Smell and touch in the Virtual Jumpcube

The Virtual Jumpcube is a virtual reality setup from 2015 that allows for jumping and flying in audiovisual virtual environments. Recently, we have included several haptic and olfactory stimuli that should further increase the degree of immersion in the experienced virtuality. These additional media channels were tested by the participants of several events and the feedback of 196 jumpers was gathered in a questionnaire. In this paper, we describe the stimulation hardware and software as well as the performed experiment and we present the major findings of the evaluation. It shows that if employed correctly, haptic and olfactory stimuli can enhance immersion and user experience significantly. Major success factors appear to be the amplitude and frequency of stimulation as well as the temporal synchronization with the other media channels, in particular the visual stimuli

DOI 10.1007/s11042-007-0106-y Evaluation of content-based image descriptors by statistical methods

Abstract Evaluation of visual information retrieval systems is usually performed by executing test queries and computing recall- and precision-like measures based on predefined media collections and ground truth information. This process is complex and time consuming. For the evaluation of feature transformations (transformation of visual media objects to feature vectors) it would be desirable to have simpler methods available as well. In this paper we introduce a supplementary evaluation procedure for features that is founded on statistical data analysis. A second novelty is that we make use of the existing visual MPEG-7 descriptors to judge the characteristics of feature transformations. The proposed procedure is divided into four steps: (1) feature extraction, (2) merging with MPEG-7 data and normalisation, (3) statistical data analysis and (4) visualisation and interpretation. Three types of statistical methods are used for evaluation: (1) univariate description (moments, etc.), (2) identification of similarities between feature elements (e.g. cluster analysis) and (3) identification of dependencies between variables (e.g. factor analysis). Statistical analysis provides beneficial insights into the structure of features that can be exploited for feature redesign. Application and advantages of the proposed approach are shown in a number of toy examples