Tens of thousands of years ago, humans drew sketches that we can see and identify even today. Sketches are the oldest recorded form of human communication and are still widely used. The universality of sketches supersedes that of culture and language. Despite the universal accessibility of sketches by humans, computers are unable to interpret or even correctly identify the contents of sketches drawn by humans with a practical level of accuracy. In my thesis, I demonstrate that the accuracy of existing sketch recognition techniques can be improved by optimizing the classification criteria. Current techniques classify a 20,000 sketch crowd-sourced dataset with 56% accuracy. I classify the same dataset with 52% accuracy, but identify factors that have the greatest effect on the accuracy. The ability for computers to identify human sketches would be useful particularly in pictionary-like games and other kinds of human-computer interaction; the concepts from sketch recognition could be extended to other kinds of object recognition.

A company, Unanimous AI, has created a software platform that allows individuals to come together as a group or a human swarm to make decisions. These human swarms amplify the decision-making capabilities of both the individuals and the group. One way Unanimous AI increases the swarm’s collective decision-making capabilities is by limiting the swarm to more informed individuals on the given topic. The previous way Unanimous AI selected users to enter the swarm was improved upon by a new methodology that is detailed in this study. This new methodology implements a new type of survey that collects data that is more indicative of a user’s knowledge on the subject than the previous survey. This study also identifies better metrics for predicting each user’s performance when predicting Major League Baseball game outcomes throughout a given week. This study demonstrates that the new machine learning models and data extraction schemes are approximately 12% more accurate than the currently implemented methods at predicting user performance. Finally, this study shows how predicting a user’s performance based purely on their inputs can increase the average performance of a group by limiting the group to the top predicted performers. This study shows that by limiting the group to the top predicted performers across five different weeks of MLB predictions, the average group performance was increased up to 5.5%, making this a superior method.

Diminished reality, as a sub-topic of augmented reality where digital information is overlaid on an environment, is the perceived removal of an object from an environment. Previous approaches to diminished reality used digital replacement techniques, inpainting, and multi-view homographies. However, few used a virtual representation of the real environment, limiting their domains to planar environments. This thesis provides a framework to achieve real-time diminished reality on an augmented reality headset. Using state-of-the-art hardware, we combine a virtual representation of the real environment with inpainting to remove existing objects from complex environments. Our work is found to be competitive with previous results, with a similar qualitative outcome under the limitations of available technology. Additionally, by implementing new texturing algorithms, a more detailed representation of the real environment is achieved.

There’s no denying the ever increasing presence of social networking in our daily lives. Every day, people share what they are thinking, doing, and experiencing. But even more so, they check their favorite networks to see what the people in their lives are sharing. Social networking has become so prevalent that most applications incorporate it since it keeps users engaged and beckons them back to the application again and again. PolyXpress is an interactive, location-based storytelling mobile application that functions as a platform for creating and experiencing stories. Written as a research project at California Polytechnic State University, it allows users to play through stories in real-world locations by using their smart phones. However, in an age of social networking, PolyXpress falls behind the curve, as it does not contain any social features. The work in this thesis aims to test if adding social networking features to PolyX- press will increase user engagement by performing a usability study. The new social features allow users to participate in public forums about stories, message friends while playing stories, and view their friends’ experiences within the app. The results of the study indicate that the overall user experience of PolyXpress was not increased by the social networking features; however, these features are desired and liked by the users. 70% of the experimental group enjoyed using the social features, while 30% remained indifferent. The problem is that the new features disrupted app satisfac- tion, as UI satisfaction decreased from 100% with the control group to 40% with the experimental group.

Studies of how users experience Virtual Reality (VR) have thus far failed to address the extent to which rendering resolution and rendering frame rate affect users’ sense of immersion in VR, including applications of VR involving simulators, treatments for psychological and mental disorders, explorations of new and nonexistent structures, and ways to better understand the human body in medical applications. This study investigated if rendering resolution affected users’ sense of immersion in VR. This was conducted by comparing the responses of two groups, relative to two measures of participant immersion: (a) participant’s sense of presence and (b) participant’s sense of embodiment. The treatment levels were (a) low 512 pixels per inch (ppi) and (b) high 2048 ppi rendering resolution. One potential moderating variable, game type, varied over three levels: narrative, objective, and situational. The participants were randomly assigned to a treatment level account for previous VR experience, neither participants nor the research observer knew the treatment level. Measurements were collected after each game via an Immersion tendency Questionnaire after each game. For each dependent measure, sample descriptive statistics—mean (M) and inter-quartile range (IQR) with a conventional significance level of 0.05—were evaluated to conclude the results. Data indicated that the rendering resolution did not affect user immersion, but the game type did affect immersion and the situational game type was determined to be significantly more immersive than the other game types.

Computer-generated imagery is ubiquitous in today's society, appearing in advertisements, video games, and computer-animated movies among other places. Much of this imagery needs to be as realistic as possible, and animators have turned to techniques such as fluid simulation to create scenes involving substances like smoke, fire, and water. The Lattice-Boltzmann Method (LBM) is one fluid simulation technique that has gained recent popularity due to its relatively simple basic algorithm and the ease with which it can be distributed across multiple processors. Unfortunately, current LBM simulations also suffer from high memory usage and restrict free surface fluids to domains of fixed size. This thesis modifies the LBM to utilize a recursive run-length-encoded (RLE) grid data structure instead of the standard fixed array of grid cells, which reduces the amount of memory required for LBM simulations as well as allowing the domain to grow and shrink as necessary to accomodate a liquid surface. The modified LBM is implemented within the open-source 3D animation package Blender and compared to Blender's current LBM simulator using the metrics of memory usage and time required to complete a given simulation. Results show that, although the RLE-based simulator can take several times longer than the current simulator to complete a given simulation, the memory usage is significantly reduced, making an RLE-based simulation preferable in a few specific circumstances.

Before there were airplanes, cars, trains, boats, or bicycles, the primary means of transportation was on foot. Unfortunately, many of the trails used by ancient travelers have long since been abandoned. We present a software tool which can help visualize and predict where these forgotten trails might lie through the use of a human-centered cost metric. By comparing the paths generated by our software with known historical trails, we demonstrate how the tool can indicate likely trails used by ancient travelers. In addition, this new tool provides novel visualizations to better help the user understand alternate paths, effect of terrain, and nearby areas of interest. Such a tool could be used by archaeologists and historians to better visualize and understand the terrain and paths around sites of historical interest. This thesis is a continuation of previous work, with emphasis on the ability to generate paths which traverse several thousand kilometers. To accomplish this, various graph simplification and path approximation algorithms are explored to construct a real-time path finding algorithm. To this end, we show that it is possible to restrict the search space for a path finding algorithm while not disrupting accuracy. Combined with a multi-threaded variant of Dijkstra's shortest path algorithm, we present a tool capable of traversing the contiguous US, a dataset containing over 19 billion datapoints, in under three hours on a 2.5 Ghz dual core processor. The tool is demonstrated on several examples which show the potential archaeological and historical applicability, and provide avenues for future improvements.

Networked virtual reality environments including virtual worlds devoted to entertainment, online socializing and remote collaboration have grown in popularity with the rise of commercially available consumer graphics hardware and the growing ubiquity of the Internet. These virtual worlds are typified by a persistent simulated three-dimensional space that communicates over a computer network, where users interact with the environment and each other through digital avatars. Development of these virtual worlds challenges the limits of the networking infrastructure, 3D streaming graphics techniques, and the distributed computing design of the virtual world systems that manages the simulation. In this dissertation, we explore solutions to different aspects of the overall problem of developing a general purpose, networked virtual environment, focusing on the networking and software system issues. Specifically, we show how to improve the networking infrastructure to better support the high packet-rate traffic that is typical of virtual worlds, efficiently stream terrain data for remote rendering, and construct a dynamically adaptive distributed systems framework suitable for virtual world simulations.

This project presents a virtual reality (VR) Interactive Narrative aiming to leave users reflecting on the perspectives one chooses to view life through. The narrative is driven by interactions designed using the concept of procedural rhetoric, which explores how rules and mechanics in games can persuade people about an idea, and Shin's cognitive model, which presents a dynamic view of immersion in VR. The persuasive nature of procedural rhetoric in combination with immersion techniques such as tangible interfaces and first-person elements of VR can effectively work together to immerse users into a compelling narrative experience with an intended emotional response output. The narrative is experienced through a young woman in a state between life and death, who wakes up as her subconscious-self in a limbo-like world consisting of core memories from her life, where the user is tasked with taking photos of the protagonist's memories for her to come back to life. Users primarily interact with and are integrated into the narrative through a photography mechanic, as they have the agency to select "perspective" filters to apply to the protagonist's camera from which to view a core memory through, ultimately choosing which perspectives of her memories become permanent when she comes back to life. This project hopes to provide an example of effectively applying procedural rhetoric to a VR interactive narrative so that future interactive narrative designers can further apply and explore how procedural rhetoric can work with immersion techniques to create compelling and immersive VR experiences.

Severe neuromuscular disorders can produce locked-in syndrome (LIS), a loss of nearly all voluntary muscle control. A brain-computer interface (BCI) using the P300 event-related potential provides communication that does not depend on neuromuscular activity and can be useful for those with LIS. Currently, there is no way of determining the effectiveness of P300-based BCIs without testing a person's performance multiple times. Additionally, P300 responses in BCI tasks may not resemble the typical P300 response. I sought to clarify the relationship between the P300 response and BCI task parameters and examine the possibility of a predictive relationship between traditional oddball tasks and BCI performance. Both waveform and component analysis have revealed several task-dependent aspects of brain activity that show significant correlation with the user's performance. These components may provide a fast and reliable metric to indicate whether the BCI system will work for a given individual.