The Dollar General: Continuous Custom Gesture Recognition Techniques At Everyday Low Prices

Humans use gestures to emphasize ideas and disseminate information. Their importance is apparent in how we continuously augment social interactions with motion—gesticulating in harmony with nearly every utterance to ensure observers understand that which we wish to communicate, and their relevance has not escaped the HCI community\u27s attention. For almost as long as computers have been able to sample human motion at the user interface boundary, software systems have been made to understand gestures as command metaphors. Customization, in particular, has great potential to improve user experience, whereby users map specific gestures to specific software functions. However, custom gesture recognition remains a challenging problem, especially when training data is limited, input is continuous, and designers who wish to use customization in their software are limited by mathematical attainment, machine learning experience, domain knowledge, or a combination thereof. Data collection, filtering, segmentation, pattern matching, synthesis, and rejection analysis are all non-trivial problems a gesture recognition system must solve. To address these issues, we introduce The Dollar General (TDG), a complete pipeline composed of several novel continuous custom gesture recognition techniques. Specifically, TDG comprises an automatic low-pass filter tuner that we use to improve signal quality, a segmenter for identifying gesture candidates in a continuous input stream, a classifier for discriminating gesture candidates from non-gesture motions, and a synthetic data generation module we use to train the classifier. Our system achieves high recognition accuracy with as little as one or two training samples per gesture class, is largely input device agnostic, and does not require advanced mathematical knowledge to understand and implement. In this dissertation, we motivate the importance of gestures and customization, describe each pipeline component in detail, and introduce strategies for data collection and prototype selection

Code Park: A New 3D Code Visualization Tool

We introduce Code Park, a novel tool for visualizing codebases in a 3D game-like environment. Code Park aims to improve a programmer's understanding of an existing codebase in a manner that is both engaging and intuitive, appealing to novice users such as students. It achieves these goals by laying out the codebase in a 3D park-like environment. Each class in the codebase is represented as a 3D room-like structure. Constituent parts of the class (variable, member functions, etc.) are laid out on the walls, resembling a syntax-aware "wallpaper". The users can interact with the codebase using an overview, and a first-person viewer mode. We conducted two user studies to evaluate Code Park's usability and suitability for organizing an existing project. Our results indicate that Code Park is easy to get familiar with and significantly helps in code understanding compared to a traditional IDE. Further, the users unanimously believed that Code Park was a fun tool to work with.Comment: Accepted for publication in 2017 IEEE Working Conference on Software Visualization (VISSOFT 2017); Supplementary video: https://www.youtube.com/watch?v=LUiy1M9hUK

Effects of Clutter on Egocentric Distance Perception in Virtual Reality

To assess the impact of clutter on egocentric distance perception, we performed a mixed-design study with 60 participants in four different virtual environments (VEs) with three levels of clutter. Additionally, we compared the indoor/outdoor VE characteristics and the HMD's FOV. The participants wore a backpack computer and a wide FOV head-mounted display (HMD) as they blind-walked towards three distinct targets at distances of 3m, 4.5m, and 6m. The HMD's field of view (FOV) was programmatically limited to 165{\deg}$\times$110{\deg}, 110{\deg}$\times$110{\deg}, or 45{\deg}$\times$35{\deg}. The results showed that increased clutter in the environment led to more precise distance judgment and less underestimation, independent of the FOV. In comparison to outdoor VEs, indoor VEs showed more accurate distance judgment. Additionally, participants made more accurate judgements while looking at the VEs through wider FOVs.Comment: This paper was not published yet in any venue or conference/journal, ACM conference format was used for the paper, authors were listed in order from first to last (advisor), 10 pages, 10 figure

Macro 64-Regions For Uniform Grids On Gpu

Uniform grids are a spatial subdivision acceleration structure well suited for ray tracing. They are known for their fast build times and ease of use, but suffer from slow traversals in the presence of empty space. To address this issue, we present macro 64-regions, a new GPU based approach for finding and storing empty volumes in an underlying uniform grid. This allows for fast traversals through regions that do not contain scene geometry. Further, unlike previous solutions to this problem, we do not store a hierarchical structure and therefore the traversal steps are simplified. Because macro 64-regions are dependent on an underlying grid, we also introduce an improvement in the grid construction process. Our method does not rely on sorting as previous methods do, but instead uses atomic operators to manage bookkeeping during the build. Using our proposed methods, we show a substantial improvement in build time, trace time, as well as an improvement in the consistency of rendering times for randomly generated views. © 2014 Springer-Verlag Berlin Heidelberg

Penny Pincher: A Blazing Fast, Highly Accurate $-Family Recognizer

The $-family of recognizers ($1, Protractor $N,$P, 1¢, and variants) are an easy to understand, easy to implement, accurate set of gesture recognizers designed for non-experts and rapid prototyping. They use template matching to classify candidate gestures and as the number of available templates increase, so do their accuracies. This, of course, is at the cost of higher latencies, which can be prohibitive in certain cases. Our recognizer Penny Pincher achieves high accuracy by being able to process a large number of templates in a short amount of time. If, for example, a recognition task is given a 50µs budget to complete its work, a fast recognizer that can process more templates within this constraint can potentially outperform its rival recognizers. Penny Pincher achieves this goal by reducing the template matching process to merely addition and multiplication, by avoiding translation, scaling, and rotation; and by avoiding calls to expensive geometric functions. Despite Penny Pincher\u27s deceptive simplicity, our recognizer, with a limited number of templates, still performs remarkably well. In an evaluation compared against four other $-family recognizers, in three of our six datasets, Penny Pincher achieves the highest accuracy of all recognizers reaching 97.5%, 99.8%, and 99.9% user independent recognition accuracy, while remaining competitive with the three remaining datasets. Further, when a time constraint is imposed, our recognizer always exhibits the highest accuracy, realizing a reduction in recognition error of between 83% to 99% in most cases

Math Boxes: A Pen-Based User Interface For Writing Difficult Mathematical Expressions

We present math boxes, a novel pen-based user interface for simplifying the task of hand writing difficult mathematical expressions. Visible bounding boxes around certain subexpressions are automatically generated as the system detects specific relationships including superscripts, subscripts, and fractions. Subexpressions contained in a box can then be extended by adding new terms directly into its given bounds. Upon accepting new characters, box boundaries are dynamically resized and neighboring terms are translated to make room for the larger box. Feedback on structural recognition is given via the boxes themselves. We also provide feedback on character recognition by morphing the user\u27s individual characters into a cleaner version stored in our ink database. To evaluate the usefulness of our proposed method, we conducted a user study in which participants write a variety of equations ranging in complexity from a simple polynomial to the more difficult expected value of the logistic distribution. The math boxes interface is compared against the commonly used offset typeset (small) method, where recognized expressions are typeset in a system font near the user\u27s unmodified ink. In our initial study, we find that the fluidness of the offset method is preferred for simple expressions but as difficulty increases, our math boxes method is overwhelmingly preferred

A $-Family Friendly Approach To Prototype Selection

We explore the benefits of intelligent prototype selection for $-family recognizers. Currently, the state of the art is to randomly select a subset of prototypes from a dataset without any processing. This results in reduced computation time for the recognizer, but also increases error rates. We propose applying optimization algorithms, specifically random mutation hill climb and a genetic algorithm, to search for reduced sets of prototypes that minimize recognition error. After an evaluation, we found that error rates could be reduced compared to random selection and rapidly approached the baseline accuracies for a number of different$-family recognizers

Streamlined And Accurate Gesture Recognition With Penny Pincher

Penny Pincher is a recently introduced template matching $-family gesture recognizer that exhibits competitive accuracy with even just one template. However, our recognizer is also able to rapidly compare a candidate gesture against numerous templates in a short amount of time, as compared to other recognizers, in order to achieve higher accuracy within a given time budget. Penny Pincher achieves this goal by reducing the template matching process to merely addition and multiplication; by avoiding translation, scaling, and rotation; and by avoiding calls to expensive geometric functions. In an evaluation compared against four other$-family recognizers, in three of our six datasets, Penny Pincher achieves the highest accuracy of all recognizers reaching 97.5%, 99.8%, and 99.9% user independent recognition accuracy, while remaining competitive with the three remaining datasets. Further, when a time constraint is imposed, our recognizer always exhibits the highest accuracy, realizing a reduction in recognition error of between 83% and 99% in most cases as Penny Pincher is able to process five times as many templates in the same amount of time as its closest competitor. Further, in this extended work, we also evaluate the effectiveness of Penny Pincher in a stressful setting using a video game prototype that makes heavy use of gestures, so that rushed and malformed gesture articulation is more likely. Our evaluation was conducted with a 24 participant between-subject user study of Protractor and Penny Pincher. Training data and in-game data collected during the user study was further used to evaluate several $-family recognizers. Again we find that our recognizer is on par with or better than the others, reducing the recognition error by as much as 5.8% to 10.4% with just a small number of templates per gesture

Code Park: A New 3D Code Visualization Tool

We introduce Code Park, a novel tool for visualizing codebases in a 3D game-like environment. Code Park aims to improve a programmer\u27s understanding of an existing codebase in a manner that is both engaging and intuitive, appealing to novice users such as students. It achieves these goals by laying out the codebase in a 3D park-like environment. Each class in the codebase is represented as a 3D room-like structure. Constituent parts of the class (variable, member functions, etc.) are laid out on the walls, resembling a syntax-aware \u27wallpaper\u27. The users can interact with the codebase using an overview, and a first-person viewer mode. We conducted two user studies to evaluate Code Park\u27s usability and suitability for organizing an existing project. Our results indicate that Code Park is easy to get familiar with and significantly helps in code understanding compared to a traditional IDE. Further, the users unanimously believed that Code Park was a fun tool to work with

A Rapid Prototyping Approach To Synthetic Data Generation For Improved 2D Gesture Recognition

Training gesture recognizers with synthetic data generated from real gestures is a well known and powerful technique that can significantly improve recognition accuracy. In this paper we introduce a novel technique called gesture path stochastic resampling (GPSR) that is computationally efficient, has minimal coding overhead, and yet despite its simplicity is able to achieve higher accuracy than competitive, state-of-the-art approaches. GPSR generates synthetic samples by lengthening and shortening gesture subpaths within a given sample to produce realistic variations of the input via a process of nonuniform resampling. As such, GPSR is an appropriate rapid prototyping technique where ease of use, understandability, and efficiency are key. Further, through an extensive evaluation, we show that accuracy significantly improves when gesture recognizers are trained with GPSR synthetic samples. In some cases, mean recognition errors are reduced by more than 70%, and in most cases, GPSR outperforms two other evaluated state-of-the-art methods