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2005 Article Unknown

A method for recognizing and describing the links among dream sources
Barcaro U., Cavallaro C., Navona C.
A method is described for the identification of possible links between dream sources. The analysis is based on automatic recognition of word root recurrences in text files, including dream reports and associations. Graph representation of the links provides a quantitative description of their basic figuresSource: Dreaming (N.Y.N.Y.) 15 (2005): 271–287. doi:10.1037/1053-0797.15.4.271
DOI: 10.1037/1053-0797.15.4.271

See at: DOI Resolver | CNR People


2005 Article Unknown

Image denoising using bivariate alpha-stable distributions in the complex wavelet domain
Achim A., Kuruoglu E. E.
Recently, the dual-tree complex wavelet transform has been proposed as a novel analysis tool featuring near shift-invariance and improved directional selectivity compared to the standard wavelet transform. Within this framework, we describe a novel technique for removing noise from digital images. We design a bivariate maximum a posteriori (MAP) estimator, which relies on the family of isotropic alpha-stable distributions. Using this relatively new statistical model we are able to better capture the heavy-tailed nature of the data as well as the interscale dependencies of wavelet coefficients. We test our algorithm for the Cauchy case, in comparison with several recently published methods. The simulation results show that our proposed technique achieves state-of-the-art performance in terms of root mean squared error.Source: IEEE signal processing letters 12 (2005): 17–20. doi:10.1109/LSP.2004.839692
DOI: 10.1109/LSP.2004.839692

See at: DOI Resolver | ieeexplore.ieee.org | CNR People


2005 Article Open Access OPEN

Separation of correlated astrophysical sources using multiple-lag data covariance matrices
Bedini L., Herranz D., Salerno E., Baccigalupi C., Kuruoglu E. E., Tonazzini A.
This paper proposes a new strategy to separate astrophysical sources that are mutually correlated. This strategy is based on second order statistics and exploits prior information about the possible structure of the mixing matrix. Unlike ICA blind separation approaches, where the sources are assumed mutually independent and no prior knowledge is assumed about the mixing matrix, our strategy allows the independence assumption to be relaxed and performs the separation of even significantly correlated sources. Besides the mixing matrix, our strategy is also capable to evaluate the source covariance functions at several lags. Moreover, once the mixing parameters have been identified, a simple deconvolution can be used to estimate the probability density functions of the source processes. To benchmark our algorithm, we used a database that simulates the one expected from the instruments that will operate onboard ESA's Planck Surveyor Satellite to measure the CMB anisotropies all over the celestial sphere.Source: EURASIP journal on applied signal processing 2005 (2005): 2400–2412. doi:10.1155/ASP.2005.2400
DOI: 10.1155/ASP.2005.2400

See at: arXiv.org e-Print Archive Open Access | EURASIP Journal on Advances in Signal Processing Open Access | EURASIP Journal on Advances in Signal Processing Open Access | SpringerOpen Open Access | DOI Resolver | CNR People


2005 Article Unknown

Editorial activity - Applications of signal processing in astrophysics and cosmology
Kuruoglu E. E., Baccigalupi C.
We live in an epoch where the frontiers of our investigation and comprehension of fundamental physics depend largely on the light coming from the sky, that is, on the study of galactic and extra-galactic radiation. Watching the sky, in principle, we have access to the highest energies conceivable, generated by the laws of nature in extreme conditions, such as nearby black holes or even close to the origin of the universe itself. For example, in the microwave band, the extra-Galactic radiation is dominated by a markedly isotropic component, obeying a black body spectrum characterized by a temperature of about 2.726 Kelvin. That is the relic of the Big Bang, originated just 300 000 years after the initial starting point of the universe. This radiation, namely the cosmic microwave background (CMB) radiation, today is the most important observable we have to access the mysterious physics of the Big Bang itself. The latter is telling us about the unknown fundamental interactions and particles, the physics of spacetime, and the nature of quantum gravity, and represents the only way to address those issues in physics today. Electronics hardware technology has reached in these very recent years the capability to study the tiniest details of the CMB, carrying the image of the primordial stage of cosmic geometry, structure, and composition. Such a fantastic challenge is ongoing in this verymoment, while several CMB detectors are operating and advanced probes are being designed for the forthcoming decades. Many breakthroughs in physics are made possible by the use of the most advanced data analysis techniques. The present datasets obtained in astrophysical and cosmological observations are huge, and cover the entire electromagnetic spectrum, dealing with very different processes, from gamma and X-rays of the high-energy astrophysics of compact stars or black holes, to the microwave and infrared emission from the whole large-scale universe. This variety of the observational techniques and signals to deal with represents a formidable challenge for signal processing.We need state-ofthe-art techniques that can analyse, summarise, and extract the necessary information from this ocean of data. To continue with the example above, the microwave sky is dominated by the CMB radiation, but several processes contribute to the total emission, coming for instance fromall the processes occurring along the line of sight, such as the emission from other galaxies or clusters of those, as well as from the diffuse gas in our own Galaxy. Each of these processes are most relevant in different contexts in astrophysics and cosmology. Recently, the astrophysics field has benefited a great deal from the rich research work going on source separation in the signal processing field. Source separation aims at the recovery of the various different components fromthemultiband observations exploiting the differences between them, induced by their independent physical origins. Despite the mutual interest, the two disciplines suffer from lack of a common publication ground, implying that the results produced in one of them are not immediately visible in the other. The aim of the present issue is to provide a unified platform that would strengthen the bridge between signal processing and astrophysics and cosmology and enable the sharing of information. We would like to provide astrophysicists and cosmologists with a spectrum of the most advanced signal processing techniques and the signal processing community an exposure to various vital real problems in analysing astrophysics data that await solution. Finally, our aim is to provide a reference for present and future literature, in the widest possible context, accounting for various applications and algorithms proposed. Indeed, as the reader may see, the topics we collected range from solar physics, thus on the scale of stars, to the reconstructuon of the most ambitious signal from the Big Bang, with the reconstruction of the CMB pattern on all sky. The methods presented in the issue range from transform domain analysis of such wavelets to data mining techniques.Source: EURASIP journal on applied signal processing 15 (2005): 2397–2399.

See at: CNR People | www.hindawi.com


2005 Article Unknown

A two-dimensional wavelet-based approach
Bozzi E., Cavaccini G., Chimenti M., Di Bono M. G., Salvetti O.
An image processing procedure is proposed to detect porosity defects in composite materials, analyzing C-scan images obtained by ultrasound inspection techniques. An image described by a set of features is analyzed in order to evaluate its similarity with a reference set. A 2D wavelet transform is applied to the input image and then a feature extraction based on statistics of the detailed images produced by the transform itself is performed. The principal component analysis technique (PCA) is then applied in order to map input features into an output plane maximizing data variance. Finally the image is classified considering the distance between points in the PCA plane. This procedure is also applied for the analysis of a single image. Preliminary results on simulation images and real C-scan maps show that the procedure is able to detect defects.Source: Pattern recognition and image analysis 15-2 (2005): 516–519.

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2005 Article Unknown

Integration of two approaches to medical image analysis for diagnostic purposes
Di Bona S., Gurevich I., Koryabkina I., Nefyodov A., Salvetti O.
This paper presents the results of the research activity performed in the field of medical image analysis within a joint study between the Institute of Information Science and Technologies of the Italian National Research Council (ISTI-CNR) and the Scientific Council 'Cybernetics' of the Russian Academy of Sciences (SCC-RAS). The studies carried out concern the analysis and classification of neuro (ISTI-CNR) and hematological (SCC-RAS) images. The comparison and integration of the approaches adopted by the two research groups have been fostered as an important activity to mutually improve the significant results obtained up to now by both ISTI-CNR and SCC-RAS in the field of medical imaging.Source: Pattern recognition and image analysis 15-2 (2005): 539–542.

See at: puma.isti.cnr.it | CNR People


2005 Article Unknown

Tracking of Moving Targets in Video Sequences
Benvenuti M., Colantonio S., Di Bono M. G., Pieri G., Salvetti O.
A research has been carried out finalised to the definition of a methodology useful to detect and track moving targets in video sequences. Algorithms performing this task have been also developed for real time monitoring and surveillance purposes. Due to deformations occurring in the appearance of the target in the videos, a Hierarchical Artificial Neural Network (HANN) has been used to recognize target occlusion or masking, and to increase the normal tracking performance. Preliminary results are presented regarding both identification and tracking of animal moving at night in an open environment, and the surveillance of known scenes for unauthorized access control.Source: WSEAS transactions on systems 4 (2005): 359–364.

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2005 Conference object Unknown

A permittivity range profile reconstruction technique for multilayered structures by a line process-augmented genetic algorithm
Genovesi S., Salerno E., Monorchio A., Manara G.
This paper presents a technique to reconstruct the permittivity range profile of a layered medium using noisy backscattering data. Being an ill-posed problem, inverse scattering normally provides either unstable or oversmoothed results. Our method tries to obtain an accurate reconstruction using a two-step genetic algorithm employing a regularization constraint with an explicit line process and a local deterministic optimization strategy.Source: 2005 IEEE AP-S International Symposium and USNC/URSI National Radio Science, Washington DC, USA, 3-8 July 2005

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2005 Conference object Unknown

A technique to optimize nonuniformly spaced arrays with low sidelobe level by using a genetic algorithm
Monorchio A., Genovesi S., Serra U., Brizzi A., Manara G.
A Genetic Algorithm procedure for synthesizing the radiation pattern of nonuniformly spaced linear arrays with low side lobe level is presented. Some selected preliminary results are shown to validate the effectiveness and the reliability of the proposed approach.Source: 2005 IEEE AP-SInternational Symposium and USNC/URSI National Radio Science, Washington DC, USA, 3-8 July 2005

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2005 Conference object Unknown

Automation of preliminary histological diagnostics of oncological diseases
Belotserkovsky A., Nedzved A., Ablameyko S., Gurevich I., Salvetti O.
Source: International Conference on Advanced Information and Telemedicine Technologies, AITTH-2005, Minsk, 2005

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2005 Conference object Unknown

Bayesian MRF-based blind source separation of convolutive mixtures of images
Tonazzini A., Gerace I.
This paper deals with the recovery of clean images from a set of their noisy convolutive mixtures. In practice, this problem can be seen as the one of simultaneously separating and restoring source images that have been first degraded by unknown filters, then summed up and added with noise. We approach this problem in the framework of Blind Source Separation (BSS), where the unknown filters, in our case FIR filters in the form of blur kernels, must be estimated jointly with the sources. Assuming the statistical independence of the source images, we adopt Bayesian estimation for all the unknowns, and exploit information about local correlation within the individual sources through the use of suitable Gibbs priors, accounting also for well-behaved edges in the images. We derive an algorithm for recovering the blur kernels that make the estimated sources fit the known properties of the original sources. The method is validated through numerical experiments in a simplified setting, which is however related to real application scenarios.Source: EUSIPCO 2005. 13th European Signal Processing Conference, Antalya, 4-8 September 2005

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2005 Conference object Unknown

Bayesian separation of non-stationary mixtures of dependent gaussian sources
Gencaga D., Kuruoglu E. E., Ertuzun A.
n this work, we propose a novel approach to perform Dependent Component Analysis (DCA). DCA can be thought as the separation of latent, dependent sources from their observed mixtures which is a more realistic model than Independent Component Analysis (ICA) where the sources are assumed to be independent. In general, the sources can be spatiotemporally dependent and the mixing system may be non-stationary. Here, we propose a DCA algorithm, that combines concepts of particle filters and Markov Chain Monte Carlo (MCMC) methods in order to separate non-stationary mixtures of spatially dependent Gaussian sources.Source: 25th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, pp. 257–265, San José, August 7-12, 2005

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2005 Conference object Unknown

Equipment and procedures for microwave nondestructive evaluation of lapideous materials
Bozzi E., Chimenti M., Genovesi S., Salerno E., Zucchelli A.
A procedure for in-field nondestructive evaluation of lapideous materials is described. A portable instrument has been developed to evaluate the average permittivity of the probed volume. This is based on measuring the resonance frequency of a microstrip patch sensor. Once this hardware is enabled to perform coherent measurements, the optimization of an edge-preserving energy functional can yield high-resolution permittivity range profiles.Source: 13th Conference on Microwave Techniques . COMITE 2005, pp. 68–71, Czech Technical University, Prague, Czech Republic, 26-28 September 2005

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2005 Conference object Unknown

Estimation of time-varying autoregressive symmetric alpha-stable processes using particle filters
Gencaga D., Kuruoglu E. E., Ertuzun A.
In the last decade alpha-stable distributions have become a standard model for impulsive data. Especially the linear symmetric alpha-stable processes have found applications in various fields. When the process parameters are time-invariant, various techniques are available for estimation. However, time-invariance is an important restriction given that in many communications applications channels are time-varying. For such processes, we propose a relatively new technique, based on particle filters which obtained great success in tracking applications involving non-Gaussian signals and nonlinear systems. Since particle filtering is a sequential method, it enables us to track the time-varying autoregression coefficients of the alpha-stable processes. The method is tested both for abruptly and slowly changing autoregressive parameters of signals, where the driving noises are symmetric-alpha-stable processes and is observed to perform very well. Moreover, the method can easily be extended to skewed alpha-stable distributions.Source: 13th European Signal Processing conference, Antalya, 4-8 September 2005

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2005 Conference object Unknown

Non-linear fusion of images and the detection of point sources
Lopez-caniego M., Sanz J. L., Herranz D., Gonzalez-nuevo J., Barreiro B., Kuruoglu E. E.
This article considers the linear and quadratic fusion of a set of n-dimensional images. We aim to produce a single image that amplifies the signal and minimizes the noise. As a starting point, we consider wavelet subimages of a single image. We use three wavelets, the Mexican Hat Wavelet Family (MHWF) and the undecimated multiscale method to obtain 3N subimages. As an application we consider the detection of galaxies in Cosmic Microwave Background radiation maps. We use linear and quadratic fusion to produce a combined image for the detection. Moreover, we test these ideas for the simple case of point sources embedded in white noise and for the case of realistic simulations of microwave images for the 44 GHz channel of ESA's Planck satellite. Using quadratic fusion and allowing a 1% of false alarms we detect 25% more sources than using linear fusion. If we allow instead %5 false alarms, quadratic fusion yields 40% more sources than linear fusion.Source: International Workshop on Nonlinear Signal and Image Processing (NSIP 2005), Sapporo, May 18-20, 2005

See at: CNR People | www.ice.eng.hokudai.ac.jp


2005 Conference object Unknown

Object tracking in a stereo and infrared vision system
Colantonio S., Di Bono M. G., Pieri G., Salvetti O., Benvenuti M.
In this paper we deal with the problem of real-time detection, recognition and tracking of moving objects in open and unknown environments using an infrared (IR) and visible vision system. This task is generally very challenging and automatic tools to identify and follow an object -target- are often subject to constraints regarding the environment under investigation, the characteristics of the target itself and its full visibility with respect to a background. The goal of our study is the identification, and the possible prediction, of the movements of a target operating in real-time using a vision system capable of stereo and IR vision. Multi-source information is acquired using a system composed of a thermo-camera and two stereo visible-cameras synchronized. We obtain a set of IR images, which make the system more robust and invariant to light changes in the scene, corresponding to stereo grey level images. Firstly, target detection is performed by extracting its characteristic features from the images and then by storing the computed parameters on a specific database; secondly, the tracking task is carried on using different algorithms. Two different computational approaches are followed for tracking: a Hierarchical Artificial Neural Network (HANN) is used during active tracking for the recognition of the actual target, while if occurring partial occlusions or masking, a database retrieval method is used to support the search of the correct target to follow. A robotized prototype has been designed with an IR camera and two visible stereo cameras mounted on it. The stereo cameras are used to obtain three-dimensional information about target with respect to its background. The prototype has been tested on case studies regarding the identification and tracking of animals moving at night in an open environment, and the surveillance of known scenes for unauthorized access control.Source: Advanced Infrared Technologies and Applications- AITA, Rome, Italy, September 7-9,

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2005 Conference object Unknown

SAR image filtering based on the heavy-tailed rayleigh model
Achim A., Kuruoglu E. E., Zerubia J.
We describe a novel adaptive despeckling filter for Synthetic Aperture Radar (SAR) images. In the proposed approach, the Radar Cross Section (RCS) is estimated using a maximum a posteriori (MAP) criterion. We first employ a logarithmic transformation to change the multiplicative speckle into additive noise. We model the RCS using the heavy-tailed Rayleigh distribution, which was recently proposed as an accurate model for amplitude SAR images. We estimate model parameters from noisy observations by applying the 'method-of-log-cumulants', which relies on the Mellin transform. Finally, we compare our proposed algorithm with the classical Lee filtering technique applied on an aerial image and we quantify the performance improvement.Source: 13th European Signal Processing conference, Antalya, 4-8 september 2005

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2005 Conference object Unknown

System health monitoring using multilevel artificial neural networks
Colantonio S., Di Bono M. G., Pieri G., Salvetti O., Cavaccini G.
The assessment of the health state of complex physical systems is of key importance for maintaining the same systems safe, less expensive, adequately equipped and operating. In this work, a methodology is defined for evaluating the structure and performance integrity of a physical system or its components. The monitoring activity is based on a Multilevel Artificial Neural Network for describing, diagnosing and predicting the state of the monitored system. Following a coarse-to-fine paradigm, artificial neural networks of different topologies and typologies are modularly and hierarchically combined to firstly process and validate the sensor measurements acquired on-field, then classify the validated measures and, at the end, predict the state of the system. In course tests on experimental data furnished by Alenia and regarding aircraft components have shown that the proposed method is a promising aid for the evaluation of the health state of a physical structure and that it can be integrated inside a single aircraft life cycle monitoring system.Source: EEE International Conference on Computational Intelligence for Measurement Systems and Applications - CIMSA 2005, pp. 50–55, Taormina, 20-22 July 2005

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2005 Conference object Unknown

Time-varying autoregressive parameter estimation of cauchy processes by particle filters
Gencaga D., Kuruoglu E. E., Ertuzun A.
In this work, a new method is proposed in order to sequentially estimate the time-varying parameters of a Cauchy distributed process. For this purpose, particle filters, which are used in non-Gaussian and nonlinear Bayesian applications, are utilised. The proposed method forms a basis for the possible future applications of the -stable distributions with timevarying autoregressive coefficients, since it is the first general method that can be used for the estimation of such coefficients without using any restrictions on the parameters. The method is tested both for abruptly and slowly changing autoregressive parameters and observed to be performing very well.Source: 13th Signal Processing and Communication Applications Conference IEEE SIU-2005, Kayseri, 16-18 Maggio 2005

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2005 Conference object Unknown

Towards automated analysis of cytological and histological specimen images
Ablameyko S., Di Bona S., Gurevich I. B., Koryabkina I., Murashov D., Nefyodov A., Salvetti O., Trykova A., Vorobjev I.
The investigations are described concerning design of automated systems for hematopoietic tumors diagnostics. The results obtained in this area are presented. NShell software is a task-oriented software, aimed at analysis and classification of lymphoid cell nucleus in the cytological specimen images. The software is based on an instrumental environment for developing, testing, and applying image processing and image analysis algorithms - 'Black Square'. The main contribution of the paper is discussion of opportunities to apply information technology and software developed by the authors for automation of oncological diagnostics based on cytological data for diagnostics based both on cytological and histological data.Source: International Conference on Advanced Information and Telemedicine Technologies, AITTH-2005, pp. 27, Minsk, November 8-10, 2005

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