New Model for VDT Associated Visual Comfort in Office Spaces

In office rooms a significant aspect of visual discomfort is related to visual quality of computer screens and it\u27s necessary to consider this visual quality as an important subject in quality of design. In this study a new method was developed to predict the visual quality of computer screen which is based on contrast reduction due to reflection. The new developed contrast model which is the basis of the new developed evaluation method is named the model of minimum required contrast

A Perceptual Color-Matching Method for Examining Color Blending in Augmented Reality Head-Up Display Graphics

Augmented reality (AR) offers new ways to visualize information on-the-go. As noted in related work, AR graphics presented via optical see-through AR displays are particularly prone to color blending, whereby intended graphic colors may be perceptually altered by real-world backgrounds, ultimately degrading usability. This work adds to this body of knowledge by presenting a methodology for assessing AR interface color robustness, as quantitatively measured via shifts in the CIE color space, and qualitatively assessed in terms of users’ perceived color name. We conducted a human factors study where twelve participants examined eight AR colors atop three real-world backgrounds as viewed through an in-vehicle AR head-up display (HUD); a type of optical see-through display used to project driving-related information atop the forward-looking road scene. Participants completed visual search tasks, matched the perceived AR HUD color against the WCS color palette, and verbally named the perceived color. We present analysis that suggests blue, green, and yellow AR colors are relatively robust, while red and brown are not, and discuss the impact of chromaticity shift and dispersion on outdoor AR interface design. While this work presents a case study in transportation, the methodology is applicable to a wide range of AR displays in many application domains and settings

Understanding, Modeling, and Simulating the Discrepancy Between Intended and Perceived Image Appearance on Optical See-Through Augmented Reality Displays

Augmented reality (AR) displays are transitioning from being primarily used in research and development settings, to being used by the general public. With this transition, these displays will be used by more people, in many different environments, and in many different contexts. Like other displays, the user\u27s perception of virtual imagery is influenced by the characteristics of the user\u27s environment, creating a discrepancy between the intended appearance and the perceived appearance of virtual imagery shown on the display. However, this problem is much more apparent for optical see-through AR displays, such as the HoloLens. For these displays, imagery is superimposed onto the user\u27s view of their environment, which can cause the imagery to become transparent and washed out in appearance from the user\u27s perspective. Any change in the user\u27s environment conditions or in the user\u27s position introduces changes to the perceived appearance of the AR imagery, and current AR displays do not adapt to maintain a consistent perceived appearance of the imagery being displayed. Because of this, in many environments the user may misinterpret or fail to notice information shown on the display. In this dissertation, I investigate the factors that influence user perception of AR imagery and demonstrate examples of how the user\u27s perception is affected for applications involving user interfaces, attention cues, and virtual humans. I establish a mathematical model that relates the user, their environment, their AR display, and AR imagery in terms of luminance or illuminance contrast. I demonstrate how this model can be used to classify the user\u27s viewing conditions and identify problems the user is prone to experience when in these conditions. I demonstrate how the model can be used to simulate changes in the user\u27s viewing conditions and to identify methods to maintain the perceived appearance of the AR imagery in changing conditions

A Survey of the State of Research on Augmented Reality from a Business Perspective using Porter’s Value Chain

In recent years, augmented reality (AR) technology has been able to demonstrate more and more impressively the potential it brings for companies and their valueadding activities, and this even though acceptance of the technology in society is only just beginning. Due to this, our work aims to bring a comprehensive overview of AR deployment opportunities based on the value chain, forcing a symbiosis of potential demonstration and acceptance promotion. For our investigation, we consider the most important peer-reviewed papers on the state of research on augmented reality from a business perspective and provide a comprehensive overview of the different possible uses of AR within a company, structured according to Porter’s value chain, as well as an outlook on future research on the expansion and further development of AR systems. Based on this, we formulate research gaps for future work on AR in the context presented

Virtual monitors vs. physical monitors: an empirical comparison for productivity work

Virtual monitors can display information through a head-worn display when a physical monitor is unavailable or provides insufficient space. Low resolution and restricted field of view are common issues of these displays. Such issues reduce readability and peripheral vision, leading to increased head movement when we increase the display size. This work evaluates the performance and user experience of a virtual monitor setup that combines software designed to minimize graphical transformations and a high-resolution virtual reality head-worn display. Participants performed productivity work across three approaches: Workstation, which is often used at office locations and consists of three side-by-side physical monitors; Laptop, which is often used in mobile locations and consists of a single physical monitor expanded with multiple desktops; and Virtual, our prototype with three side-by-side virtual monitors. Results show that participants deemed Virtual faster, easier to use, and more intuitive than Laptop, evidencing the advantages of head and eye glances over full content switches. They also confirm the existence of a gap between Workstation and Virtual, as Workstation achieved the highest user experience. We conclude with design guidelines obtained from the lessons learned in this study

Multimodal representation learning with neural networks

Abstract: Representation learning methods have received a lot of attention by researchers and practitioners because of their successful application to complex problems in areas such as computer vision, speech recognition and text processing [1]. Many of these promising results are due to the development of methods to automatically learn the representation of complex objects directly from large amounts of sample data [2]. These efforts have concentrated on data involving one type of information (images, text, speech, etc.), despite data being naturally multimodal. Multimodality refers to the fact that the same real-world concept can be described by different views or data types. Addressing multimodal automatic analysis faces three main challenges: feature learning and extraction, modeling of relationships between data modalities and scalability to large multimodal collections [3, 4]. This research considers the problem of leveraging multiple sources of information or data modalities in neural networks. It defines a novel model called gated multimodal unit (GMU), designed as an internal unit in a neural network architecture whose purpose is to find an intermediate representation based on a combination of data from different modalities. The GMU learns to decide how modalities influence the activation of the unit using multiplicative gates. The GMU can be used as a building block for different kinds of neural networks and can be seen as a form of intermediate fusion. The model was evaluated on four supervised learning tasks in conjunction with fully-connected and convolutional neural networks. We compare the GMU with other early and late fusion methods, outperforming classification scores in the evaluated datasets. Strategies to understand how the model gives importance to each input were also explored. By measuring correlation between gate activations and predictions, we were able to associate modalities with classes. It was found that some classes were more correlated with some particular modality. Interesting findings in genre prediction show, for instance, that the model associates the visual information with animation movies while textual information is more associated with drama or romance movies. During the development of this project, three new benchmark datasets were built and publicly released. The BCDR-F03 dataset which contains 736 mammography images and serves as benchmark for mass lesion classification. The MM-IMDb dataset containing around 27000 movie plots, poster along with 50 metadata annotations and that motivates new research in multimodal analysis. And the Goodreads dataset, a collection of 1000 books that encourages the research on success prediction based on the book content. This research also facilitates reproducibility of the present work by releasing source code implementation of the proposed methods.Doctorad

Flat Panel Displays in an Automotive Environment

A study was made of the field of flat panel displays, and their potential application in an automotive environment. Using contemporary display technology, semiconductors and software, a model was developed to fit an existing automotive instrumentation application. The resulting model was critically assessed in respect to the demands of such an application in respect to existing instrumentation methods. The viability and suitability of implementing such a design are discussed as well as its ability to be intrinsically portable and adaptable

Engineering data compendium. Human perception and performance. User's guide

The concept underlying the Engineering Data Compendium was the product of a research and development program (Integrated Perceptual Information for Designers project) aimed at facilitating the application of basic research findings in human performance to the design and military crew systems. The principal objective was to develop a workable strategy for: (1) identifying and distilling information of potential value to system design from the existing research literature, and (2) presenting this technical information in a way that would aid its accessibility, interpretability, and applicability by systems designers. The present four volumes of the Engineering Data Compendium represent the first implementation of this strategy. This is the first volume, the User's Guide, containing a description of the program and instructions for its use

Human factors aspects of control room design: Guidelines and annotated bibliography

A human factors analysis of the workstation design for the Earth Radiation Budget Satellite mission operation room is discussed. The relevance of anthropometry, design rules, environmental design goals, and the social-psychological environment are discussed