Audio-visual foreground extraction for event characterization

This paper presents a new method able to integrate audio and visual information for scene analysis in a typical surveillance scenario, using only one camera and one monaural microphone. Visual information is analyzed by a standard visual background/foreground (BG/FG) modelling module, enhanced with a novelty detection stage, and coupled with an audio BG/FG modelling scheme. The audiovisual association is performed on-line, by exploiting the concept of synchrony. Experimental tests carrying out classification and clustering of events show all the potentialities of the proposed approach, also in comparison with the results obtained by using the single modalities

Recognizing and forecasting the sign of financial local trends using hidden Markov models

The problem of forecasting financial time series has received great attention in the past, from both Econometrics and Pattern Recognition researchers. In this context, most of the efforts were spent to represent and model the volatility of the financial indicators in long time series. In this paper a different problem is faced, the prediction of increases and decreases in short (local) financial trends. This problem, poorly considered by the researchers, needs specific models, able to capture the movement in the short time and the asymmetries between increase and decrease periods. The methodology presented in this paper explicitly considers both aspects, encoding the financial returns in binary values (representing the signs of the returns), which are subsequently modelled using two separate Hidden Markov models, one for increases and one for decreases, respectively. The approach has been tested with different experiments with the Dow Jones index and other shares of the same market of different risk, with encouraging results

On the use of SIFT features for face authentication

Several pattern recognition and classification techniques have been applied to the biometrics domain. Among them, an interesting technique is the Scale Invariant Feature Transform (SIFT), originally devised for object recognition. Even if SIFT features have emerged as a very powerful image descriptors, their employment in face analysis context has never been systematically investigated. This paper investigates the application of the SIFT approach in the context of face authentication. In order to determine the real potential and applicability of the method, different matching schemes are proposed and tested using the BANCA database and protocol, showing promising results

Feature Level Fusion of Face and Fingerprint Biometrics

The aim of this paper is to study the fusion at feature extraction level for face and fingerprint biometrics. The proposed approach is based on the fusion of the two traits by extracting independent feature pointsets from the two modalities, and making the two pointsets compatible for concatenation. Moreover, to handle the problem of curse of dimensionality, the feature pointsets are properly reduced in dimension. Different feature reduction techniques are implemented, prior and after the feature pointsets fusion, and the results are duly recorded. The fused feature pointset for the database and the query face and fingerprint images are matched using techniques based on either the point pattern matching, or the Delaunay triangulation. Comparative experiments are conducted on chimeric and real databases, to assess the actual advantage of the fusion performed at the feature extraction level, in comparison to the matching score level.Comment: 6 pages, 7 figures, conferenc

Combination of atomic force microscopy and principal component analysis as a general method for direct recognition of functional and structural domains in nanonocomposite materials

In this work, we report a simple method to direct identify nanometer sized textures in composite materials by means of AFM spectroscopy, aiming at recognizing structured region to be further investigated. It consists in acquiring a set of dynamic data organized in spectroscopy maps and subsequently extracting most valuable information by means of the principal component analysis (PCA) method. This algorithm projects the information of D spectroscopy curves, each containing P data, acquired at each point of an LxC grid into a subset of LxC maps without any assumption on the sample structure, filtering out redundancies and noise. As a consequence, a huge amount of 3D data is condensed into few 2D maps, easy to be examined. Results of this algorithm allow to find and locate regions of interest within the map, allowing a further reduction of data series to be extensively analyzed or modeled. In this work, we explain the main features of the method and show its application on a nanocomposite sample. Microsc. Res. Tech. 73:973-981, 2010. © 2010 Wiley-Liss, Inc

Bag of Peaks

Abstract Motivation: The analysis of high-resolution proton nuclear magnetic resonance (NMR) spectrometry can assist human experts to implicate metabolites expressed by diseased biofluids. Here, we explore an intermediate representation, between spectral trace and classifier, able to furnish a communicative interface between expert and machine. This representation permits equivalent, or better, classification accuracies than either principal component analysis (PCA) or multi-dimensional scaling (MDS). In the training phase, the peaks in each trace are detected and clustered in order to compile a common dictionary, which could be visualized and adjusted by an expert. The dictionary is used to characterize each trace with a fixed-length feature vector, termed Bag of Peaks, ready to be classified with classical supervised methods. Results: Our small-scale study, concerning Type I diabetes in Sardinian children, provides a preliminary indication of the effectiveness of the Bag of Peaks approach over standard PCA and MDS. Consistently, higher classification accuracies are obtained once a sufficient number of peaks (>10) are included in the dictionary. A large-scale simulation of noisy spectra further confirms this advantage. Finally, suggestions for metabolite-peak loci that may be implicated in the disease are obtained by applying standard feature selection techniques. Availability: Matlab code to compute the Bag of Peaks representation may be found at http://economia.uniss.it/docenti/bicego/BagOfPeaks/BagOfPeaks.zip Contact: [email protected]

On the quantitative estimation of short-term aging in human faces

Facial aging has been only partially studied in the past and mostly in a qualitative way. This paper presents a novel approach to the estimation of facial aging aimed to the quantitative evaluation of the changes in facial appearance over time. In particular, the changes both in face shape and texture, due to short-time aging, are considered. The developed framework exploits the concept of “distinctiveness” of facial features and the temporal evolution of such measure. The analysis is performed both at a global and local level to define the features which are more stable over time. Several experiments are performed on publicly available databases with image sequences densely sampled over a time span of several years. The reported results clearly show the potential of the methodology to a number of applications in biometric identification from human faces

Recognizing People's Faces: from Human to Machine Vision

Recognizing people's face

Comparing faces: a computational and perceptual study

The problem of extracting distinctive parts from a face is addressed. Rather than examining a priori specified features such as nose, eyes, month or others, the aim here is to extract from a face the most distinguishing or dissimilar parts with respect to another given face, i.e. finding differences between faces. A computational approach, based on log polar patch sampling and evaluation, has been compared with results obtained from a newly designed perceptual test involving 45 people. The results of the comparison confirm the potential of the proposed computational method

Subspace clustering for situation assessment in aquatic drones

We propose a novel methodology based on subspace clustering for detecting, modeling and interpreting aquatic drone states in the context of autonomous water monitoring. It enables both more informative and focused analysis of the large amounts of data collected by the drone, and enhanced situation awareness, which can be exploited by operators and drones to improve decision making and autonomy. The approach is completely data-driven and unsupervised. It takes unlabeled sensor traces from several water monitoring missions and returns both a set of sparse drone state models and a clustering of data samples according to these models. We tested the methodology on a real dataset containing data of six different missions, two rivers and four lakes in different countries, for about 5.5 hours of navigation. Results show that the methodology is able to recognize known states “in/out of the water”, “up- stream/downstream navigation” and “manual/autonomous drive”, and to discover meaningful unknown states from their data-based properties, enabling novelty detection