We explore the design of situated visual links in outdoor augmented reality (AR) for connecting miniature buildings on a virtual map to their real-world counterparts. We first distill design criteria from prior work, then conduct two user studies to evaluate a set of proposed link designs to better understand users' preferences for different design choices of the links. In two user studies we evaluated, respectively, a set of link geometries in a virtual environment and a refined AR prototype in two different outdoor environments. The studies reveal that links help in identifying buildings in the environments. Participants prefer straight rather than curved links, simple and thin links to avoid information occlusion, and links and maps aligned with their direction of view. We recommend using a consistent color with a strong contrast to the background color for all links in a scene. To improve visibility, the diameter of links should grow with distance to the viewer and optional animated stripes can be placed on links. The findings of this study have the potential to bolster the development of various situated visualization applications, such as those used in urban planning, tourism, smart agriculture, and other fields.

Abstract After a long period of scepticism, more and more publications describe basic research but also practical approaches to how abstract data can be presented in immersive environments for effective and efficient data understanding. Central aspects of this important research question in immersive analytics research are concerned with the use of 3D for visualization, the embedding in the immersive space, the combination with spatial data, suitable interaction paradigms and the evaluation of use cases. We provide a characterization that facilitates the comparison and categorization of published works and present a survey of publications that gives an overview of the state of the art, current trends, and gaps and challenges in current research.

Immersive Analytics is a quickly evolving field that unites several areas such as visualisation, immersive environments, and human-computer interaction to support human data analysis with emerging technologies. This research has thrived over the past years with multiple workshops, seminars, and a growing body of publications, spanning several conferences. Given the rapid advancement of interaction technologies and novel application domains, this paper aims toward a broader research agenda to enable widespread adoption. We present 17 key research challenges developed over multiple sessions by a diverse group of 24 international experts, initiated from a virtual scientific workshop at ACM CHI 2020. These challenges aim to coordinate future work by providing a systematic roadmap of current directions and impending hurdles to facilitate productive and effective applications for Immersive Analytics.

The COVID-19 pandemic has induced dramatic effects in all areas of society worldwide. It has led to drastic restrictions on academic exchanges and caused research projects to be put on hold. The need to maintain social distancing has severely affected the research communities that rely on in-person studies. In particular, Quality of Experience (QoE) research relies heavily on user studies and in-person subjective tests as means of obtaining ground truths for system designs. In this position paper, we focus on the impact of COVID-19 on conducting subjective tests for digital media quality assessment. An overview of the related discussions that take place in associated research communities is provided. A number of challenges are posed in terms of hygiene, ethics, standardization, and research quality issues. Opportunities for consolidating QoE research under COVID-19 and beyond are put forward, including alternative experimental designs and adaptation of research methodologies. Numerous enablers are suggested and discussed, revealing various avenues that can be followed to assist in conducting subjective tests under pandemic conditions. We believe that this position paper will be helpful for researchers and practitioners relying on in-person studies to keep abreast about the potential measures that support coming out of the COVID-19 pandemic strong and being prepared for future endeavors.

Invasive species are a major cause of ecological damage and commercial losses. A current problem spreading in North America and Europe is the vinegar fly Drosophila suzukii. Unlike other Drosophila, it infests non-rotting and healthy fruits and is therefore of concern to fruitgrowers, such as vintners. Consequently, large amounts of data about infestations have been collected in recent years. However, there is a lack of interactive methods to investigate this data. We employ ensemble-based classification to predict areas susceptible to infestation by D.suzukii and bring them into a spatio-temporal context using maps and glyph-based visualizations. Following the information-seeking mantra, we provide a visual analysis system Drosophigatorfor spatio-temporal event prediction, enabling the investigation of the spread dynamics of invasive species. We demonstrate the usefulness of this approach in two use cases.

There exists a gulf between the rhetoric in visualisation research about the significance of uncertainty and the inclusion of representations of uncertainty in visualisations used in practice. The graphical representation of uncertainty information has emerged as a problem of great importance in visualisation research. This contribution presents a survey of 286 uncertainty visualisation research publications. All publications are categorised with regard to publication type, publication venue, application domain, target user, and evaluation type. We present an interactive web-based browser that facilitates easy visual search and exploration of the publications included in the survey. We conclude that uncertainty visualisation is severely limited by the quality and scope of uncertainty data, by the limited confidence in the data, and by the perceptual and cognitive confusion that the graphical representation of the data can generate.

Welcome and Overview

Immersive analytics allows a large amount of data and complex structures to be concurrently investigated. We propose a collaborative analytics system that benefits from new advances in immersive technologies for collaborators working in the early stages of data exploration. We implemented a combination of Star Coordinates and Star Plot visualisation techniques to support the visualisation of multidimensional data and the encoding of datasets using simple and compact visual representations. To support data analytics tasks, we propose tools and interaction techniques for users to build decision trees for visualising and analysing data in a top-down method.

Human–computer interaction systems that bypass manual control can be beneficial for many use cases, including users with severe motor disability. We investigated pupillometry (inferring mental activity via dilations of the pupil) as an interaction method because it is noninvasive, easy to analyse, and increasingly available for practical development. In 3 experiments we investigated the efficacy of using pupillometry to detect imaginary motor movements of the hand. In Experiment 1 we demonstrated that, on average, the pupillary response is greater when the participant is imagining a hand-grasping motion, as compared with the control condition. In Experiment 2 we investigated how imaginary hand-grasping affects the pupillary response over time. In Experiment 3 we employed a simple classifier to demonstrate single-trial detection of imagined motor events using pupillometry. Using the mean pupil diameter of a single trial, accuracy rates as high as 71.25%, were achieved. Implications for the development of a pupillometry-based switch and future directions are discussed.

In this paper, we list the goals for and the pros and cons of guidance, and we discuss the role that it can play not only in key low-level visualization tasks but also the more sophisticated model-generation tasks of visual analytics. Recent advances in artificial intelligence, particularly in machine learning, have led to high hopes regarding the possibilities of using automatic techniques to perform some of the tasks that are currently done manually using visualization by data analysts. However, visual analytics remains a complex activity, combining many different subtasks. Some of these tasks are relatively low-level, and it is clear how automation could play a role—for example, classification and clustering of data. Other tasks are much more abstract and require significant human creativity, for example, linking insights gleaned from a variety of disparate and heterogeneous data artifacts to build support for decision making. In this paper, we outline the potential applications of guidance, as well as the inputs to guidance. We discuss challenges in implementing guidance, including the inputs to guidance systems and how to provide guidance to users. We propose potential methods for evaluating the quality of guidance at different phases in the analytic process and introduce the potential negative effects of guidance as a source of bias in analytic decision making.

We performed a user study in which we investigated the user experience of HoloBee, a visual analytics system for the exploration of bee drift data. We compared two user interfaces, a head-mounted augmented reality (aR) interface and a desktop interface. We present here a correlation analysis of the user experience ratings that we collected in a questionnaire: intuitive, easy to use, comfortable, natural, and efficient. All criteria are strongly correlated with an interface preference rating. Efficient and comfortable are, respectively, found to be most and least strongly correlated with interface preference. Furthermore, while all rating criteria are highly correlated amongst themselves for the desktop interface, natural and intuitive are only marginally correlated for the AR interface indicating that natural user interfaces are not necessarily intuitive.

course Share on Big data visual analytics: fundamentals, techniques, and tools Authors: Quang Vinh Nguyen Western Sydney University, Australia Western Sydney University, AustraliaView Profile , Ulrich Engelke CSIRO, Australia CSIRO, AustraliaView Profile Authors Info & Claims SA '17: SIGGRAPH Asia 2017 CoursesNovember 2017 Article No.: 2Pages 1–203https://doi.org/10.1145/3134472.3134516Published:27 November 2017Publication History 0citation436DownloadsMetricsTotal Citations0Total Downloads436Last 12 Months26Last 6 weeks7 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Design of preferred products requires affective quality information which relates to human emotional satisfaction. However, it is expensive and time consuming to conduct a full survey to investigate affective qualities regarding all objective features of a product. Therefore, developing a prediction model is essential in order to understand affective qualities on a product. This paper proposes a novel fuzzy regression method in order to predict affective quality and estimate fuzziness in human assessment, when objective features are given. The proposed fuzzy regression also improves on traditional fuzzy regression that simulate only a single characteristic with the resulting limitation that the amount of fuzziness is linear correlated with the independent and dependent variables. The proposed method uses a varying spread to simulate nonlinear and nonsymmetrical fuzziness caused by affective quality assessment. The effectiveness of the proposed method is evaluated by two very different case studies, affective design of an electric iron and image quality assessment, which involve different amounts of data, varying fuzziness, and discrete and continuous data. The results obtained by the proposed method are compared with those obtained by the state of art and the recently developed fuzzy regression methods. The results show that the proposed method can generate better prediction models in terms of three fuzzy criteria, which address both predictions of magnitudes and fuzziness.

We investigate task performance and reading characteristics for scatterplots (Cartesian coordinates) and parallel coordinates. In a controlled eye-tracking study, we asked 24 participants to assess the relative distance of points in multidimensional space, depending on the diagram type (parallel coordinates or a horizontal collection of scatterplots), the number of data dimensions (2, 4, 6, or 8), and the relative distance between points (15%, 20%, or 25%). For a given reference point and two target points, we instructed participants to choose the target point that was closer to the reference point in multidimensional space. We present a visual scanning model that describes different strategies to solve this retrieval task for both diagram types, and propose corresponding hypotheses that we test using task completion time, accuracy, and gaze positions as dependent variables. Our results show that scatterplots outperform parallel coordinates significantly in 2 dimensions, however, the task was solved more quickly and more accurately with parallel coordinates in 8 dimensions. The eye-tracking data further shows significant differences between Cartesian and parallel coordinates, as well as between different numbers of dimensions. For parallel coordinates, there is a clear trend toward shorter fixations and longer saccades with increasing number of dimensions. Using an area-of-interest (AOI) based approach, we identify different reading strategies for each diagram type: For parallel coordinates, the participants’ gaze frequently jumped back and forth between pairs of axes, while axes were rarely focused on when viewing Cartesian coordinates. We further found that participants’ attention is biased: toward the center of the whole plotfor parallel coordinates and skewed to the center/left side for Cartesian coordinates. We anticipate that these results may support the design of more effective visualizations for multidimensional data.

An evolutionary algorithm (EA)-assisted spatial sampling methodology is proposed to assist decision makers in sensor network (SN) deployments. We incorporated an interpolation technique with leave-one-out cross-validation (LOOCV) to assess the representativeness of a particular SN design. For the validation of our method, we utilized Tasmania's South Esk Hydrological Model developed by the Commonwealth Scientific and Industrial Research Organisation, which includes a range of environmental variables describing the landscape. We demonstrated that our proposed methodology is capable of assisting in the initial design of SN deployment. Ordinary Kriging is shown to be the best suited spatial interpolation algorithm for the EA's LOOCV under the current empirical study.

Images captured by digital cameras are generally not perfect as image blurring is usually generated by camera motion through long hand-held exposure. Deblurring filters can be used to improve image quality by removing image blur. Prior to develop a deblurring filter, a simulator for image quality assessment is essential to optimize filter parameters. Although subjective image quality assessment (subjective IQA) is commonly used for evaluating the visual effect of digital images for a wide range of image processing applications, it is inconvenient to be implemented in real-time. Generally, statistical regression is used to generate a functional map to correlate the subjective IQA and the objective image quality metrics. However, it cannot address the uncertainty caused by human judgment during the subjective IQA. This paper first proposes a fuzzy regression method to develop the functional map that overcomes the limitation of statistical regression that cannot account for uncertainty introduced through human judgment. Based on the fuzzy regression models, the deblurring filter parameters can be optimized. Experimental results show that the satisfactory deblurring can be achieved on blurred images captured by a smartphone camera.

The impact of perceptual relevance information on content aware image retargeting is investigated. We integrated fixation density maps and region-of-interest maps into a contemporary image retargeting algorithm to test the hypothesis that the latter result in superior performance given their object level representation. We performed an experimental study with human participants to evaluate the performance gains relative to benchmark conditions. The experiment revealed that the kind of perceptual relevance information, image content, and retargeting ratio all have a strong impact on the overall performance. Recorded response times provided further insight into the difficulty that people experienced when performing the assessment task. These findings are instrumental for the image retargeting research community to further improve their algorithms by augmenting content awareness with perceptual relevance.

The combination of low density SNP arrays and DNA pooling is a fast and cost effective approach to genotyping that opens up basic genomics to a range of new applications and studies. However we have identified significant limitations in the existing approach to calculating allele frequencies with DNA pooling. These limitations include a reduced ability to deal with SNP to SNP variation via the standard interpolation method. Our contribution is a new hierarchical learning framework which resolves these drawbacks. The framework involves a hierarchy of two greedily trained layers of learners. The first layer learns the bias of each SNP then applies a calibration to reduce SNP bias by mapping into a common coordinate system across all SNPs. The second layer learns an allele frequency function exploiting the global SNP data. A range of algorithms have been applied including linear regression, neural network and support vector regression. The framework has been tested on pooled samples of Black Tiger prawns that have been genotyped with low density Sequenom iPLEX panels. Analysis of pooled samples and the corresponding individually genotyped SNP samples indicate the pooling approach introduces an allele frequency RMS error of 0.12. The existing calibration approach corrects ~14% of the error. Our hierarchical approach is 4.5 times as effective by correcting for ~64% of the introduced error. This is a significant reduction and has the potential to enable genetic studies previously not possible due to allele frequency error. Although testing so far is limited to low density SNP arrays the approach was developed to generalize to other SNP genotyping technologies.

Our previous research has demonstrated that adding natural scene saliency (NSS) obtained from eye-tracking data may improve an objective metric's performance in predicting perceived image quality. In this letter, we further investigate the image content dependency of this improvement. Results show that the variation in saliency between observers highly depends on image content, and that this variation predicts the extent to which a certain image may profit from adding saliency in the objective image quality assessment.

Fixation density maps (FDM) created from eye tracking experiments are widely used in image processing applications. The FDM are assumed to be reliable ground truths of human visual attention and as such, one expects a high similarity between FDM created in different laboratories. So far, no studies have analyzed the degree of similarity between FDM from independent laboratories and the related impact on the applications. In this paper, we perform a thorough comparison of FDM from three independently conducted eye tracking experiments. We focus on the effect of presentation time and image content and evaluate the impact of the FDM differences on three applications: visual saliency modeling, image quality assessment, and image retargeting. It is shown that the FDM are very similar and that their impact on the applications is low. The individual experiment comparisons, however, are found to be significantly different, showing that inter-laboratory differences strongly depend on the experimental conditions of the laboratories. The FDM are publicly available to the research community.

We introduce a novel region-of-interest (ROI) database for natural image content, the ROI-D database. The database consists of ROI maps created from manual selections obtained in a psychophysical experiment with 20 participants. The presented stimuli were 42 photographic images taken from 3 publicly available image quality databases. In addition to the ROI selections, dominance ratings were recorded that provide further insight into the interest of the selected ROI in relation to the background. In this paper, the experiment is described, the resulting ROI database is analysed, and possible applications of the database are discussed. The ROI-D database is made freely available to the image processing research community.

We analyse variations of saliency between human observers when viewing natural image content. For this purpose, we conducted an eye tracking experiment to record gaze patterns of humans when viewing natural images. The resulting gaze patterns were used to create saliency maps for each observer and image. A correlation analysis is then conducted with the aim to quantify the variability of saliency between the observers and the different images. The results are considered to be a first step towards gaining a better understanding regarding the reliability of saliency maps for natural images.

Visual attention models are typically designed based on human gaze patterns recorded through eye tracking. In this paper, two similar eye tracking experiments from independent laboratories are presented, in which humans observed natural images under task-free condition. The resulting saliency maps are analysed with respect to two criteria; the consistency between the experiments and the impact of the image presentation time. It is shown, that the saliency maps between the experiments are strongly correlated independent of presentation time. It is further revealed that the presentation time can be reduced without substantially sacrificing the accuracy of the convergent saliency map. The results provide valuable insight into the similarity of saliency maps from independent laboratories and are highly beneficial for the creation of converging saliency maps at reduced experimental time and cost.

In this paper, distortions caused by packet loss during video transmission are evaluated with respect to their perceived annoyance. In this respect, the impact of visual saliency on the level of annoyance is of particular interest, as regions and objects in a video frame are typically not of equal importance to the viewer. For this purpose, gaze patterns from a task free eye tracking experiment were utilised to identify salient regions in a number of videos. Packet loss was then introduced into the bit stream such as that the corresponding distortions appear either in a salient region or in a non-salient region. A subjective experiment was then conducted in which human observers rated the annoyance of the distortions in the videos. The outcomes show a strong tendency that distortions in a salient region are indeed perceived as much more annoying as compared to distortions in the non-salient region. The saliency of the distorted image content was further found to have a larger impact on the perceived annoyance as compared to the distortion duration. The findings of this work are considered to be of great use to improve prediction performance of video quality metrics in the context of transmission errors.

Mean opinion scores obtained in subjective image quality experiments are widely accepted as measures of perceived visual quality. They have, however, a strong limitation regarding the reliability of the rated quality, since there is no explicit information as to whether the human observer experienced difficulties when judging image quality. We thus suggest that additional information about the observers confidence should be provided along with the actual quality measure. In this paper, we analyse two ways of obtaining this confidence measure; firstly as a confidence score given by the human observer and secondly as an indirect measure of the observers response time to provide the quality score. We reveal strong relationships of confidence scores and response times to the quality scores. We further propose a model to predict observer confidence based on the quality scores and response times.

The modelling of perceptual image quality metrics has experienced increased effort in recent years. In order to allow for model design, validation, and comparison, a number of subjective image quality databases has been made available to the research community. Most metrics that were designed using these databases assess the quality uniformly over the whole image, not taking into account stronger attention to salient regions of an image. In order to facilitate incorporation of visual attention (VA) into objective metric design we have conducted an eye tracking experiment. The experiment and its outcomes will be explained in detail in this paper. The actual gaze data recorded in the experiment is publicly available in order to facilitate and encourage VA modelling for image quality metrics.

Visual attention (VA) is an integral property of the human visual system. The deployment of a VA model can be beneficial for many image and video applications, such as, compression, transmission, and quality assessment. However, the design of a VA model is highly dependent on the subjective VA data used as ground truth and the application that the model is intended for. In this paper, we discuss two ways of obtaining subjective VA data that can subsequently be used to develop VA models; selective regions-of-interest and visual fixation patterns. The feasibility of both methods will be discussed, in particular, with respect to visual quality assessment.

In this paper, regional attention to structural degradations in images is analyzed to improve perceptual quality prediction performance of objective image quality metrics. Subjective experiments were conducted to identify regions-of-interest for a set of natural images. A region-selective metric design is then applied to four objective image quality metrics which were trained and validated with respect to quality prediction accuracy and generalization to unknown images. For this purpose, data is used from subjective quality experiments conducted at two independent laboratories. It is shown that the region-selective design is highly beneficial for the considered objective image quality metrics, in particular, prediction accuracy can be significantly increased.

In this paper, we consider multiobjective optimization for obtaining the feature weights associated with structural image impairments and its application to wireless imaging quality assessment. The proposed framework supports optimization with respect to finding a trade-off between metric prediction accuracy and generalization to unknown images. Evaluation of optimal trade-off solutions for two representative scenarios reveal the benefits of the proposed approach. In particular, quality prediction accuracy of an objective image metric can be strongly increased and negligible features are identified that can be discarded to save computational complexity.

In this paper, multiobjective optimization is applied to determine the optimal feature weights for a multi-resolution image quality metric. The optimization is conducted with respect to two aims; maximization of quality prediction accuracy and generalisation ability to unknown images. A thresholding of the optimal weights is applied to further reduce computational complexity of the quality metric. A detailed discussion of the trade-off between prediction performance and complexity with respect to the threshold is provided. The metric design and evaluation is supported using mean opinion scores from subjective quality experiments that we conducted in two independent laboratories. Comparison to other state of the art quality metrics reveals the ability of the proposed metric to accurately predict subjective image quality perception.

In digital transmission, images may undergo quality degradation due to lossy compression and error-prone channels. Efficient measurement tools are needed to quantify induced distortions and to predict their impact on perceived quality. In this paper, an artificial neural network (ANN) is proposed for perceptual image quality assessment. The quality prediction is based on structural image features such as blocking, blur, image activity, and intensity masking. Training and testing of the ANN is performed with reference to subjective experiments and the obtained mean opinion scores (MOS). It is shown that the proposed ANN is capable of predicting MOS over a wide range of image distortions. This applies to both cases, when reference information about the structure of the original image is available to the ANN but also in absence of this knowledge. The considered ANN would therefore be well suited for combination with link adaption techniques.

This paper examines the application of UDP-lite for unequal error detection in packet-switched speech transmission via Internet protocols (voice-over-IP) over UMTS radio channels. Traditionally, UDP is used as transport layer protocol, which contains a checksum that covers the complete packet. Thus, any packet with residual bit errors is discarded. Speech codecs like AMR, however, can tolerate bit errors in less sensitive parts of the bitstream. A more recent development, UDP-lite, provides unequal error detection with a partial checksum that covers only the sensitive parts of a packet. Thus, only packets with errors in important bits are discarded. We compare the use of UDP-lite for UMTS channels with convolutional and channels with turbo coding. The results show that the achievable quality improvement by applying UDP-lite depends on the residual bit error distribution of the chosen UMTS channel coding method. While we determined a quality improvement for channels with convolutional coding, we did not get an improvement for turbo coding. Furthermore, when combined with header compression, the convolutional coder with use of UDP-lite can reach the performance of the turbo coder with use of UDP