Statistics for Spatial Data.

In 1974 an article appeared in Science magazine with the dry-sounding title “Judgment Under Uncertainty: Heuristics and Biases” by a pair of psychologists who were not well known outside their discipline of decision theory. In it Amos Tversky and Daniel Kahneman introduced the world to Prospect Theory, which mapped out how humans actually behave when faced with decisions about gains and losses, in contrast to how economists assumed that people behave. Prospect Theory turned Economics on its head by demonstrating through a series of ingenious experiments that people are much more concerned with losses than they are with gains, and that framing a choice from one perspective or the other will result in decisions that are exactly the opposite of each other, even if the outcomes are monetarily the same. Prospect Theory led cognitive psychology in a new direction that began to uncover other human biases in thinking that are probably not learned but are part of our brain’s wiring.

Thinking fast and slow.

Social Network Analysis

In 1997, Azuma published a survey on augmented reality (AR). Our goal is to complement, rather than replace, the original survey by presenting representative examples of the new advances. We refer one to the original survey for descriptions of potential applications (such as medical visualization, maintenance and repair of complex equipment, annotation, and path planning); summaries of AR system characteristics (such as the advantages and disadvantages of optical and video approaches to blending virtual and real, problems in display focus and contrast, and system portability); and an introduction to the crucial problem of registration, including sources of registration error and error-reduction strategies.

https://www.cc.gatech.edu/~blair/papers/ARsurveyCGA.pdf

Recent advances in augmented reality

Recently dense trajectories were shown to be an efficient video representation for action recognition and achieved state-of-the-art results on a variety of datasets. This paper improves their performance by taking into account camera motion to correct them. To estimate camera motion, we match feature points between frames using SURF descriptors and dense optical flow, which are shown to be complementary. These matches are, then, used to robustly estimate a homography with RANSAC. Human motion is in general different from camera motion and generates inconsistent matches. To improve the estimation, a human detector is employed to remove these matches. Given the estimated camera motion, we remove trajectories consistent with it. We also use this estimation to cancel out camera motion from the optical flow. This significantly improves motion-based descriptors, such as HOF and MBH. Experimental results on four challenging action datasets (i.e., Hollywood2, HMDB51, Olympic Sports and UCF50) significantly outperform the current state of the art.

/pdf/action-recognition-with-improved-trajectories-3z939q0pgz.pdf

Action Recognition with Improved Trajectories

Augmented reality annotations and virtual scene navigation add new dimensions to remote collaboration. In this paper, we present a touchscreen interface for creating freehand drawings as world-stabilized annotations and for virtually navigating a scene reconstructed live in 3D, all in the context of live remote collaboration. Two main focuses of this work are (1) automatically inferring depth for 2D drawings in 3D space, for which we evaluate four possible alternatives, and (2) gesture-based virtual navigation designed specifically to incorporate constraints arising from partially modeled remote scenes. We evaluate these elements via qualitative user studies, which in addition provide insights regarding the design of individual visual feedback elements and the need to visualize the direction of drawings.

In touch with the remote world: remote collaboration with augmented reality drawings and virtual navigation

Twitter is a major forum for rapid dissemination of user-provided content in real time. As such, a large proportion of the information it contains is not particularly relevant to many users and in fact is perceived as unwanted 'noise' by many. There has been increased research interest in predicting whether tweets are relevant, newsworthy or credible, using a variety of models and methods. In this paper, we focus on an analysis that highlights the utility of the individual features in Twitter such as hash tags, retweets and mentions for predicting credibility. We first describe a context-based evaluation of the utility of a set of features for predicting manually provided credibility assessments on a corpus of microblog tweets. This is followed by an evaluation of the distribution/presence of each feature across 8 diverse crawls of tweet data. Last, an analysis of feature distribution across dyadic pairs of tweets and retweet chains of various lengths is described. Our results show that the best indicators of credibility include URLs, mentions, retweets and tweet length and that features occur more prominently in data describing emergency and unrest situations.

Credibility in Context: An Analysis of Feature Distributions in Twitter

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.

/pdf/multithreaded-hybrid-feature-tracking-for-markerless-4qn6pdc52u.pdf

Multithreaded Hybrid Feature Tracking for Markerless Augmented Reality

This paper presents and evaluates three computational models for recommending credible topic-specific information in Twitter. The first model focuses on credibility at the user level, harnessing various dynamics of information flow in the underlying social graph to compute a rating. The second model applies a content-based strategy to compute a finer-grained credibility score for individual tweets. Lastly, we discuss a third model which combines facets from both models in a hybrid method, using both averaging and filtering hybrid strategies. To evaluate our novel credibility models, we perform an evaluation on 7 topic specific data sets mined from the Twitter streaming API, with specific focus on a data set of 37K users who tweeted about the topic "Libya". Results show that the social model outperfoms hybrid and content-based prediction models in terms of predictive accuracy over a set of manually collected credibility ratings on the "Libya" dataset.

/pdf/modeling-topic-specific-credibility-on-twitter-1xmijv56v4.pdf

Modeling topic specific credibility on twitter

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.

Tobias Höllerer

Papers

In touch with the remote world: remote collaboration with augmented reality drawings and virtual navigation

Credibility in Context: An Analysis of Feature Distributions in Twitter

Multithreaded Hybrid Feature Tracking for Markerless Augmented Reality

Modeling topic specific credibility on twitter

iVisDesigner: Expressive Interactive Design of Information Visualizations.