This study investigates the effects of fruits and vegetables (FaVs) abnormality on consumer perceptions and purchasing behavior. For the purposes of this study, a virtual grocery store was created with a fresh FaVs section, where 142 participants became immersed using an Oculus Rift DK2 Head-Mounted Display (HMD) software. Participants were presented either "normal", "slightly" misshapen, "moderately" misshapen or "severely" misshapen" FaVs. The study findings indicate that shoppers tend to purchase a similar number of FaVs whatever their level of deformity. However, perceptions of the appearance and quality of the FaVs depend on the degree of abnormality. "Moderately" misshapen FaVs are perceived as significantly better than those that are "heavily" misshapen but also "slightly" misshapen (except for the appearance of fruits).
International audienceUrban fabric characterization is very useful in urban design, planning, modeling and simulation. It is traditionally considered as a descriptive task mainly based on visual inspection of urban plans. Cartographic databases and geographic information systems (GIS) capabilities make possible the analytical formalization of this issue. This paper proposes a renewed approach to characterize urban fabrics using buildings' footprints data. This characterization method handles both architectural form and urban open space morphology since urban space can be intuitively and simply divided into built-up areas (buildings) and non-built-up areas (open spaces). First, we propose to build a mesh of the open space (a morphologic tessellation) and then we formalize relevant urban morphology properties and translate them into a set of indicators (using some common-used indispensable indicators and proposing a new formulation or generalization of a few others). This first step produces a highly dimensional data set for each footprint characterizing both the building and its surrounding open space. This data set is then reduced and classified using a spatial clustering process, the self-organizing maps in this case. Our method only requires buildings' footprints as input data. It can be applied on huge datasets and is independent from urban contexts. The results show that the classification produced is more faithful to ground truth (highlighting the variety of urban morpho-logic structures) than traditional descriptive characterizations generally lacking open space properties
Figure 1: Overview of the experimental setup. The participant had his head on a chinrest and was able to see virtual elements with an OST device or a RPD. He was asked to point a target (virtual or real) with his finger (wearing a tracked ring). The frame was covered by white panels to prevent the participant from seeing his hand. (Here, one panel is transparent for illustration purpose).
Projectors are important display devices for large scale augmented reality applications. However, precisely calibrating projectors with large focus distances implies a trade-off between practicality and accuracy. People either need a huge calibration board or a precise 3D model . In this paper, we present a practical projectorcamera calibration method to solve this problem. The user only needs a small calibration board to calibrate the system regardless of the focus distance of the projector. Results show that the rootmean-squared re-projection error (RMSE) for a 450cm projection distance is only about 4mm, even though it is calibrated using a small B4 (250 × 353mm) calibration board.
Kingdom. She holds DPhil in Management Studies (Marketing) from the University of Oxford. Her current research is focused on issues related to consumer behavior, digital marketing, retailing and advertising. Her research has been published in journals, such as the
In recent years Augmented Reality (AR) has become more and more popular, especially since the availability of mobile devices, such as smartphones or tablets, brought AR into our everyday life. Although the AR community has not yet agreed on a formal definition of AR, some work focused on proposing classifications of existing AR methods or applications. Such applications cover a wide variety of technologies, devices and goals, consequently existing taxonomies rely on multiple classification criteria that try to take into account AR applications diversity. In this paper we review existing taxonomies of augmented reality applications and we propose our own, which is based on (1) the number of degrees of freedom required by the application, as well as on (2) the visualization mode used, (3) the temporal base of the displayed content and (4) the rendering modalities used in the application. Our taxonomy covers location-based services as well as more traditional visionbased AR applications. Although AR is mainly based on the visual sense, other rendering modalities are also covered by the same degree-of-freedom criterion in our classification.
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