Investigating Search Among Physical and Virtual Objects Under Different Lighting Conditions.

By situating computer-generated content in the physical world, mobile augmented reality (AR) can support many tasks that involve effective search and inspection of physical environments. Currently, there is limited information regarding the viability of using AR in realistic wide-area outdoor environments and how AR experiences affect human behavior in these environments. Here, we conducted a wide-area outdoor AR user study ($n=48$) using a commercially available AR headset (Microsoft Hololens 2) to compare (1) user interactions with physical and virtual objects in the environment (2) the effects of different lighting conditions on user behavior and AR experience and (3) the impact of varying cognitive load on AR task performance. Participants engaged in a treasure hunt task where they searched for and classified virtual target items (green "gems") in an augmented outdoor courtyard scene populated with physical and virtual objects. Cognitive load was manipulated so that in half the search trials users were required to monitor an audio stream and respond to specific target sounds. Walking paths, head orientation and eye gaze information were measured, and users were queried about their memory of encountered objects and provided feedback on the experience. Key findings included (1) Participants self-reported significantly lower comfort in the ambient natural light condition, with virtual objects more visible and participants more likely to walk into physical objects at night; (2) recall for physical objects was worse than for virtual objects, (3) participants discovered more gems hidden behind virtual objects than physical objects, implying higher attention on virtual objects and (4) dual-tasking modified search behavior. These results suggest there are important technical, perceptual and cognitive factors that must be considered if the full potential of "anywhere and anytime mobile AR" is to be realized.

SRI-EEG: State-Based Recurrent Imputation for EEG Artifact Correction.

Electroencephalogram (EEG) signals are often used as an input modality for Brain Computer Interfaces (BCIs). While EEG signals can be beneficial for numerous types of interaction scenarios in the real world, high levels of noise limits their usage to strictly noise-controlled environments such as a research laboratory. Even in a controlled environment, EEG is susceptible to noise, particularly from user motion, making it highly challenging to use EEG, and consequently BCI, as a ubiquitous user interaction modality. In this work, we address the EEG noise/artifact correction problem. Our goal is to detect physiological artifacts in EEG signal and automatically replace the detected artifacts with imputed values to enable robust EEG sensing overall requiring significantly reduced manual effort than is usual. We present a novel EEG state-based imputation model built upon a recurrent neural network, which we call SRI-EEG, and evaluate the proposed method on three publicly available EEG datasets. From quantitative and qualitative comparisons with six conventional and neural network based approaches, we demonstrate that our method achieves comparable performance to the state-of-the-art methods on the EEG artifact correction task.

ARbis Pictus: A Study of Vocabulary Learning with Augmented Reality.

We conducted a fundamental user study to assess potential benefits of AR technology for immersive vocabulary learning. With the idea that AR systems will soon be able to label real-world objects in any language in real time, our within-subjects lab-based study explores the effect of such an AR vocabulary prompter on participants learning nouns in an unfamiliar foreign language, compared to a traditional flashcard-based learning approach. Our results show that the immersive AR experience of learning with virtual labels on real-world objects is both more effective and more enjoyable for the majority of participants, compared to flashcards. Specifically, when participants learned through augmented reality, they scored significantly better on both same-day and 4-day delayed productive recall tests than when they learned using the flashcard method. We believe this result is an indication of the strong potential for language learning in augmented reality, particularly because of the improvement shown in sustained recall compared to the traditional approach.

Effects of Unaugmented Periphery and Vibrotactile Feedback on Proxemics with Virtual Humans in AR.

In this paper, we investigate factors and issues related to human locomotion behavior and proxemics in the presence of a real or virtual human in augmented reality (AR). First, we discuss a unique issue with current-state optical see-through head-mounted displays, namely the mismatch between a small augmented visual field and a large unaugmented periphery, and its potential impact on locomotion behavior in close proximity of virtual content. We discuss a potential simple solution based on restricting the field of view to the central region, and we present the results of a controlled human-subject study. The study results show objective benefits for this approach in producing behaviors that more closely match those that occur when seeing a real human, but also some drawbacks in overall acceptance of the restricted field of view. Second, we discuss the limited multimodal feedback provided by virtual humans in AR, present a potential improvement based on vibrotactile feedback induced via the floor to compensate for the limited augmented visual field, and report results showing that benefits of such vibrations are less visible in objective locomotion behavior than in subjective estimates of co-presence. Third, we investigate and document significant differences in the effects that real and virtual humans have on locomotion behavior in AR with respect to clearance distances, walking speed, and head motions. We discuss potential explanations for these effects related to social expectations, and analyze effects of different types of behaviors including idle standing, jumping, and walking that such real or virtual humans may exhibit in the presence of an observer.

User-Perspective AR Magic Lens from Gradient-Based IBR and Semi-Dense Stereo.

We present a new approach to rendering a geometrically-correct user-perspective view for a magic lens interface, based on leveraging the gradients in the real world scene. Our approach couples a recent gradient-domain image-based rendering method with a novel semi-dense stereo matching algorithm. Our stereo algorithm borrows ideas from PatchMatch, and adapts them to semi-dense stereo. This approach is implemented in a prototype device build from off-the-shelf hardware, with no active depth sensing. Despite the limited depth data, we achieve high-quality rendering for the user-perspective magic lens.

Effects of Information Availability on Command-and-Control Decision Making: Performance, Trust, and Situation Awareness.

OBJECTIVE: We investigated how increases in task-relevant information affect human decision-making performance, situation awareness (SA), and trust in a simulated command-and-control (C2) environment. BACKGROUND: Increased information is often associated with an improvement of SA and decision-making performance in networked organizations. However, previous research suggests that increasing information without considering the task relevance and the presentation can impair performance. METHOD: We used a simulated C2 task across two experiments. Experiment 1 varied the information volume provided to individual participants and measured the speed and accuracy of decision making for task performance. Experiment 2 varied information volume and information reliability provided to two participants acting in different roles and assessed decision-making performance, SA, and trust between the paired participants. RESULTS: In both experiments, increased task-relevant information volume did not improve task performance. In Experiment 2, increased task-relevant information volume reduced self-reported SA and trust, and incorrect source reliability information led to poorer task performance and SA. CONCLUSION: These results indicate that increasing the volume of information, even when it is accurate and task relevant, is not necessarily beneficial to decision-making performance. Moreover, it may even be detrimental to SA and trust among team members. APPLICATION: Given the high volume of available and shared information and the safety-critical and time-sensitive nature of many decisions, these results have implications for training and system design in C2 domains. To avoid decrements to SA, interpersonal trust, and decision-making performance, information presentation within C2 systems must reflect human cognitive processing limits and capabilities.

Characterizing spatial distributions of astrocytes in the mammalian retina.

MOTIVATION: In addition to being involved in retinal vascular growth, astrocytes play an important role in diseases and injuries, such as glaucomatous neuro-degeneration and retinal detachment. Studying astrocytes, their morphological cell characteristics and their spatial relationships to the surrounding vasculature in the retina may elucidate their role in these conditions. RESULTS: Our results show that in normal healthy retinas, the distribution of observed astrocyte cells does not follow a uniform distribution. The cells are significantly more densely packed around the blood vessels than a uniform distribution would predict. We also show that compared with the distribution of all cells, large cells are more dense in the vicinity of veins and toward the optic nerve head whereas smaller cells are often more dense in the vicinity of arteries. We hypothesize that since veinal astrocytes are known to transport toxic metabolic waste away from neurons they may be more critical than arterial astrocytes and therefore require larger cell bodies to process waste more efficiently. AVAILABILITY AND IMPLEMENTATION: A 1/8th size down-sampled version of the seven retinal image mosaics described in this article can be found on BISQUE (Kvilekval et al., 2010) at http://bisque.ece.ucsb.edu/client_service/view?resource=http://bisque.ece.ucsb.edu/data_service/dataset/6566968.

iVisDesigner: Expressive Interactive Design of Information Visualizations.

We present the design, implementation and evaluation of iVisDesigner, a web-based system that enables users to design information visualizations for complex datasets interactively, without the need for textual programming. Our system achieves high interactive expressiveness through conceptual modularity, covering a broad information visualization design space. iVisDesigner supports the interactive design of interactive visualizations, such as provisioning for responsive graph layouts and different types of brushing and linking interactions. We present the system design and implementation, exemplify it through a variety of illustrative visualization designs and discuss its limitations. A performance analysis and an informal user study are presented to evaluate the system.

Model Estimation and Selection towards Unconstrained Real-Time Tracking and Mapping.

We present an approach and prototype implementation to initialization-free real-time tracking and mapping that supports any type of camera motion in 3D environments, that is, parallax-inducing as well as rotation-only motions. Our approach effectively behaves like a keyframe-based Simultaneous Localization and Mapping system or a panorama tracking and mapping system, depending on the camera movement. It seamlessly switches between the two modes and is thus able to track and map through arbitrary sequences of parallax-inducing and rotation-only camera movements. The system integrates both model-based and model-free tracking, automatically choosing between the two depending on the situation, and subsequently uses the "Geometric Robust Information Criterion" to decide whether the current camera motion can best be represented as a parallax-inducing motion or a rotation-only motion. It continues to collect and map data after tracking failure by creating separate tracks which are later merged if they are found to overlap. This is in contrast to most existing tracking and mapping systems, which suspend tracking and mapping and thus discard valuable data until relocalization with respect to the initial map is successful. We tested our prototype implementation on a variety of video sequences, successfully tracking through different camera motions and fully automatically building combinations of panoramas and 3D structure.

The effects of visual realism on search tasks in mixed reality simulation.

In this paper, we investigate the validity of Mixed Reality (MR) Simulation by conducting an experiment studying the effects of the visual realism of the simulated environment on various search tasks in Augmented Reality (AR). MR Simulation is a practical approach to conducting controlled and repeatable user experiments in MR, including AR. This approach uses a high-fidelity Virtual Reality (VR) display system to simulate a wide range of equal or lower fidelity displays from the MR continuum, for the express purpose of conducting user experiments. For the experiment, we created three virtual models of a real-world location, each with a different perceived level of visual realism. We designed and executed an AR experiment using the real-world location and repeated the experiment within VR using the three virtual models we created. The experiment looked into how fast users could search for both physical and virtual information that was present in the scene. Our experiment demonstrates the usefulness of MR Simulation and provides early evidence for the validity of MR Simulation with respect to AR search tasks performed in immersive VR.

Spatial interaction in a multiuser immersive instrument.

The AlloSphere provides multiuser spatial interaction through a curved surround screen and surround sound. Two projects illustrate how researchers employed the AlloSphere to investigate the combined use of personal-device displays and the shared display. Another two projects combined multiuser interaction with multiagent systems. These projects point to directions for future ensemble-style collaborative interaction.

Stereoscopic highlighting: 2D graph visualization on stereo displays.

In this paper we present a new technique and prototype graph visualization system, stereoscopic highlighting, to help answer accessibility and adjacency queries when interacting with a node-link diagram. Our technique utilizes stereoscopic depth to highlight regions of interest in a 2D graph by projecting these parts onto a plane closer to the viewpoint of the user. This technique aims to isolate and magnify specific portions of the graph that need to be explored in detail without resorting to other highlighting techniques like color or motion, which can then be reserved to encode other data attributes. This mechanism of stereoscopic highlighting also enables focus+context views by juxtaposing a detailed image of a region of interest with the overall graph, which is visualized at a further depth with correspondingly less detail. In order to validate our technique, we ran a controlled experiment with 16 subjects comparing static visual highlighting to stereoscopic highlighting on 2D and 3D graph layouts for a range of tasks. Our results show that while for most tasks the difference in performance between stereoscopic highlighting alone and static visual highlighting is not statistically significant, users performed better when both highlighting methods were used concurrently. In more complicated tasks, 3D layout with static visual highlighting outperformed 2D layouts with a single highlighting method. However, it did not outperform the 2D layout utilizing both highlighting techniques simultaneously. Based on these results, we conclude that stereoscopic highlighting is a promising technique that can significantly enhance graph visualizations for certain use cases.

Robust Relocalization and Its Evaluation for Online Environment Map Construction.

The acquisition of surround-view panoramas using a single hand-held or head-worn camera relies on robust real-time camera orientation tracking and relocalization. This paper presents robust methodology and evaluation for camera orientation relocalization, using virtual keyframes for online environment map construction. In the case of tracking loss, incoming camera frames are matched against known-orientation keyframes to re-estimate camera orientation. Instead of solely using real keyframes from incoming video, the proposed approach employs virtual keyframes which are distributed strategically within completed portions of an environment map. To improve tracking speed, we introduce a new variant of our system which carries out relocalization only when tracking fails and uses inexpensive image-patch descriptors. We compare different system variants using three evaluation methods to show that the proposed system is useful in a practical sense. To improve relocalization robustness against lighting changes in indoor and outdoor environments, we propose a new approach based on illumination normalization and saturated area removal. We examine the performance of our solution over several indoor and outdoor video sequences, evaluating relocalization rates based on ground truth from a pan-tilt unit.

behaviorism: a framework for dynamic data visualization.

While a number of information visualization software frameworks exist, creating new visualizations, especially those that involve novel visualization metaphors, interaction techniques, data analysis strategies, and specialized rendering algorithms, is still often a difficult process. To facilitate the creation of novel visualizations we present a new software framework, behaviorism, which provides a wide range of flexibility when working with dynamic information on visual, temporal, and ontological levels, but at the same time providing appropriate abstractions which allow developers to create prototypes quickly which can then easily be turned into robust systems. The core of the framework is a set of three interconnected graphs, each with associated operators: a scene graph for high-performance 3D rendering, a data graph for different layers of semantically linked heterogeneous data, and a timing graph for sophisticated control of scheduling, interaction, and animation. In particular, the timing graph provides a unified system to add behaviors to both data and visual elements, as well as to the behaviors themselves. To evaluate the framework we look briefly at three different projects all of which required novel visualizations in different domains, and all of which worked with dynamic data in different ways: an interactive ecological simulation, an information art installation, and an information visualization technique.

Multithreaded hybrid feature tracking for markerless augmented reality.

We describe a novel markerless camera tracking approach and user interaction methodology for augmented reality (AR) on unprepared tabletop environments. We propose a real-time system architecture that combines two types of feature tracking. Distinctive image features of the scene are detected and tracked frame-to-frame by computing optical flow. In order to achieve real-time performance, multiple operations are processed in a synchronized multi-threaded manner: capturing a video frame, tracking features using optical flow, detecting distinctive invariant features, and rendering an output frame. We also introduce user interaction methodology for establishing a global coordinate system and for placing virtual objects in the AR environment by tracking a user's outstretched hand and estimating a camera pose relative to it. We evaluate the speed and accuracy of our hybrid feature tracking approach, and demonstrate a proof-of-concept application for enabling AR in unprepared tabletop environments, using bare hands for interaction.

Depth-fused 3D imagery on an immaterial display.

We present an immaterial display that uses a generalized form of depth-fused 3D (DFD) rendering to create unencumbered 3D visuals. To accomplish this result, we demonstrate a DFD display simulator that extends the established depth-fused 3D principle by using screens in arbitrary configurations and from arbitrary viewpoints. The feasibility of the generalized DFD effect is established with a user study using the simulator. Based on these results, we developed a prototype display using one or two immaterial screens to create an unencumbered 3D visual that users can penetrate, examining the potential for direct walk-through and reach-through manipulation of the 3D scene. We evaluate the prototype system in formative and summative user studies and report the tolerance thresholds discovered for both tracking and projector errors.

Heads up and camera down: a vision-based tracking modality for mobile mixed reality.

Anywhere Augmentation pursues the goal of lowering the initial investment of time and money necessary to participate in mixed reality work, bridging the gap between researchers in the field and regular computer users. Our paper contributes to this goal by introducing the GroundCam, a cheap tracking modality with no significant setup necessary. By itself, the GroundCam provides high frequency, high resolution relative position information similar to an inertial navigation system, but with significantly less drift. We present the design and implementation of the GroundCam, analyze the impact of several design and run-time factors on tracking accuracy, and consider the implications of extending our GroundCam to different hardware configurations. Motivated by the performance analysis, we developed a hybrid tracker that couples the GroundCam with a wide area tracking modality via a complementary Kalman filter, resulting in a powerful base for indoor and outdoor mobile mixed reality work. To conclude, the performance of the hybrid tracker and its utility within mixed reality applications is discussed.