout pedigree visualization method in order to overcome some of the identified limitations. An implementation of the proposed enhancements and results of a preliminary evaluation are also provided.

This paper describes an attempt to explore possible interrelations between today's online communities, trust and their online social patterns. It reports on a study in progress to provide a deeper understanding of how and at what level such relations affect the development of learning social contexts, i.e contexts that represent individuals needs, group commitments, their responsibilities, goals and loyalties towards the need to progress, to bound and gain knowledge or become skilled. The paper starts by discussing this study's rational, and proceeds on discussing alternative ways of depicting and understanding how our attitudes regarding online relationships can be related with trust. It terminates with a briefly presentation and discussion of the early results the ongoing work.

The work presented in this paper aims at exploring new ways of integrating, visualizing and interacting with geotechnical and geophysical data that may be more rich and interactive than those offered by most current Geographic Information Systems (GIS). Some visualization techniques enabling simultaneous visualization of the several data types available in our case study are proposed. Moreover, methods were developed to guide experts while defining layers and other relevant geological structures. The work is still in an early stage and is main goal has been assessing the validity and adequacy of the proposed techniques to the specific geotechnical and geophysical data under consideration.

Facility location and vehicle routing are amongst the most important logistic decisions in today's organizations. These aspects are intertwined and, in some cases, should be addressed in an integrated way (giving rise to the location-routing approach). A decision support tool that can make easier the visualization (and editing) of information regarding these problems is becoming increasingly important as: it enables to further understand the problem at hand; and, at the same time, it fosters better communication of the decisions in a way easier to understand by the general public. This paper presents some concepts for information visualization on the problems arisen by the aforementioned decisions, which have been incorporated in a decision support tool and tested using usability evaluation methods.

This paper presents a user study performed to compare the usability of the Wiimote as an input device to visualize information and navigate in Google Earth using two different configurations. This study had the collaboration of 15 participants which performed a set of tasks using the Wiimote as an input device while the image was projected on a common projection screen, as well as a mouse on a desktop. Results show that most users clearly preferred one of the Wiimote configurations over the other, and over the mouse; moreover, they had better performances using the preferred configuration, and found it easier to use.

This paper presents a process combining range and intensity based techniques, in order to get better 3D models than those obtained using these techniques separately. The procedure needs an initial estimation for internal and external camera parameters for two or more intensity images. The technique uses passive triangulation to refine initial camera calibrations and ensure a good registration of range and video data sets. Afterwards, corresponding points from the intensity images are triangulated and introduced in the original range cloud of points. The objective is to complete the models in areas where data is missing or to increase the resolution in areas of high interest and 3D contents.

In this paper, we present techniques combining intensity and range data to achieve more realistic three-dimensional models from real world scenes. The principal novelty is the use of the intensity information not only for texturing the final 3D model but also to improve the segmentation of the range data. The technique registers video and range, i.e., the camera parameters are computed in order to get both data in the same reference frame. Edge extraction techniques are then applied to select corresponding edges and a line-straightening algorithm is used to correct the 3D model. Results are presented for both indoor and outdoor environments.