Fidelity metrics for virtual environment simulations based on spatial memory awareness states

This paper describes a methodology based on human judgments of memory awareness states for assessing the simulation fidelity of a virtual environment (VE) in relation to its real scene counterpart. To demonstrate the distinction between task performance-based approaches and additional human evaluation of cognitive awareness states, a photorealistic VE was created. Resulting scenes displayed on a headmounted display (HMD) with or without head tracking and desktop monitor were then compared to the real-world task situation they represented, investigating spatial memory after exposure. Participants described how they completed their spatial recollections by selecting one of four choices of awareness states after retrieval in an initial test and a retention test a week after exposure to the environment. These reflected the level of visual mental imagery involved during retrieval, the familiarity of the recollection and also included guesses, even if informed. Experimental results revealed variations in the distribution of participants’ awareness states across conditions while, in certain cases, task performance failed to reveal any. Experimental conditions that incorporated head tracking were not associated with visually induced recollections. Generally, simulation of task performance does not necessarily lead to simulation of the awareness states involved when completing a memory task. The general premise of this research focuses on how tasks are achieved, rather than only on what is achieved. The extent to which judgments of human memory recall, memory awareness states, and presence in the physical and VE are similar provides a fidelity metric of the simulation in question

Reconstructing Surfaces Using Anisotropic Basis Functions

Point sets obtained from computer vision techniques are often noisy and non-uniform. We present a new method of surface reconstruction that can handle such data sets using anisotropic basis functions. Our reconstruction algorithm draws upon the work in variational implicit surfaces for constructing smooth and seamless 3D surfaces. Implicit functions are often formulated as a sum of weighted basis functions that are radially symmetric. Using radially symmetric basis functions inherently assumes, however that the surface to be reconstructed is, everywhere, locally symmetric. Such an assumption is true only at planar regions, and hence, reconstruction using isotropic basis is insufficient to recover objects that exhibit sharp features. We preserve sharp features using anisotropic basis that allow the surface to vary locally. The reconstructed surface is sharper along edges and at corner points. We determine the direction of anisotropy at a point by performing principal component analysis of the data points in a small neighborhood. The resulting field of principle directions across the surface is smoothed through tensor filtering. We have applied the anisotropic basis functions to reconstruct surfaces from noisy synthetic 3D data and from real range data obtained from space carving

Reconstructing Surfaces by Volumetric Regularization Using Radial Basis Functions

We present a new method of surface reconstruction that generates smooth and seamless models from sparse, noisy, nonuniform, and low resolution range data. Data acquisition techniques from computer vision, such as stereo range images and space carving, produce 3D point sets that are imprecise and nonuniform when compared to laser or optical range scanners. Traditional reconstruction algorithms designed for dense and precise data do not produce smooth reconstructions when applied to vision-based data sets. Our method constructs a 3D implicit surface, formulated as a sum of weighted radial basis functions. We achieve three primary advantages over existing algorithms: (1) the implicit functions we construct estimate the surface well in regions where there is little data, (2) the reconstructed surface is insensitive to noise in data acquisition because we can allow the surface to approximate, rather than exactly interpolate, the data, and (3) the reconstructed surface is locally detailed, yet globally smooth, because we use radial basis functions that achieve multiple orders of smoothness

Automatically learning structural units in educational videos with the hierarchical hidden Markov models

In this paper we present a coherent approach using the hierarchical HMM with shared structures to extract the structural units that form the building blocks of an education/training video. Rather than using hand-crafted approaches to define the structural units, we use the data from nine training videos to learn the parameters of the HHMM, and thus naturally extract the hierarchy. We then study this hierarchy and examine the nature of the structure at different levels of abstraction. Since the observable is continuous, we also show how to extend the parameter learning in the HHMM to deal with continuous observations

The sweet smell of success: Enhancing multimedia applications with olfaction

This is the Post-Print version of the Article. The official published version can be accessed from the link below - Copyright @ 2012 ACMOlfaction, or smell, is one of the last challenges which multimedia applications have to conquer. As far as computerized smell is concerned, there are several difficulties to overcome, particularly those associated with the ambient nature of smell. In this article, we present results from an empirical study exploring users' perception of olfaction-enhanced multimedia displays. Findings show that olfaction significantly adds to the user multimedia experience. Moreover, use of olfaction leads to an increased sense of reality and relevance. Our results also show that users are tolerant of the interference and distortion effects caused by olfactory effect in multimedia

AdaBoost.MRF: boosted Markov random forests and application to multilevel activity recognition

Activity recognition is an important issue in building intelligent monitoring systems. We address the recognition of multilevel activities in this paper via a conditional Markov random field (MRF), known as the dynamic conditional random field (DCRF). Parameter estimation in general MRFs using maximum likelihood is known to be computationally challenging (except for extreme cases), and thus we propose an efficient boosting-based algorithm AdaBoost.MRF for this task. Distinct from most existing work, our algorithm can handle hidden variables (missing labels) and is particularly attractive for smarthouse domains where reliable labels are often sparsely observed. Furthermore, our method works exclusively on trees and thus is guaranteed to converge. We apply the AdaBoost.MRF algorithm to a home video surveillance application and demonstrate its efficacy