Evaluation of six night vision enhancement systems: qualitative and quantitative support for intelligent image processing

OBJECTIVE: An evaluation study was conducted to answer the question of which system properties of night vision enhancement systems (NVESs) provide a benefit for drivers without increasing their workload. BACKGROUND: Different infrared sensor, image processing, and display technologies can be integrated into an NVES to support nighttime driving. Because each of these components has its specific strengths and weaknesses, careful testing is required to determine their best combination. METHOD: Six prototypical systems were assessed in two steps. First, a heuristic evaluation with experts from ergonomics, perception, and traffic psychology was conducted. It produced a broad overview of possible effects of system properties on driving. Based on these results, an experimental field study with 15 experienced drivers was performed. Criteria used to evaluate the development potential of the six prototypes were the usability dimensions of effectiveness, efficiency, and user satisfaction (International Organization for Standardization, 1998). RESULTS: Results showed that the intelligibility of information, the easiness with which obstacles could be located in the environment, and the position of the display presenting the output of the system were of crucial importance for the usability of the NVES and its acceptance. Conclusion: All relevant requirements are met best by NVESs that are positioned at an unobtrusive location and are equipped with functions for the automatic identification of objects and for event-based warnings. APPLICATION: These design recommendations and the presented approach to evaluate the systems can be directly incorporated into the development process of future NVESs.     

Stereo‐Consistent Contours in Object Space

Notebook scribbles, art or technical illustrations—line drawings are a simplistic method to visually communicate information. Automated line drawings often originate from virtual 3D models, but one cannot trivially experience their three‐dimensionality. This paper introduces a novel concept to produce stereo‐consistent line drawings of virtual 3D objects. Some contour lines do not only depend on an objects geometry, but also on the position of the observer. To accomplish consistency between multiple view positions, our approach exploits geometrical characteristics of 3D surfaces in object space. Established techniques for stereo‐consistent line drawings operate on rendered pixel images. In contrast, our pipeline operates in object space using vector geometry, which yields many advantages: The position of the final viewpoint(s) is flexible within a certain window even after the contour generation, e.g. a stereoscopic image pair is only one possible application. Such windows can be concatenated to simulate contours observed from an arbitrary camera path. Various types of popular contour generators can be handled equivalently, occlusions are natively supported and stylization based on geometry characteristics is also easily possible.     

Effects of task experience and layout on learning from text and pictures with or without unnecessary picture descriptions

The presentation of extraneous (i.e., irrelevant or unnecessary) information may hamper learning with multimedia. The present study examined whether people can learn to ignore unnecessary information with increasing experience with the task and whether this depends on the layout of that information. In two experiments, participants learned about the process of mitosis from a multimedia slideshow, with each slide presenting a combination of expository text and a picture on one of the stages in the process. Slides either contained no unnecessary text (control condition) or unnecessary text (i.e., merely describing the picture) either integrated in the picture (integrated condition) or presented underneath the picture (separated condition). Knowledge about the studied mitosis phase was tested immediately after each slide using a cloze test. Across Experiments 1 and 2, we did not find a reliable negative effect of the unnecessary text on cloze test performance. As a result, the question of whether task experience would reduce or eliminate that negative effect could not be answered. The eye movement data did confirm, however, that participants attended less to the unnecessary information with increasing task experience, suggesting that students can adapt their study strategy and learn to ignore unnecessary information.     

Human Intelligence in the Process of Semantic Content Creation

Despite significant progress over the last years the large-scale adoption of semantic technologies is still to come. One of the reasons for this state of affairs is assumed to be the lack of useful semantic content, a prerequisite for almost every IT system or application using semantics. Through its very nature, this content can not be created fully automatically, but requires, to a certain degree, human contribution. The interest of Internet users in semantics, and in particular in creating semantic content, is, however, low. This is understandable if we think of several characteristics exposed by many of the most prominent semantic technologies, and the applications thereof. One of these characteristics is the high barrier of entry imposed. Interacting with semantic technologies today requires specific skills and expertise on subjects which are not part of the mainstream IT knowledge portfolio. A second characteristic are the incentives that are largely missing in the design of most semantic applications. The benefits of using machine-understandable content are in most applications fully decoupled from the effort of creating and maintaining this content. In other words, users do not have a motivation to contribute to the process. Initiatives in the areas of the Social Semantic Web acknowledged this problem, and identified mechanisms to motivate users to dedicate more of their time and resources to participate in the semantic content creation process. Still, even if incentives are theoretically in place, available human labor is limited and must only be used for those tasks that are heavily dependent on human intervention, and cannot be reliably automated. In this article, we concentrate on this step in between. As a first contribution, we analyze the process of semantic content creation in order to identify those tasks that are inherently human-driven. When building semantic applications involving these specific tasks, one has to install incentive schemes that are likely to encourage users to perform exactly these tasks that crucially rely on manual input. As a second contribution of the article, we propose incentives or incentive-driven tools that can be used to increase user interest in semantic content creation tasks. We hope that our findings will be adopted as recommendations for establishing a fundamentally new form of design of semantic applications by the semantic technologies community.     

Derivation of Strictly Stable High Order Difference Approximations for Variable-Coefficient PDE

A new way of deriving strictly stable high order difference operators for partial differential equations (PDE) is demonstrated for the 1D convection diffusion equation with variable coefficients. The derivation is based on a diffusion term in conservative, i.e. self-adjoint, form. Fourth order accurate difference operators are constructed by mass lumping Galerkin finite element methods so that an explicit method is achieved. The analysis of the operators is confirmed by numerical tests. The operators can be extended to multi dimensions, as we demonstrate for a 2D example. The discretizations are also relevant for the Navier–Stokes equations and other initial boundary value problems that involve up to second derivatives with variable coefficients.     

AI and robotics in the European restaurant sector: Assessing potentials for process innovation in a high-contact service industry

Electronic Markets - The restaurant technology market is rapidly evolving and is transforming the restaurant business as a significant sector of tourism and hospitality. Enabled by artificial...     

The influence of text modality on learning with static and dynamic visualizations

In this study we investigated the influence of text modality on learning with static and dynamic visualizations in a dynamic domain, namely the physical principles underlying fish locomotion. A 2 × 2-design with type of visualization (static vs. dynamic) and text modality (spoken vs. written) as independent variables was used. Concerning learning outcomes, it was hypothesized that (1) learners presented with dynamic visualizations would outperform learners presented with static visualizations, (2) learners presented with spoken text would outperform learners presented with written text, and (3) an interaction between type of visualization and modality would occur: the superiority of dynamic over static visualizations was expected to be more pronounced for spoken compared to written text. Subjective cognitive load measures were assessed and expected to mirror the aforementioned pattern of learning outcomes in accordance with Cognitive Load Theory (i.e., higher extraneous cognitive load (ECL) related to lower learning outcomes). For transfer tasks, the first two hypotheses could be confirmed. However, there was no interaction. Moreover, ECL was rated higher by subjects when learning with static compared to dynamic visualizations, but there were no differences for ECL with respect to the text modality. The results are discussed within the framework of Cognitive Load Theory.