A New Calibration Process for a Homogeneous Cyclorama Illumination in Virtual TV Sets

A virtual TV set combines actors and objects with computer-generated virtual environments in real time. Nowadays, this technology is widely used in television broadcasts and cinema productions. A virtual TV set consists of three main elements: the stage, the computer-system and the chroma-keyer. The stage is composed by a monochrome cyclorama (the background) in front of which actors and objects are located (the foreground). The computer-system generates the virtual elements that will form the virtual environment. The chroma-keyer combines the elements in the foreground with the computer-generated environments by erasing the monochrome background and insetting the synthetic elements using the chroma-keying technique. In order to ease the background removal, the cyclorama illumination must be diffuse and homogeneous, avoiding the hue differences that are introduced by shadows, shines and over-lighted areas. The analysis of this illumination is usually performed manually by an expert using a photometer which makes the process slow, tedious and dependent on the experience of the operator. In this paper, a new calibration process to check and improve the homogeneity of a cyclorama’s illumination by non-experts using a custom software which provides both visual information and statistical data, is presented. This calibration process segments a cyclorama image in regions with similar luminance and calculates the centroid of each of them. The statistical study of the variation in the size of the regions and the position of the centroids are the key tools used to determine the homogeneity of the cyclorama lighting.This work has received financial support from the Consellería de Cultura, Educación e Ordenación Universitaria (accreditation 2016–2019, ED431G/08) and the European Regional Development Fund (ERDF)S

Dataremix: Aesthetic Experiences of Big Data and Data Abstraction

This PhD by published work expands on the contribution to knowledge in two recent large-scale transdisciplinary artistic research projects: ATLAS in silico and INSTRUMENT | One Antarctic Night and their exhibited and published outputs. The thesis reflects upon this practice-based artistic research that interrogates data abstraction: the digitization, datafication and abstraction of culture and nature, as vast and abstract digital data. The research is situated in digital arts practices that engage a combination of big (scientific) data as artistic material, embodied interaction in virtual environments, and poetic recombination. A transdisciplinary and collaborative artistic practice, x-resonance, provides a framework for the hybrid processes, outcomes, and contributions to knowledge from the research. These are purposefully and productively situated at the objective | subjective interface, have potential to convey multiple meanings simultaneously to a variety of audiences and resist disciplinary definition. In the course of the research, a novel methodology emerges, dataremix, which is employed and iteratively evolved through artistic practice to address the research questions: 1) How can a visceral and poetic experience of data abstraction be created? and 2) How would one go about generating an artistically-informed (scientific) discovery? Several interconnected contributions to knowledge arise through the first research question: creation of representational elements for artistic visualization of big (scientific) data that includes four new forms (genomic calligraphy, algorithmic objects as natural specimens, scalable auditory data signatures, and signal objects); an aesthetic of slowness that contributes an extension to the operative forces in Jevbratt’s inverted sublime of looking down and in to also include looking fast and slow; an extension of Corby’s objective and subjective image consisting of “informational and aesthetic components” to novel virtual environments created from big 3 (scientific) data that extend Davies’ poetic virtual spatiality to poetic objective | subjective generative virtual spaces; and an extension of Seaman’s embodied interactive recombinant poetics through embodied interaction in virtual environments as a recapitulation of scientific (objective) and algorithmic processes through aesthetic (subjective) physical gestures. These contributions holistically combine in the artworks ATLAS in silico and INSTRUMENT | One Antarctic Night to create visceral poetic experiences of big data abstraction. Contributions to knowledge from the first research question develop artworks that are visceral and poetic experiences of data abstraction, and which manifest the objective | subjective through art. Contributions to knowledge from the second research question occur through the process of the artworks functioning as experimental systems in which experiments using analytical tools from the scientific domain are enacted within the process of creation of the artwork. The results are “returned” into the artwork. These contributions are: elucidating differences in DNA helix bending and curvature along regions of gene sequences specified as either introns or exons, revealing nuanced differences in BLAST results in relation to genomics sequence metadata, and cross-correlation of astronomical data to identify putative variable signals from astronomical objects for further scientific evaluation

gEYEded: Subtle and Challenging Gaze-Based Player Guidance in Exploration Games

This paper investigates the effects of gaze-based player guidance on the perceived game experience, performance, and challenge in a first-person exploration game. In contrast to existing research, the proposed approach takes the game context into account by providing players not only with guidance but also granting them an engaging game experience with a focus on exploration. This is achieved by incorporating gaze-sensitive areas that indicate the location of relevant game objects. A comparative study was carried out to validate our concept and to examine if a game supported with a gaze guidance feature triggers a more immersive game experience in comparison to a crosshair guidance version and a solution without any guidance support. In general, our study findings reveal a more positive impact of the gaze-based guidance approach on the experience and performance in comparison to the other two conditions. However, subjects had a similar impression concerning the game challenge in all conditions

From rituals to magic: Interactive art and HCI of the past, present, and future

The connection between art and technology is much tighter than is commonly recognized. The emergence of aesthetic computing in the early 2000s has brought renewed focus on this relationship. In this article, we articulate how art and Human–Computer Interaction (HCI) are compatible with each other and actually essential to advance each other in this era, by briefly addressing interconnected components in both areas—interaction, creativity, embodiment, affect, and presence. After briefly introducing the history of interactive art, we discuss how art and HCI can contribute to one another by illustrating contemporary examples of art in immersive environments, robotic art, and machine intelligence in art. Then, we identify challenges and opportunities for collaborative efforts between art and HCI. Finally, we reiterate important implications and pose future directions. This article is intended as a catalyst to facilitate discussions on the mutual benefits of working together in the art and HCI communities. It also aims to provide artists and researchers in this domain with suggestions about where to go next

An Adaptive Multi-Level Quantization-Based Reinforcement Learning Model for Enhancing UAV Landing on Moving Targets

The autonomous landing of an unmanned aerial vehicle (UAV) on a moving platform is an essential functionality in various UAV-based applications. It can be added to a teleoperation UAV system or part of an autonomous UAV control system. Various robust and predictive control systems based on the traditional control theory are used for operating a UAV. Recently, some attempts were made to land a UAV on a moving target using reinforcement learning (RL). Vision is used as a typical way of sensing and detecting the moving target. Mainly, the related works have deployed a deep-neural network (DNN) for RL, which takes the image as input and provides the optimal navigation action as output. However, the delay of the multi-layer topology of the deep neural network affects the real-time aspect of such control. This paper proposes an adaptive multi-level quantization-based reinforcement learning (AMLQ) model. The AMLQ model quantizes the continuous actions and states to directly incorporate simple Q-learning to resolve the delay issue. This solution makes the training faster and enables simple knowledge representation without needing the DNN. For evaluation, the AMLQ model was compared with state-of-art approaches and was found to be superior in terms of root mean square error (RMSE), which was 8.7052 compared with the proportional-integral-derivative (PID) controller, which achieved an RMSE of 10.0592