Developing Digital Media Platforms for Early Design

In recent times, mobile devices are becoming an integral part of our daily life. Software applications on these handheld devices are successfully migrating the traditional paper-based activities such as reading news, books, and even navigating through maps, onto the digital medium. While these applications allow information access anywhere and anytime, there is still a necessity for repurposing these digital media to support content/information creation especially in domains such as industrial design where paper-based activities are common. To utilize direct-touch tablets for collaborative conceptual design, we studied their affordances and iteratively developed a web-based wiki system, named skWiki. In this thesis, we first report an evaluation of the impact of utilizing a capacitive stylus for tracing and sketching on direct-touch tablets. This study uncovers the differences in quantitative and qualitative performance of the tablet medium compared to the paper medium when using a stylus (pen) or finger input for both tracing and sketching. While paper performed better overall, we found that the tablet medium, when used with a capacitive stylus, performed comparably to the paper medium for sketching tasks. These findings can guide sketch application designers in developing an appropriate interaction design for various input methods. In order to explore the advantages of the ubiquity of information generated on digital media, we developed Sketchbox, an Android application for sketching and sharing ideas using Dropbox as the storage cloud. An evaluation of the usage patterns of this application in a collaborative toy design scenario provided necessary guidelines for developing the skWiki system. skWiki overcomes the drawbacks of traditional wiki software, that are used as design repositories, by providing a rich editor infrastructure for sketching, text editing, and image editing. Apart from these features, skWiki provides a higher degree of freedom in sharing (cloning, branching, and merging) different versions of a sketch at various data granularities by introducing the concept of paths for maintaining revisions in a collaborative design process. We evaluated the utility of skWiki through a user study by comparing constrained and unconstrained sharing models. Furthermore, skWiki was used by the students of toy design and product design courses for both collaborative ideation and design activities. We discuss the findings and qualitative feedback from the evaluation of skWiki, and potential features for the next version of this tool

Designing Hybrid Interactions through an Understanding of the Affordances of Physical and Digital Technologies

Two recent technological advances have extended the diversity of domains and social contexts of Human-Computer Interaction: the embedding of computing capabilities into physical hand-held objects, and the emergence of large interactive surfaces, such as tabletops and wall boards. Both interactive surfaces and small computational devices usually allow for direct and space-multiplex input, i.e., for the spatial coincidence of physical action and digital output, in multiple points simultaneously. Such a powerful combination opens novel opportunities for the design of what are considered as hybrid interactions in this work. This thesis explores the affordances of physical interaction as resources for interface design of such hybrid interactions. The hybrid systems that are elaborated in this work are envisioned to support specific social and physical contexts, such as collaborative cooking in a domestic kitchen, or collaborative creativity in a design process. In particular, different aspects of physicality characteristic of those specific domains are explored, with the aim of promoting skill transfer across domains. irst, different approaches to the design of space-multiplex, function-specific interfaces are considered and investigated. Such design approaches build on related work on Graspable User Interfaces and extend the design space to direct touch interfaces such as touch-sensitive surfaces, in different sizes and orientations (i.e., tablets, interactive tabletops, and walls). These approaches are instantiated in the design of several experience prototypes: These are evaluated in different settings to assess the contextual implications of integrating aspects of physicality in the design of the interface. Such implications are observed both at the pragmatic level of interaction (i.e., patterns of users' behaviors on first contact with the interface), as well as on user' subjective response. The results indicate that the context of interaction affects the perception of the affordances of the system, and that some qualities of physicality such as the 3D space of manipulation and relative haptic feedback can affect the feeling of engagement and control. Building on these findings, two controlled studies are conducted to observe more systematically the implications of integrating some of the qualities of physical interaction into the design of hybrid ones. The results indicate that, despite the fact that several aspects of physical interaction are mimicked in the interface, the interaction with digital media is quite different and seems to reveal existing mental models and expectations resulting from previous experience with the WIMP paradigm on the desktop PC

Physical sketching tools and techniques for customized sensate surfaces

Sensate surfaces are a promising avenue for enhancing human interaction with digital systems due to their inherent intuitiveness and natural user interface. Recent technological advancements have enabled sensate surfaces to surpass the constraints of conventional touchscreens by integrating them into everyday objects, creating interactive interfaces that can detect various inputs such as touch, pressure, and gestures. This allows for more natural and intuitive control of digital systems. However, prototyping interactive surfaces that are customized to users' requirements using conventional techniques remains technically challenging due to limitations in accommodating complex geometric shapes and varying sizes. Furthermore, it is crucial to consider the context in which customized surfaces are utilized, as relocating them to fabrication labs may lead to the loss of their original design context. Additionally, prototyping high-resolution sensate surfaces presents challenges due to the complex signal processing requirements involved. This thesis investigates the design and fabrication of customized sensate surfaces that meet the diverse requirements of different users and contexts. The research aims to develop novel tools and techniques that overcome the technical limitations of current methods and enable the creation of sensate surfaces that enhance human interaction with digital systems.Sensorische Oberflächen sind aufgrund ihrer inhärenten Intuitivität und natürlichen Benutzeroberfläche ein vielversprechender Ansatz, um die menschliche Interaktionmit digitalen Systemen zu verbessern. Die jüngsten technologischen Fortschritte haben es ermöglicht, dass sensorische Oberflächen die Beschränkungen herkömmlicher Touchscreens überwinden, indem sie in Alltagsgegenstände integriert werden und interaktive Schnittstellen schaffen, die diverse Eingaben wie Berührung, Druck, oder Gesten erkennen können. Dies ermöglicht eine natürlichere und intuitivere Steuerung von digitalen Systemen. Das Prototyping interaktiver Oberflächen, die mit herkömmlichen Techniken an die Bedürfnisse der Nutzer angepasst werden, bleibt jedoch eine technische Herausforderung, da komplexe geometrische Formen und variierende Größen nur begrenzt berücksichtigt werden können. Darüber hinaus ist es von entscheidender Bedeutung, den Kontext, in dem diese individuell angepassten Oberflächen verwendet werden, zu berücksichtigen, da eine Verlagerung in Fabrikations-Laboratorien zum Verlust ihres ursprünglichen Designkontextes führen kann. Zudem stellt das Prototyping hochauflösender sensorischer Oberflächen aufgrund der komplexen Anforderungen an die Signalverarbeitung eine Herausforderung dar. Diese Arbeit erforscht dasDesign und die Fabrikation individuell angepasster sensorischer Oberflächen, die den diversen Anforderungen unterschiedlicher Nutzer und Kontexte gerecht werden. Die Forschung zielt darauf ab, neuartigeWerkzeuge und Techniken zu entwickeln, die die technischen Beschränkungen derzeitigerMethoden überwinden und die Erstellung von sensorischen Oberflächen ermöglichen, die die menschliche Interaktion mit digitalen Systemen verbessern

Tangible user interfaces : past, present and future directions

In the last two decades, Tangible User Interfaces (TUIs) have emerged as a new interface type that interlinks the digital and physical worlds. Drawing upon users' knowledge and skills of interaction with the real non-digital world, TUIs show a potential to enhance the way in which people interact with and leverage digital information. However, TUI research is still in its infancy and extensive research is required in or- der to fully understand the implications of tangible user interfaces, to develop technologies that further bridge the digital and the physical, and to guide TUI design with empirical knowledge. This paper examines the existing body of work on Tangible User In- terfaces. We start by sketching the history of tangible user interfaces, examining the intellectual origins of this field. We then present TUIs in a broader context, survey application domains, and review frame- works and taxonomies. We also discuss conceptual foundations of TUIs including perspectives from cognitive sciences, phycology, and philoso- phy. Methods and technologies for designing, building, and evaluating TUIs are also addressed. Finally, we discuss the strengths and limita- tions of TUIs and chart directions for future research

Application of Machine Learning within Visual Content Production

We are living in an era where digital content is being produced at a dazzling pace. The heterogeneity of contents and contexts is so varied that a numerous amount of applications have been created to respond to people and market demands. The visual content production pipeline is the generalisation of the process that allows a content editor to create and evaluate their product, such as a video, an image, a 3D model, etc. Such data is then displayed on one or more devices such as TVs, PC monitors, virtual reality head-mounted displays, tablets, mobiles, or even smartwatches. Content creation can be simple as clicking a button to film a video and then share it into a social network, or complex as managing a dense user interface full of parameters by using keyboard and mouse to generate a realistic 3D model for a VR game. In this second example, such sophistication results in a steep learning curve for beginner-level users. In contrast, expert users regularly need to refine their skills via expensive lessons, time-consuming tutorials, or experience. Thus, user interaction plays an essential role in the diffusion of content creation software, primarily when it is targeted to untrained people. In particular, with the fast spread of virtual reality devices into the consumer market, new opportunities for designing reliable and intuitive interfaces have been created. Such new interactions need to take a step beyond the point and click interaction typical of the 2D desktop environment. The interactions need to be smart, intuitive and reliable, to interpret 3D gestures and therefore, more accurate algorithms are needed to recognise patterns. In recent years, machine learning and in particular deep learning have achieved outstanding results in many branches of computer science, such as computer graphics and human-computer interface, outperforming algorithms that were considered state of the art, however, there are only fleeting efforts to translate this into virtual reality. In this thesis, we seek to apply and take advantage of deep learning models to two different content production pipeline areas embracing the following subjects of interest: advanced methods for user interaction and visual quality assessment. First, we focus on 3D sketching to retrieve models from an extensive database of complex geometries and textures, while the user is immersed in a virtual environment. We explore both 2D and 3D strokes as tools for model retrieval in VR. Therefore, we implement a novel system for improving accuracy in searching for a 3D model. We contribute an efficient method to describe models through 3D sketch via an iterative descriptor generation, focusing both on accuracy and user experience. To evaluate it, we design a user study to compare different interactions for sketch generation. Second, we explore the combination of sketch input and vocal description to correct and fine-tune the search for 3D models in a database containing fine-grained variation. We analyse sketch and speech queries, identifying a way to incorporate both of them into our system's interaction loop. Third, in the context of the visual content production pipeline, we present a detailed study of visual metrics. We propose a novel method for detecting rendering-based artefacts in images. It exploits analogous deep learning algorithms used when extracting features from sketches

Algorithms for sketching surfaces

CISRG discussion paper ; 1

Integrating Multiple Sketch Recognition Methods to Improve Accuracy and Speed

Sketch recognition is the computer understanding of hand drawn diagrams. Recognizing sketches instantaneously is necessary to build beautiful interfaces with real time feedback. There are various techniques to quickly recognize sketches into ten or twenty classes. However for much larger datasets of sketches from a large number of classes, these existing techniques can take an extended period of time to accurately classify an incoming sketch and require significant computational overhead. Thus, to make classification of large datasets feasible, we propose using multiple stages of recognition. In the initial stage, gesture-based feature values are calculated and the trained model is used to classify the incoming sketch. Sketches with an accuracy less than a threshold value, go through a second stage of geometric recognition techniques. In the second geometric stage, the sketch is segmented, and sent to shape-specific recognizers. The sketches are matched against predefined shape descriptions, and confidence values are calculated. The system outputs a list of classes that the sketch could be classified as, along with the accuracy, and precision for each sketch. This process both significantly reduces the time taken to classify such huge datasets of sketches, and increases both the accuracy and precision of the recognition