Jorge Moragues

Adaptive augmented reality for cultural heritage: ARtSENSE project

The paper presents the new concept of Adaptive Augmented Reality (A2R), employed within the context of the creation of an AR guide for the museum visit, that is being developed in the context of an EU research project. The main objective of the project is to provide a prototype that enables a personalized experience for every individual visitor by adapting to the psychological state of the visitor the content presented through an augmented reality museum guidance system.

Infrared image processing and its application to forest fire surveillance

This paper describes an scheme for automatic forest surveillance. A complete system for forest fire detection is firstly presented although we focus on infrared image processing. The proposed scheme based on infrared image processing performs early detection of any fire threat. With the aim of determining the presence or absence of fire, the proposed algorithms performs the fusion of different detectors which exploit different expected characteristics of a real fire, like persistence and increase. Theoretical results and practical simulations are presented to corroborate the control of the system related with probability of false alarm (PFA). Probability of detection (PD) dependence on signal to noise ration (SNR) is also evaluated.

Fast communication: Detection of signals of unknown duration by multiple energy detectors

An extension of the classical energy detector is proposed to deal with the case of unknown signal duration. Multiple energy detectors are applied to partitions of the original observation interval; presence of signal is decided if at least one of the detectors is in favor of it. We have derived the corresponding probabilities of false alarm and detection for a particular strategy of successive segmentations of the original interval, thus obtaining a layered structure of energy detectors. One key problem is that individual decisions obtained from the multiple energy detectors are statistically dependent, thus complicating the derivation of the overall probabilities of detection and false alarm. ROC curves have been computed, showing significant improvements in detectability when there is a large mismatch between the duration of the observation interval and the actual duration of the signal. This can be especially interesting in the framework of novelty detection where specific parameters of the signal, like duration, are totally unknown.

An extended energy detector for non-Gaussian and non-independent noise

Energy detectors are optimum to detect uncorrelated Gaussian signals or generalized likelihood ratio tests to detect completely unknown signals; in both cases, background noise must be uncorrelated Gaussian. However, energy detectors degrade when background noise is non-independent and non-Gaussian. An extension is presented in this paper to deal with this situation. Independence is achieved by means of a matrix linear transformation derived from independent component analysis. Non-Gaussianity is avoided by applying a scalar non-linear function to every element of the linearly transformed observation vector. Practical procedures for estimating the linear and nonlinear transformations are given in the paper. A SNR enhancement factor has been defined for the weak signal case, which is indicative of the expected improvement of the proposed extension. Some simulations illustrate the achieved improvements.

Acoustic detection and classification using temporal and frequency multiple energy detector features

The problem of acoustic detection and recognition is of particular interest in surveillance applications, especially in noisy environments with sound sources of different nature. Therefore, we present a multiple energy detector (MED) structure which is used to extract a new set of features for classification, called frequency MED (FMED) and combined MED (CMED). The focus of this paper is to compare these two novel feature sets with the commonly used MFCC and to evaluate their performance in a general sound classification task with different acoustic sources and adverse noise conditions. The promising results obtained show that, in low SNR, the proposed CMED features work significantly better than the MFCC.

Improving Detection of Acoustic Signals by Means of a Time and Frequency Multiple Energy Detector

Standard energy detectors (ED) are optimum to detect unknown signals in presence of uncorrelated Gaussian noise. However, in real applications the signal duration and bandwidth are unpredictable and this fact can considerably degrade the detection performance if the appropriate observation vector length is not correctly selected. Therefore, a multiple energy detector (MED) structure is applied in the time as well as in the frequency domain and it is evaluated in real acoustic scenarios. The results obtained demonstrate the robustness of the MED structure and a performance improvement in comparison to the standard ED.

Exploring the Affective Museum Visiting Experience: Adaptive Augmented Reality (A2R) and Cultural Heritage

Providing engaging interpretation resources for museum and gallery visitors may have a great impact on the overall museum visiting experience all by assisting museums in maintaining long-term relationships with their public. This paper focuses on the ways through which AR can be employed in museum and gallery settings as an interpretation medium. It also introduces a new generation of multimedia guides for the museum visit inspired by the concept of Adaptive Augmented Reality (A2R). Adaptive Augmented Reality (A2R) provides visual and acoustic augmentations that come to supplement the artefact or site viewed by a museum or gallery visitor and monitors the cognitive and affective impact of all interactions of the museum visitor both with the physical and the digital environment. The ultimate goal is to make every museum visit unique, by tailoring an Augmented Reality visit with contents that are susceptible to increase the affective impact of the augmented museum visiting experience and hence encourage int...

Fast communication: Unknown signal detection by one-class detector based on Gaussian copula

One-class detector is an option to deal with the problem of detecting an unknown signal in a background noise, as it is only necessary to know the noise distribution. Thus a Gaussian copula is proposed to capture the dependence among the noise samples, meanwhile the marginals can be estimated using well-known methods. We show that classical energy detectors are particular cases of the proposed one-class detector, when Gaussian noise distribution is assumed, but are inappropriate in other cases. Experiments combining simulated noise and real acoustic events have confirmed the superiority of the proposed detectors when noise is non-Gaussian. An interpretation of the methods in terms of the Edgeworth expansion is also included.

Background noise suppression for acoustic localization by means of an adaptive energy detection approach

A microphone array can be employed to localize dominant acoustic sources in a given noisy environment. This capability is successfully used in good signal to noise ratio (SNR) conditions but its accuracy decreases considerably in the presence of other background noise sources. In order to counteract this effect, a novel approach that combines the information provided by a Gaussian energy detector (GED) with the approved localization method SRP-PHAT is presented in this paper. To evaluate the presented technique, several acoustic sources (speech and impulsive sounds) were considered in a variety of different scenarios to demonstrate the robustness and the accuracy of the system proposed.

Generalized Matched Subspace Filter for Nonindependent Noise Based on ICA

We propose a generalization of the matched subspace filter for the detection of unknown signals in a background of non-Gaussian and nonindependent noise. The generalization is based on a modification of the Rao test by including a linear transformation derived from independent component analysis (ICA). Receiver operating characteristic (ROC) curves computed for simulated examples illustrate the significant improvement achieved with the generalized solution.