Computer-Assisted Interactive Documentary and Performance Arts in Illimitable Space

This major component of the research described in this thesis is 3D computer graphics, specifically the realistic physics-based softbody simulation and haptic responsive environments. Minor components include advanced human-computer interaction environments, non-linear documentary storytelling, and theatre performance. The journey of this research has been unusual because it requires a researcher with solid knowledge and background in multiple disciplines; who also has to be creative and sensitive in order to combine the possible areas into a new research direction. [...] It focuses on the advanced computer graphics and emerges from experimental cinematic works and theatrical artistic practices. Some development content and installations are completed to prove and evaluate the described concepts and to be convincing. [...] To summarize, the resulting work involves not only artistic creativity, but solving or combining technological hurdles in motion tracking, pattern recognition, force feedback control, etc., with the available documentary footage on film, video, or images, and text via a variety of devices [....] and programming, and installing all the needed interfaces such that it all works in real-time. Thus, the contribution to the knowledge advancement is in solving these interfacing problems and the real-time aspects of the interaction that have uses in film industry, fashion industry, new age interactive theatre, computer games, and web-based technologies and services for entertainment and education. It also includes building up on this experience to integrate Kinect- and haptic-based interaction, artistic scenery rendering, and other forms of control. This research work connects all the research disciplines, seemingly disjoint fields of research, such as computer graphics, documentary film, interactive media, and theatre performance together.Comment: PhD thesis copy; 272 pages, 83 figures, 6 algorithm

Machine Learning Approaches to Human Body Shape Analysis

Soft biometrics, biomedical sciences, and many other fields of study pay particular attention to the study of the geometric description of the human body, and its variations. Although multiple contributions, the interest is particularly high given the non-rigid nature of the human body, capable of assuming different poses, and numerous shapes due to variable body composition. Unfortunately, a well-known costly requirement in data-driven machine learning, and particularly in the human-based analysis, is the availability of data, in the form of geometric information (body measurements) with related vision information (natural images, 3D mesh, etc.). We introduce a computer graphics framework able to generate thousands of synthetic human body meshes, representing a population of individuals with stratified information: gender, Body Fat Percentage (BFP), anthropometric measurements, and pose. This contribution permits an extensive analysis of different bodies in different poses, avoiding the demanding, and expensive acquisition process. We design a virtual environment able to take advantage of the generated bodies, to infer the body surface area (BSA) from a single view. The framework permits to simulate the acquisition process of newly introduced RGB-D devices disentangling different noise components (sensor noise, optical distortion, body part occlusions). Common geometric descriptors in soft biometric, as well as in biomedical sciences, are based on body measurements. Unfortunately, as we prove, these descriptors are not pose invariant, constraining the usability in controlled scenarios. We introduce a differential geometry approach assuming body pose variations as isometric transformations of the body surface, and body composition changes covariant to the body surface area. This setting permits the use of the Laplace-Beltrami operator on the 2D body manifold, describing the body with a compact, efficient, and pose invariant representation. We design a neural network architecture able to infer important body semantics from spectral descriptors, closing the gap between abstract spectral features, and traditional measurement-based indices. Studying the manifold of body shapes, we propose an innovative generative adversarial model able to learn the body shapes. The method permits to generate new bodies with unseen geometries as a walk on the latent space, constituting a significant advantage over traditional generative methods

Proceedings of the 1st Doctoral Consortium at the European Conference on Artificial Intelligence (DC-ECAI 2020)

1st Doctoral Consortium at the European Conference on Artificial Intelligence (DC-ECAI 2020), 29-30 August, 2020 Santiago de Compostela, SpainThe DC-ECAI 2020 provides a unique opportunity for PhD students, who are close to finishing their doctorate research, to interact with experienced researchers in the field. Senior members of the community are assigned as mentors for each group of students based on the student’s research or similarity of research interests. The DC-ECAI 2020, which is held virtually this year, allows students from all over the world to present their research and discuss their ongoing research and career plans with their mentor, to do networking with other participants, and to receive training and mentoring about career planning and career option

Geostatistical integration of geophysical, well bore and outcrop data for flow modeling of a deltaic reservoir analogue

Significant world oil and gas reserves occur in deltaic reservoirs. Characterization of deltaic reservoirs requires understanding sedimentary and diagenetic heterogeneity at the submeter scale in three dimensions. However, deltaic facies architecture is complex and poorly understood. Moreover, precipitation of extensive calcite cement during diagenesis can modify the depositional permeability of sandstone reservoir and affect fluid flow. Heterogeneity contributes to trapping a significant portion of mobile oil in deltaic reservoirs analogous of Cretaceous Frontier Formation, Powder River Basin, Wyoming. This dissertation focuses on 3D characterization of an ancient deltaic lobe. The Turonian Wall Creek Member in central Wyoming has been selected for the present study, which integrates outcrop digitized image analysis, 2D and 3D interpreted ground penetrating radar surveys, outcrop gamma ray measurements, well logs, permeameter logs and transects, and other data for 3D reservoir characterization and flow modeling. Well log data are used to predict the geological facies using beta-Bayes method and classic multivariate statistic methods, and predictions are compared with the outcrop description. Geostatistical models are constructed for the size, orientation, and shape of the concretions using interpreted GPR, well, and outcrop data. The spatial continuity of concretions is quantified using photomosaic derived variogram analysis. Relationships among GRP attributes, well data, and outcrop data are investigated, including calcite concretion occurrence and permeability measurements from outcrop. A combination of truncated Gaussian simulation and Bayes rule predicts 3D concretion distributions. Comparisons between 2D flow simulations based on outcrop observations and an ensemble of geostatistical models indicates that the proposed approach can reproduce essential aspects of flow behavior in this system. Experimental design, analysis of variance, and flow simulations examine the effects of geological variability on breakthrough time, sweep efficiency and upscaled permeability. The proposed geostatistical and statistical methods can improve prediction of flow behavior even if conditioning data are sparse and radar data are noisy. The derived geostatistical models of stratigraphy, facies and diagenesis are appropriate for analogous deltaic reservoirs. Furthermore, the results can guide data acquisition, improve performance prediction, and help to upscale models

Digital Image Access & Retrieval

The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio

Proceedings of the 2009 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory

The joint workshop of the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB, Karlsruhe, and the Vision and Fusion Laboratory (Institute for Anthropomatics, Karlsruhe Institute of Technology (KIT)), is organized annually since 2005 with the aim to report on the latest research and development findings of the doctoral students of both institutions. This book provides a collection of 16 technical reports on the research results presented on the 2009 workshop

Data Mining in Social Networks

The objective of the study is to examine the idea of Big Data and its applications in data mining. The data in the universe is expanding step by step every year and turns into large data. These significant data can be determined to utilize a few data mining undertakings. In short, Big Data can be called as an “asset” and data mining is a technique that is employed to give useful results. This paper implements an HACE algorithm that analysis the structure of big data and presents an efficient data mining technique. This framework model incorporates a mixture of information sources, mining techniques, customer interest, security, and data protection system. The study also analyzes and presents the challenges and issues faced in the big data model