3-D Interfaces for Spatial Construction

It is becoming increasingly easy to bring the body directly to digital form via stereoscopic immersive displays and tracked input devices. Is this space a viable one in which to construct 3d objects? Interfaces built upon two-dimensional displays and 2d input devices are the current standard for spatial construction, yet 3d interfaces, where the dimensionality of the interactive space matches that of the design space, have something unique to offer. This work increases the richness of 3d interfaces by bringing several new tools into the picture: the hand is used directly to trace surfaces; tangible tongs grab, stretch, and rotate shapes; a handle becomes a lightsaber and a tool for dropping simple objects; and a raygun, analagous to the mouse, is used to select distant things. With these tools, a richer 3d interface is constructed in which a variety of objects are created by novice users with relative ease. What we see is a space, not exactly like the traditional 2d computer, but rather one in which a distinct and different set of operations is easy and natural. Design studies, complemented by user studies, explore the larger space of three-dimensional input possibilities. The target applications are spatial arrangement, freeform shape construction, and molecular design. New possibilities for spatial construction develop alongside particular nuances of input devices and the interactions they support. Task-specific tangible controllers provide a cultural affordance which links input devices to deep histories of tool use, enhancing intuition and affective connection within an interface. On a more practical, but still emotional level, these input devices frame kinesthetic space, resulting in high-bandwidth interactions where large amounts of data can be comfortably and quickly communicated. A crucial issue with this interface approach is the tension between specific and generic input devices. Generic devices are the tradition in computing -- versatile, remappable, frequently bereft of culture or relevance to the task at hand. Specific interfaces are an emerging trend -- customized, culturally rich, to date these systems have been tightly linked to a single application, limiting their widespread use. The theoretical heart of this thesis, and its chief contribution to interface research at large is an approach to customization. Instead of matching an application domain's data, each new input device supports a functional class. The spatial construction task is split into four types of manipulation: grabbing, pointing, holding, and rubbing. Each of these action classes spans the space of spatial construction, allowing a single tool to be used in many settings without losing the unique strengths of its specific form. Outside of 3d interface, outside of spatial construction, this approach strikes a balance between generic and specific suitable for many interface scenarios. In practice, these specific function groups are given versatility via a quick remapping technique which allows one physical tool to perform many digital tasks. For example, the handle can be quickly remapped from a lightsaber that cuts shapes to tools that place simple platonic solids, erase portions of objects, and draw double-helices in space. The contributions of this work lie both in a theoretical model of spatial interaction, and input devices (combined with new interactions) which illustrate the efficacy of this philosophy. This research brings the new results of Tangible User Interface to the field of Virtual Reality. We find a space, in and around the hand, where full-fledged haptics are not necessary for users physically connect with digital form.</p

Spartan Daily, September 15, 1983

Volume 81, Issue 12https://scholarworks.sjsu.edu/spartandaily/7061/thumbnail.jp

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

3D Sketching for Interactive Model Retrieval in Virtual Reality

We describe a novel method for searching 3D model collections using free-form sketches within a virtual environment as queries. As opposed to traditional sketch retrieval, our queries are drawn directly onto an example model. Using immersive virtual reality the user can express their query through a sketch that demonstrates the desired structure, color and texture. Unlike previous sketch-based retrieval methods, users remain immersed within the environment without relying on textual queries or 2D projections which can disconnect the user from the environment. We perform a test using queries over several descriptors, evaluating the precision in order to select the most accurate one. We show how a convolutional neural network (CNN) can create multi-view representations of colored 3D sketches. Using such a descriptor representation, our system is able to rapidly retrieve models and in this way, we provide the user with an interactive method of navigating large object datasets. Through a user study we demonstrate that by using our VR 3D model retrieval system, users can perform search more quickly and intuitively than with a naive linear browsing method. Using our system users can rapidly populate a virtual environment with specific models from a very large database, and thus the technique has the potential to be broadly applicable in immersive editing systems

The Development of a Semantic Model for the Interpretation of Mathematics including the use of Technology

The semantic model developed in this research was in response to the difficulty a group of mathematics learners had with conventional mathematical language and their interpretation of mathematical constructs. In order to develop the model ideas from linguistics, psycholinguistics, cognitive psychology, formal languages and natural language processing were investigated. This investigation led to the identification of four main processes: the parsing process, syntactic processing, semantic processing and conceptual processing. The model showed the complex interdependency between these four processes and provided a theoretical framework in which the behaviour of the mathematics learner could be analysed. The model was then extended to include the use of technological artefacts into the learning process. To facilitate this aspect of the research, the theory of instrumentation was incorporated into the semantic model. The conclusion of this research was that although the cognitive processes were interdependent, they could develop at different rates until mastery of a topic was achieved. It also found that the introduction of a technological artefact into the learning environment introduced another layer of complexity, both in terms of the learning process and the underlying relationship between the four cognitive processes

Computational Analysis of Eye-Strain for Digital Screens based on Eye Tracking Studies

Computer vision syndrome (CVS) is composed of multiple eye vision problems due to the prolonged use of digital displays, including tablets and smartphones. These problems were shown to affect visual comfort as well as work productivity in both adults and teenagers. CVS causes eye and vision symptoms such as eye-strain, eye burn, dry eyes, double vision, and blurred vision. CVS, which causes severe vision and muscular problems due to repeated eye movements and excessive eye focus on computer screens, is a cause of work-related stress. In this thesis, we address this problem and present three general-purpose mathematical compound models for assessing eye-strain in eye-tracking applications, namely (1) Fixation-based Eye fatigue Load Index (FELiX), (2) Index of Difficulty for Eye-tracking Applications (IDEA), and (3) Eye-Strain Probation Model (ESPiM) based on eye-tracking parameters and subjective ratings to measure, predict, and compare the amount of fatigue or cognitive workload during target selection tasks for different user groups or interaction techniques. The ESPiM model is the outcome of both FELiX and IDEA, which benefit from direct subjective rating and, therefore, can be applied to assess the ESPiM model's efficacy. We present experiments and user studies that show that these models can measure potential eye-strain levels on individuals based on physical circumstances such as screen resolution and target positions per time

Bond Graph Modelling of Physical Systems

This thesis describes new methods for creating and analysing bond graph models of continuous physical systems. The concept of a core model representation is central to this research, since it is shown that the need to generate and maintain a range of models discourages the widespread use of modelling. Mathematical models appropriate to specific applications are not, in general, sufficiently comprehensive to be used as the core model representation, whereas all the models of interest for analysis and simulation may be derived from a bond graph model. Hierarchical model representations are shown to be an aid to reducing complexity, and thus the bond graph methodologies, which are developed, fully support hierarchical models. A new bond graph algorithm for identifying and solving algebraic loops is described, and extended to provide a steady-state model of the system. The new algorithm is shown to systematically create a differential algebraic equation (DAE) model of the system. Bond graph causality is shown to be a powerful analytical concept, but classical causal propagation algorithms have limitations which are discussed. These limitations are overcome by a novel computable causality approach, and its bicausal bond graph representation. The computable causality algorithm is used for resolving algebraic loops, and handling of modulations. The new concepts of unilateral bonds and bicausal bond graphs generalise the classical causality notation to permit physically unrealisable (but computationally useful) bond graph causalities. The computable causality algorithm provides a systematic method for deriving generalised state equation (or DAE) mathematical models from bicausal bond graphs. Practical applications of the new bond graph techniques are demonstrated through the analysis of four real physical systems as case studies. The implementation and operation of a DOS-based tool which uses bond graphs as the core model representation is described

Cognitive architecture for an Attention-based and Bidirectional Loop-closing Domain (CABILDO)

This Ph. D. Thesis presents a novel attention-based cognitive architecture for social robots. The architecture aims to join perception and reasoning considering a double and simultaneous imbrication: the ongoing task biases the perceptual process to obtain only useful elements whereas perceived items determine the behaviours to be accomplished. Therefore, the proposed architecture represents a bidirectional solution to the perception-reasoning-action loop closing problem. The basis of the architecture is an Object-Based Visual Attention model. This perception system draws attention over perceptual units of visual information, called proto-objects. In order to highlight relevant elements, not only several intrinsic basic features (such as colour, location or shape) but also the constraints provided by the ongoing behaviour and context are considered. The proposed architecture is divided into two levels of performance. The lower level is concerned with quantitative models of execution, namely tasks that are suitable for the current work conditions, whereas a qualitative framework that describes and defines tasks relationships and coverages is placed at the top level. Perceived items determine the tasks that can be executed in each moment, following a need-based approach. Thereby, the tasks that better fit the perceived environment are more likely to be executed. Finally, the cognitive architecture has been tested using a real and unrestricted scenario that involves a real robot, time-varying tasks and daily life situations, in order to demonstrate that the proposal is able to efficiently address time- and behaviour-varying environments, overcoming the main drawbacks of already existing models

The LANDSAT Tutorial Workbook: Basics of Satellite Remote Sensing

Most of the subject matter of a full training course in applying remote sensing is presented in a self-teaching mode in this how-to manual which combines a review of basics, a survey of systems, and a treatment of the principles and mechanics of image analysis by computers, with a laboratory approach for learning to utilize the data through practical experiences. All relevant image products are included