Interactive virtual probing of 4D MRI blood-flow

Better understanding of hemodynamics conceivably leads to improved diagnosis and prognosis of cardiovascular diseases. Therefore, an elaborate analysis of the blood-flow in heart and thoracic arteries is essential. Contemporary MRI techniques enable acquisition of quantitative time-resolved flow information, resulting in 4D velocity fields that capture the blood-flow behavior. Visual exploration of these fields provides comprehensive insight into the unsteady blood-flow behavior, and precedes a quantitative analysis of additional blood-flow parameters. The complete inspection requires accurate segmentation of anatomical structures, encompassing a time-consuming and hard-to-automate process, especially for malformed morphologies. We present a way to avoid the laborious segmentation process in case of qualitative inspection, by introducing an interactive virtual probe. This probe is positioned semi-automatically within the blood-flow field, and serves as a navigational object for visual exploration. The difficult task of determining position and orientation along the view-direction is automated by a fitting approach, aligning the probe with the orientations of the velocity field. The aligned probe provides an interactive seeding basis for various flow visualization approaches. We demonstrate illustration-inspired particles, integral lines and integral surfaces, conveying distinct characteristics of the unsteady blood-flow. Lastly, we present the results of an evaluation with domain experts, valuing the practical use of our probe and flow visualization techniques.     

Integrating perceptual level of detail with head-pose estimation and its uncertainty

Immersive virtual environments with life-like interaction capabilities can provide a high fidelity view of the virtual world and seamless interaction methods to the user. High demanding requirements, however, raise many challenges in the development of sensing technologies and display systems. The focus of this study is on improving the performance of human–computer interaction by rendering optimizations guided by head pose estimates and their uncertainties. This work is part of a larger study currently being under investigation at NASA Ames, called “Virtual GloveboX” (VGX). VGX is a virtual simulator that aims to provide advanced training and simulation capabilities for astronauts to perform precise biological experiments in a glovebox aboard the International Space Station (ISS). Our objective is to enhance the virtual experience by incorporating information about the user’s viewing direction into the rendering process. In our system, viewing direction is approximated by estimating head orientation using markers placed on a pair of polarized eye-glasses. Using eye-glasses does not pose any constraints in our operational environment since they are an integral part of a stereo display used in VGX. During rendering, perceptual level of detail methods are coupled with head-pose estimation to improve the visual experience. A key contribution of our work is incorporating head pose estimation uncertainties into the level of detail computations to account for head pose estimation errors. Subject tests designed to quantify user satisfaction under different modes of operation indicate that incorporating uncertainty information during rendering improves the visual experience of the user.     

ServiceSs: An Interoperable Programming Framework for the Cloud

The rise of virtualized and distributed infrastructures has led to new challenges to accomplish the effective use of compute resources through the design and orchestration of distributed applications. As legacy, monolithic applications are replaced with service-oriented applications, questions arise about the steps to be taken in order to maximize the usefulness of the infrastructures and to provide users with tools for the development and execution of distributed applications. One of the issues to be solved is the existence of multiple cloud solutions that are not interoperable, which forces the user to be locked to a specific provider or to continuously adapt applications. With the objective of simplifying the programmers challenges, ServiceSs provides a straightforward programming model and an execution framework that helps on abstracting applications from the actual execution environment. This paper presents how ServiceSs transparently interoperates with multiple providers implementing the appropriate interfaces to execute scientific applications on federated clouds.     

Self-modeling in humanoid soccer robots

In this paper we discuss the applicability, potential benefits, open problems and expected contributions that an emerging set of self-modeling techniques might bring on the development of humanoid soccer robots. The idea is that robots might continuously generate, validate and adjust physical models of their sensorimotor interaction with the world. These models are exploited for adapting behavior in simulation, enhancing the learning skills of a robot with the regular transference of controllers developed in simulation to reality. Moreover, these simulations can be used to aid the execution of complex sensorimotor tasks, speed up adaptation and enhance task planning. We present experiments on the generation of behaviors for humanoid soccer robots using the Back-to-Reality algorithm. General motivations are presented, alternative algorithms are discussed and, most importantly, directions of research are proposed.     

Preface to the Special Issue on Human Pose,Motion, Activities and Shape in 3D

International Journal of Computer Vision -     

Cross-Approximate Entropy parallel computation on GPUs for biomedical signal analysis. Application to MEG recordings

Cross-Approximate Entropy (Cross-ApEn) is a useful measure to quantify the statistical dissimilarity of two time series. In spite of the advantage of Cross-ApEn over its one-dimensional counterpart (Approximate Entropy), only a few studies have applied it to biomedical signals, mainly due to its high computational cost. In this paper, we propose a fast GPU-based implementation of the Cross-ApEn that makes feasible its use over a large amount of multidimensional data. The scheme followed is fully scalable, thus maximizes the use of the GPU despite of the number of neural signals being processed. The approach consists in processing many trials or epochs simultaneously, with independence of its origin. In the case of MEG data, these trials can proceed from different input channels or subjects. The proposed implementation achieves an average speedup greater than 250× against a CPU parallel version running on a processor containing six cores. A dataset of 30 subjects containing 148 MEG channels (49 epochs of 1024 samples per channel) can be analyzed using our development in about 30 min. The same processing takes 5 days on six cores and 15 days when running on a single core. The speedup is much larger if compared to a basic sequential Matlab^® implementation, that would need 58 days per subject. To our knowledge, this is the first contribution of Cross-ApEn measure computation using GPUs. This study demonstrates that this hardware is, to the day, the best option for the signal processing of biomedical data with Cross-ApEn.     

Evolutionary discriminative confidence estimation for spoken term detection

Spoken term detection (STD) is the task of searching for occurrences of spoken terms in audio archives. It relies on robust confidence estimation to make a hit/false alarm (FA) decision. In order to optimize the decision in terms of the STD evaluation metric, the confidence has to be discriminative. Multi-layer perceptrons (MLPs) and support vector machines (SVMs) exhibit good performance in producing discriminative confidence; however they are severely limited by the continuous objective functions, and are therefore less capable of dealing with complex decision tasks. This leads to a substantial performance reduction when measuring detection of out-of-vocabulary (OOV) terms, where the high diversity in term properties usually leads to a complicated decision boundary. In this paper we present a new discriminative confidence estimation approach based on evolutionary discriminant analysis (EDA). Unlike MLPs and SVMs, EDA uses the classification error as its objective function, resulting in a model optimized towards the evaluation metric. In addition, EDA combines heterogeneous projection functions and classification strategies in decision making, leading to a highly flexible classifier that is capable of dealing with complex decision tasks. Finally, the evolutionary strategy of EDA reduces the risk of local minima. We tested the EDA-based confidence with a state-of-the-art phoneme-based STD system on an English meeting domain corpus, which employs a phoneme speech recognition system to produce lattices within which the phoneme sequences corresponding to the enquiry terms are searched. The test corpora comprise 11 h of speech data recorded with individual head-mounted microphones from 30 meetings carried out at several institutes including ICSI; NIST; ISL; LDC; the Virginia Polytechnic Institute and State University; and the University of Edinburgh. The experimental results demonstrate that EDA considerably outperforms MLPs and SVMs on both classification and confidence measurement in STD, and the advantage is found to be more significant on OOV terms than on in-vocabulary (INV) terms. In terms of classification performance, EDA achieved an equal error rate (EER) of 11% on OOV terms, compared to 34% and 31% with MLPs and SVMs respectively; for INV terms, an EER of 15% was obtained with EDA compared to 17% obtained with MLPs and SVMs. In terms of STD performance for OOV terms, EDA presented a significant relative improvement of 1.4% and 2.5% in terms of average term-weighted value (ATWV) over MLPs and SVMs respectively.