2009
Journal article
Closed Access
A heavy-tailed empirical Bayes method for replicated microarray data
Salas-Gonzalez D., Kuruoglu E. E., Ruiz D. P.
DNAmicroarray has been recognized as being an important tool for studying the expression of thousands of genes simultaneously. These experiments allow us to compare two different samples of cDNA obtained under different conditions. A novel method for the analysis of replicated microarray experiments based upon the modelling of gene expression distribution as a mixture of alpha-stable distributions is presented. Some features of the distribution of gene expression, such as Pareto tails and the fact that the variance of any given array increases concomitantly with an increase in the number of genes studied, suggest the possibility of modelling gene expression distribution on the basis of alpha-stable density. The proposed methodology uses very well known properties of alpha-stable distribution, such as the scale mixture of normals. A Bayesian log-posterior odds is calculated, which allows us to decide whether a gene is expressed differentially or not. The proposed methodology is illustrated using simulated and experimental data and the results are compared with other existing statistical approaches. The proposed heavy-tail model improves the performance of other distributions and is easily applicable to microarray gene data, specially if the dataset contains outliers or presents high variance between replicates.Source: Computational statistics & data analysis (Print) 53 (2009): 1535–1546. doi:10.1016/j.csda.2008.08.008
DOI: 10.1016/j.csda.2008.08.008
Metrics:
Salas-Gonzalez D., Kuruoglu E. E., Ruiz D. P.
DNAmicroarray has been recognized as being an important tool for studying the expression of thousands of genes simultaneously. These experiments allow us to compare two different samples of cDNA obtained under different conditions. A novel method for the analysis of replicated microarray experiments based upon the modelling of gene expression distribution as a mixture of alpha-stable distributions is presented. Some features of the distribution of gene expression, such as Pareto tails and the fact that the variance of any given array increases concomitantly with an increase in the number of genes studied, suggest the possibility of modelling gene expression distribution on the basis of alpha-stable density. The proposed methodology uses very well known properties of alpha-stable distribution, such as the scale mixture of normals. A Bayesian log-posterior odds is calculated, which allows us to decide whether a gene is expressed differentially or not. The proposed methodology is illustrated using simulated and experimental data and the results are compared with other existing statistical approaches. The proposed heavy-tail model improves the performance of other distributions and is easily applicable to microarray gene data, specially if the dataset contains outliers or presents high variance between replicates.Source: Computational statistics & data analysis (Print) 53 (2009): 1535–1546. doi:10.1016/j.csda.2008.08.008
DOI: 10.1016/j.csda.2008.08.008
Metrics:
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Computational Statistics & Data Analysis 
| www.sciencedirect.com | CNR ExploRA
2009
Journal article
Open Access
Astrophysical image separation by blind time-frequency source separation methods
Ozgen M. T., Kuruoglu E. E., Herranz D.
In this paper, two prevalent blind time-frequency (TF) source separation methods in the literature are adapted to astrophysical image mixtures and four algorithms are developed to separate them into their astrophysical components. The components considered in this work are cosmic microwave background (CMB) radiation, galactic dust and synchrotron, among which the CMB component is emphasized. These simulated components mixed via realistic coefficients are subjected to simulated additive, nonstationary Gaussian noise components of realistic power levels, to yield image mixtures on which our orthogonal and nonorthogonal TF algorithms are applied. The developed algorithms are compared with the FastICA algorithm and CMB component is found to be recovered with an improvement reaching to 3.25 decibels from CMB-synchrotron mixtures. The proposed techniques are believed to be generically applicable in separating other types of astrophysical components as well.Source: Digital signal processing (Print) 19 (2009): 360–369. doi:10.1016/j.dsp.2007.12.003
DOI: 10.1016/j.dsp.2007.12.003
Metrics:
Ozgen M. T., Kuruoglu E. E., Herranz D.
In this paper, two prevalent blind time-frequency (TF) source separation methods in the literature are adapted to astrophysical image mixtures and four algorithms are developed to separate them into their astrophysical components. The components considered in this work are cosmic microwave background (CMB) radiation, galactic dust and synchrotron, among which the CMB component is emphasized. These simulated components mixed via realistic coefficients are subjected to simulated additive, nonstationary Gaussian noise components of realistic power levels, to yield image mixtures on which our orthogonal and nonorthogonal TF algorithms are applied. The developed algorithms are compared with the FastICA algorithm and CMB component is found to be recovered with an improvement reaching to 3.25 decibels from CMB-synchrotron mixtures. The proposed techniques are believed to be generically applicable in separating other types of astrophysical components as well.Source: Digital signal processing (Print) 19 (2009): 360–369. doi:10.1016/j.dsp.2007.12.003
DOI: 10.1016/j.dsp.2007.12.003
Metrics:
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Aperta - TÜBİTAK Açık Arşivi 
| Digital Signal Processing | Anadolu Üniversitesi Akademik Arşiv Sistemi | www.sciencedirect.com | CNR ExploRA
2009
Journal article
Restricted
Bayesian separation of images modelled with MRFs using MCMC
Kayabol K., Kuruoglu E. E., Sankur B.
We investigate the source separation problem of random fields within a Bayesian framework. The Bayesian formulation enables the incorporation of prior image models in the estimation of sources. Due to the intractability of the analytical solution we resort to numerical methods for the joint maximization of the a posteriori distribution of the unknown variables and parameters. We construct the prior densities of pixels using Markov random fields based on a statistical model of the gradient image, and we use a fully Bayesian method with modified-Gibbs sampling. We contrast our work to approximate Bayesian solutions such as Iterated Conditional Modes (ICM) and to non-Bayesian solutions of ICA variety. The performance of the method is tested on synthetic mixtures of texture images and astrophysical images under various noise scenarios. The proposed method is shown to outperform significantly both its approximate Bayesian and non-Bayesian competitors.Source: IEEE transactions on image processing 18 (2009): 982–994. doi:10.1109/TIP.2009.2012905
DOI: 10.1109/tip.2009.2012905
Metrics:
Kayabol K., Kuruoglu E. E., Sankur B.
We investigate the source separation problem of random fields within a Bayesian framework. The Bayesian formulation enables the incorporation of prior image models in the estimation of sources. Due to the intractability of the analytical solution we resort to numerical methods for the joint maximization of the a posteriori distribution of the unknown variables and parameters. We construct the prior densities of pixels using Markov random fields based on a statistical model of the gradient image, and we use a fully Bayesian method with modified-Gibbs sampling. We contrast our work to approximate Bayesian solutions such as Iterated Conditional Modes (ICM) and to non-Bayesian solutions of ICA variety. The performance of the method is tested on synthetic mixtures of texture images and astrophysical images under various noise scenarios. The proposed method is shown to outperform significantly both its approximate Bayesian and non-Bayesian competitors.Source: IEEE transactions on image processing 18 (2009): 982–994. doi:10.1109/TIP.2009.2012905
DOI: 10.1109/tip.2009.2012905
Metrics:
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IEEE Transactions on Image Processing | ieeexplore.ieee.org | CNR ExploRA
2009
Journal article
Restricted
Finite mixture of alpha-stable distributions
Salas-Gonzalez D., Kuruoglu E. E., Ruiz D. P.
Over the last decades, the ±-stable distribution has proved to be a very efficient model for impulsive data. In this paper, we propose an extension of stable distributions, namely mixture of ±-stable distributions to model multimodal, skewed and impulsive data. A fully Bayesian framework is presented for the estimation of the stable density parameters and the mixture parameters. As opposed to most previous work on mixture models, the model order is assumed unknown and is estimated using reversible jump Markov chain Monte Carlo. It is important to note that the Gaussian mixture model is a special case of the presented model which provides additional flexibility to model skewed and impulsive phenomena. The algorithm is tested using synthetic and real data, accurately estimating ±-stable parameters, mixture coefficients and the number of components in the mixture.Source: Digital signal processing (Print) 19 (2009): 250–264. doi:10.1016/j.dsp.2007.11.004
DOI: 10.1016/j.dsp.2007.11.004
Metrics:
Salas-Gonzalez D., Kuruoglu E. E., Ruiz D. P.
Over the last decades, the ±-stable distribution has proved to be a very efficient model for impulsive data. In this paper, we propose an extension of stable distributions, namely mixture of ±-stable distributions to model multimodal, skewed and impulsive data. A fully Bayesian framework is presented for the estimation of the stable density parameters and the mixture parameters. As opposed to most previous work on mixture models, the model order is assumed unknown and is estimated using reversible jump Markov chain Monte Carlo. It is important to note that the Gaussian mixture model is a special case of the presented model which provides additional flexibility to model skewed and impulsive phenomena. The algorithm is tested using synthetic and real data, accurately estimating ±-stable parameters, mixture coefficients and the number of components in the mixture.Source: Digital signal processing (Print) 19 (2009): 250–264. doi:10.1016/j.dsp.2007.11.004
DOI: 10.1016/j.dsp.2007.11.004
Metrics:
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Digital Signal Processing | www.sciencedirect.com | CNR ExploRA
2009
Conference article
Restricted
ICA by maximizing non-stability
Wang B., Kuruoglu E. E., Zhang J.
We propose a new approach for ICA by maximizing the non-stability contrast function in this paper. This new version of ICA is motivated by the Generalized Central Limit Theorem (GCLT), an important extension of classical CLT. We demonstrate that the classical ICA based on maximization of non-Gaussianity is a special case of the new approach of ICA we introduce here which is based on maximization of non-Stability with certain constraints. To be able to quantify non-stability, we introduce a new measure of stability namely Alpha-stable negentropy. A numerical gradient ascent algorithm for the maximization of the alpha-stable negentropy with the objective of source separation is also introduced in this paper. Experiments show that ICA by maximum of non-stability performs very successfully in impulsive source separation problems.Source: Independent Component Analysis and Signal Separation. 8th International Conference, pp. 179–186, Paraty, Rio Janerio, Brasile, 15-18 Marzo 2009
DOI: 10.1007/978-3-642-00599-2_23
Metrics:
Wang B., Kuruoglu E. E., Zhang J.
We propose a new approach for ICA by maximizing the non-stability contrast function in this paper. This new version of ICA is motivated by the Generalized Central Limit Theorem (GCLT), an important extension of classical CLT. We demonstrate that the classical ICA based on maximization of non-Gaussianity is a special case of the new approach of ICA we introduce here which is based on maximization of non-Stability with certain constraints. To be able to quantify non-stability, we introduce a new measure of stability namely Alpha-stable negentropy. A numerical gradient ascent algorithm for the maximization of the alpha-stable negentropy with the objective of source separation is also introduced in this paper. Experiments show that ICA by maximum of non-stability performs very successfully in impulsive source separation problems.Source: Independent Component Analysis and Signal Separation. 8th International Conference, pp. 179–186, Paraty, Rio Janerio, Brasile, 15-18 Marzo 2009
DOI: 10.1007/978-3-642-00599-2_23
Metrics:
See at:
doi.org | link.springer.com | CNR ExploRA
2009
Conference article
Open Access
Image source separation using color channel dependencies
Kayabol K., Kuruoglu E. E., Sankur B.
We investigate the problem of source separation in images in the Bayesian framework using the color channel dependencies. As a case in point we consider the source separation of color images which have dependence between its components. A Markov Random Field (MRF) is used for modeling of the inter and intra-source local correlations. We resort to Gibbs sampling algorithm for obtaining the MAP estimate of the sources since non-Gaussian priors are adopted. We test the performance of the proposed method both on synthetic color texture mixtures and a realistic color scene captured with a spurious reflection.Source: Independent Component Analysis and Signal Separation. 8th International Conference, pp. 499–506, Paraty, Brazil, 15-18 March 2009
DOI: 10.1007/978-3-642-00599-2_63
Metrics:
Kayabol K., Kuruoglu E. E., Sankur B.
We investigate the problem of source separation in images in the Bayesian framework using the color channel dependencies. As a case in point we consider the source separation of color images which have dependence between its components. A Markov Random Field (MRF) is used for modeling of the inter and intra-source local correlations. We resort to Gibbs sampling algorithm for obtaining the MAP estimate of the sources since non-Gaussian priors are adopted. We test the performance of the proposed method both on synthetic color texture mixtures and a realistic color scene captured with a spurious reflection.Source: Independent Component Analysis and Signal Separation. 8th International Conference, pp. 499–506, Paraty, Brazil, 15-18 March 2009
DOI: 10.1007/978-3-642-00599-2_63
Metrics:
See at:
Aperta - TÜBİTAK Açık Arşivi | doi.org | link.springer.com | CNR ExploRA
2009
Journal article
Open Access
Modelling and assessing differential gene expression using the alpha stable distribution
Salas-Gonzalez D., Kuruoglu E. E., Ruiz D. P.
After normalization, the distribution of gene expressions for very different organisms have a similar shape, usually exhibit heavier tails than a Gaussian distribution, and have a certain degree of asymmetry. Therefore, this distribution has been modeled in the literature using different parametric families of distributions, such the Asymmetric Laplace or the Cauchy distribution. Moreover, it is known that the tails of spot-intensity distributions are described by a power law and the variance of a given array increases with the number of genes. These features of the distribution of gene expression strongly suggest that the alpha-stable distribution is suitable to model it. In this work, we model the error distribution for gene expression data using the alpha-stable distribution. This distribution is tested successfully for four different datasets. The Kullback-Leibler, Chi-square and Hellinger tests are performed to compare how alpha-stable, Asymmetric Laplace and Gaussian fit the spot intensity distribution. The alpha-stable is proved to perform much better for every array in every dataset considered. Furthermore, using an alpha-stable mixture model, a Bayesian log-posterior odds is calculated allowing us to decide whether a gene is differently expressed or not. This statistic is based on the Scale Mixture of Normals and other well known properties of the alpha-stable distribution. The proposed methodology is illustrated using simulated data and the results are compared with the other existing statistical approach.Source: The international journal of biostatistics 5 (2009): 16–24. doi:10.2202/1557-4679.1120
DOI: 10.2202/1557-4679.1120
Metrics:
Salas-Gonzalez D., Kuruoglu E. E., Ruiz D. P.
After normalization, the distribution of gene expressions for very different organisms have a similar shape, usually exhibit heavier tails than a Gaussian distribution, and have a certain degree of asymmetry. Therefore, this distribution has been modeled in the literature using different parametric families of distributions, such the Asymmetric Laplace or the Cauchy distribution. Moreover, it is known that the tails of spot-intensity distributions are described by a power law and the variance of a given array increases with the number of genes. These features of the distribution of gene expression strongly suggest that the alpha-stable distribution is suitable to model it. In this work, we model the error distribution for gene expression data using the alpha-stable distribution. This distribution is tested successfully for four different datasets. The Kullback-Leibler, Chi-square and Hellinger tests are performed to compare how alpha-stable, Asymmetric Laplace and Gaussian fit the spot intensity distribution. The alpha-stable is proved to perform much better for every array in every dataset considered. Furthermore, using an alpha-stable mixture model, a Bayesian log-posterior odds is calculated allowing us to decide whether a gene is differently expressed or not. This statistic is based on the Scale Mixture of Normals and other well known properties of the alpha-stable distribution. The proposed methodology is illustrated using simulated data and the results are compared with the other existing statistical approach.Source: The international journal of biostatistics 5 (2009): 16–24. doi:10.2202/1557-4679.1120
DOI: 10.2202/1557-4679.1120
Metrics:
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www.bepress.com | The International Journal of Biostatistics | The International Journal of Biostatistics | CNR ExploRA
2009
Journal article
Restricted
Permittivity range profile reconstruction of multilayered structures from microwave backscattering data by using particle swarm optimization
Genovesi S., Salerno E., Monorchio A., Manara G.
A method for the investigation of multilayered structures by using microwave probes is proposed. An iterative optimization procedure reconstructs the permittivity range profiles of such structures from backscattering data by optimizing a functional with a data term and a regularization term, including a line process to overcome the global smoothness enforced by classical regularization.Source: Microwave and optical technology letters (Print) 51 (2009): 2390–2394. doi:10.1002/mop.24642
DOI: 10.1002/mop.24642
Metrics:
Genovesi S., Salerno E., Monorchio A., Manara G.
A method for the investigation of multilayered structures by using microwave probes is proposed. An iterative optimization procedure reconstructs the permittivity range profiles of such structures from backscattering data by optimizing a functional with a data term and a regularization term, including a line process to overcome the global smoothness enforced by classical regularization.Source: Microwave and optical technology letters (Print) 51 (2009): 2390–2394. doi:10.1002/mop.24642
DOI: 10.1002/mop.24642
Metrics:
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Microwave and Optical Technology Letters | onlinelibrary.wiley.com | CNR ExploRA
2009
Journal article
Unknown
Diagnosis of lymphatic tumors by case-based reasoning on microscopic images
Colantonio S., Perner P., Salvetti O.
In this paper, a novel method for diagnosing lymphatic tissue tumors is presented. Microscopic specimen images are analyzed for extracting and characterizing malignant cells. A case-based reasoning approach is followed for classifying morphologic and densitometric cell features so as to provide a final diagnosis.Source: 2 (2009): 29–40.
Colantonio S., Perner P., Salvetti O.
In this paper, a novel method for diagnosing lymphatic tissue tumors is presented. Microscopic specimen images are analyzed for extracting and characterizing malignant cells. A case-based reasoning approach is followed for classifying morphologic and densitometric cell features so as to provide a final diagnosis.Source: 2 (2009): 29–40.
See at: CNR ExploRA
2009
Journal article
Restricted
An ontological framework for media analysis and mining
Colantonio S., Salvetti O., Gurevich I. B., Trusova Y.
Advances in tools and technologies for digital media production and analysis have assured the availability of larger and larger amount of data which carry a huge amount of information for solving specific application tasks. This development has stressed the need for advanced systems that are not limited to media storage and management but include also their intelligent representation and retrieval. In this paper, we report current results of an ontological framework under development for mining media data, thus offering the possibility of storing, retrieving, analyzing and investigating media to discover novel knowledge relevant to strategic application processes.Source: Pattern recognition and image analysis 19 (2009): 221–230. doi:10.1134/S1054661809020023
DOI: 10.1134/s1054661809020023
Metrics:
Colantonio S., Salvetti O., Gurevich I. B., Trusova Y.
Advances in tools and technologies for digital media production and analysis have assured the availability of larger and larger amount of data which carry a huge amount of information for solving specific application tasks. This development has stressed the need for advanced systems that are not limited to media storage and management but include also their intelligent representation and retrieval. In this paper, we report current results of an ontological framework under development for mining media data, thus offering the possibility of storing, retrieving, analyzing and investigating media to discover novel knowledge relevant to strategic application processes.Source: Pattern recognition and image analysis 19 (2009): 221–230. doi:10.1134/S1054661809020023
DOI: 10.1134/s1054661809020023
Metrics:
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Pattern Recognition and Image Analysis | link.springer.com | CNR ExploRA
2009
Journal article
Restricted
Heart deformation pattern analysis through shape modelling
Moroni D., Colantonio S., Salvetti O., Salvetti M.
In this paper, we present an approach to the description of time-varying anatomical structures. The main goal is to compactly but faithfully describe the whole heart cycle in such a way to allow for deformation pattern characterization and assessment. Using such an encoding, a reference database can be built, thus permitting similarity searches or data mining procedures.Source: Pattern recognition and image analysis 19 (2009): 262–270. doi:10.1134/S1054661809020084
DOI: 10.1134/s1054661809020084
Metrics:
Moroni D., Colantonio S., Salvetti O., Salvetti M.
In this paper, we present an approach to the description of time-varying anatomical structures. The main goal is to compactly but faithfully describe the whole heart cycle in such a way to allow for deformation pattern characterization and assessment. Using such an encoding, a reference database can be built, thus permitting similarity searches or data mining procedures.Source: Pattern recognition and image analysis 19 (2009): 262–270. doi:10.1134/S1054661809020084
DOI: 10.1134/s1054661809020084
Metrics:
See at:
Pattern Recognition and Image Analysis | link.springer.com | CNR ExploRA
2009
Journal article
Unknown
Il Web semantico
Martinelli M.
Interview on Semantic WebSource: Internet magazine (Lond.) 135 F (2009): 82–83.
Martinelli M.
Interview on Semantic WebSource: Internet magazine (Lond.) 135 F (2009): 82–83.
See at: CNR ExploRA
2009
Journal article
Open Access
Object tracking in video-surveillance
Moroni D., Pieri G.
This paper faces the automatic object tracking problem in a video-surveillance task. A previously selected and then identified target has to be retrieved in the scene under investigation because it is lost due to masking, occlusions, or quick and unexpected movements. A two-step procedure is used, firstly motion detection is used to determine a candidate target in the scene, secondly using a semantic categorization and Content Based Image Retrieval techniques, the candidate target is identified whether it is the one that was lost or not. The use of Content Based Image Retrieval serves as support to the search problem and is performed using a reference data base which was populated a priori.Source: Pattern recognition and image analysis 19 (2009): 271–276. doi:10.1134/S1054661809020096
DOI: 10.1134/s1054661809020096
Metrics:
Moroni D., Pieri G.
This paper faces the automatic object tracking problem in a video-surveillance task. A previously selected and then identified target has to be retrieved in the scene under investigation because it is lost due to masking, occlusions, or quick and unexpected movements. A two-step procedure is used, firstly motion detection is used to determine a candidate target in the scene, secondly using a semantic categorization and Content Based Image Retrieval techniques, the candidate target is identified whether it is the one that was lost or not. The use of Content Based Image Retrieval serves as support to the search problem and is performed using a reference data base which was populated a priori.Source: Pattern recognition and image analysis 19 (2009): 271–276. doi:10.1134/S1054661809020096
DOI: 10.1134/s1054661809020096
Metrics:
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ISTI Repository | Pattern Recognition and Image Analysis | link.springer.com | CNR ExploRA
2009
Conference article
Unknown
Ontology-based framework to image mining
Colantonio S., Gurevich I. B., Pieri G., Salvetti O., Trusova Y.
A novel knowledge-based approach for supporting image processing and analysis is presented as well as its use within a framework for image mining. Modern approaches to knowledge representation, ontologies and reasoning, have been combined with techniques for image processing, analysis and understanding within a semantic framework able to support the extraction of novel knowledge for image collectionsSource: 2nd International Workshop on Image Mining Theory and Applications, pp. 11–19, Lisboa, Portugal, 7-8 February 2009
Colantonio S., Gurevich I. B., Pieri G., Salvetti O., Trusova Y.
A novel knowledge-based approach for supporting image processing and analysis is presented as well as its use within a framework for image mining. Modern approaches to knowledge representation, ontologies and reasoning, have been combined with techniques for image processing, analysis and understanding within a semantic framework able to support the extraction of novel knowledge for image collectionsSource: 2nd International Workshop on Image Mining Theory and Applications, pp. 11–19, Lisboa, Portugal, 7-8 February 2009
See at: CNR ExploRA
2009
Conference article
Restricted
A decision support system for aiding heart failure management
Colantonio S., Martinelli M., Moroni D., Salvetti O., Chiarugi F., Emmanouilidou D.
The purpose of this paper is to present an effective way to achieve a high-level integration of a Clinical Decision Support System in the general process of Heart Failure care and to discuss the advantages of such an approach. In particular, the relevant and significant medical knowledge and experts' know-how have been modelled according to an ontological formalism extended with a base of rules for inferential reasoning. These have been also combined with advanced analytical tools for data processing. In particular, methods for the segmentation of echocardiographic image sequences and algorithms for ECG processing have been implemented and integrated into the system.Source: Ninth International Conference on Intelligent Systems Design and Applications, pp. 351–356, Pisa, 30 NOvember -2 December 2009
DOI: 10.1109/isda.2009.117
Metrics:
Colantonio S., Martinelli M., Moroni D., Salvetti O., Chiarugi F., Emmanouilidou D.
The purpose of this paper is to present an effective way to achieve a high-level integration of a Clinical Decision Support System in the general process of Heart Failure care and to discuss the advantages of such an approach. In particular, the relevant and significant medical knowledge and experts' know-how have been modelled according to an ontological formalism extended with a base of rules for inferential reasoning. These have been also combined with advanced analytical tools for data processing. In particular, methods for the segmentation of echocardiographic image sequences and algorithms for ECG processing have been implemented and integrated into the system.Source: Ninth International Conference on Intelligent Systems Design and Applications, pp. 351–356, Pisa, 30 NOvember -2 December 2009
DOI: 10.1109/isda.2009.117
Metrics:
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doi.org | ieeexplore.ieee.org | CNR ExploRA
2009
Conference article
Restricted
Astrophysical component separation with Langevin sampler
Kayabol K., Kuruoglu E. E., Sankur B.
Lighting artifacts are one of the main issues in digital photography: complex light setups are needed to attenuate or remove them. Flash light is a very easy way to illuminate an object or an environment, but it is rarely considered in most of the Computer Graphics and Computer Vision applications. This is due to the big amount of artifacts introduced by this lighting, and to the difficulty in modeling its behavior. In this paper we present a simple method to use flash light in the context of color acquisition and mapping on 3D models. We propose a simple way to accurately estimate the flash position with respect to the camera, and we propose two automatic methods to detect and remove artifacts from a set of images which are registered to a 3D model. These methods are integrated in the context of a color mapping framework. The results show that it is possible to obtain high quality colored 3D models using flash light, which is the most simple illumination setup. This results are extremely important especially in the context of Cultural heritage, where the acquisition of color has often to be performed on site, without a specific lighting setup.Source: IEEE 17th Signal Processing and Communications Applications Conference, pp. 77–84, Antalya/Turkey, 9-11 April 2009
DOI: 10.1109/siu.2009.5136378
Metrics:
Kayabol K., Kuruoglu E. E., Sankur B.
Lighting artifacts are one of the main issues in digital photography: complex light setups are needed to attenuate or remove them. Flash light is a very easy way to illuminate an object or an environment, but it is rarely considered in most of the Computer Graphics and Computer Vision applications. This is due to the big amount of artifacts introduced by this lighting, and to the difficulty in modeling its behavior. In this paper we present a simple method to use flash light in the context of color acquisition and mapping on 3D models. We propose a simple way to accurately estimate the flash position with respect to the camera, and we propose two automatic methods to detect and remove artifacts from a set of images which are registered to a 3D model. These methods are integrated in the context of a color mapping framework. The results show that it is possible to obtain high quality colored 3D models using flash light, which is the most simple illumination setup. This results are extremely important especially in the context of Cultural heritage, where the acquisition of color has often to be performed on site, without a specific lighting setup.Source: IEEE 17th Signal Processing and Communications Applications Conference, pp. 77–84, Antalya/Turkey, 9-11 April 2009
DOI: 10.1109/siu.2009.5136378
Metrics:
See at:
doi.org | ieeexplore.ieee.org | CNR ExploRA
2009
Conference article
Open Access
Event recognition with time varying Hidden Markov Model
Wang Z., Kuruoglu E. E., Yang X., Xu Y., Yu S.
Standard Hidden Markov Model (HMM) and the more general Dynamic Bayesian Network (DBN) models assume stationarity of state transition distribution. However, this assumption does not hold for many real life events of interest. In this paper, we propose a new time sequence model that extends HMM to time varying scenario. The time varying property is realized in our model by explicitly allowing the change of state transition density as the time spent in a particular state passes by. Rather than keeping transition densities at different time spots independent of each other, we exploit their temporal correlation by applying a hierarchical Dirichlet prior. This leads to a more robust time varying model, especially when training data are scarce. We also employ Markov Chain Monte Carlo (MCMC) sampling in learning the MAP estimate of time varying parameters, with a transition kernel incorporating linear optimization. The proposed model is applied to recognizing real video events, and is shown to outperform existing HMM-based methods.Source: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1761–1764, Taipei, Taiwan, 19-24 April 2009
DOI: 10.1109/icassp.2009.4959945
Metrics:
Wang Z., Kuruoglu E. E., Yang X., Xu Y., Yu S.
Standard Hidden Markov Model (HMM) and the more general Dynamic Bayesian Network (DBN) models assume stationarity of state transition distribution. However, this assumption does not hold for many real life events of interest. In this paper, we propose a new time sequence model that extends HMM to time varying scenario. The time varying property is realized in our model by explicitly allowing the change of state transition density as the time spent in a particular state passes by. Rather than keeping transition densities at different time spots independent of each other, we exploit their temporal correlation by applying a hierarchical Dirichlet prior. This leads to a more robust time varying model, especially when training data are scarce. We also employ Markov Chain Monte Carlo (MCMC) sampling in learning the MAP estimate of time varying parameters, with a transition kernel incorporating linear optimization. The proposed model is applied to recognizing real video events, and is shown to outperform existing HMM-based methods.Source: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1761–1764, Taipei, Taiwan, 19-24 April 2009
DOI: 10.1109/icassp.2009.4959945
Metrics:
See at:
www.ifp.illinois.edu | doi.org | ieeexplore.ieee.org | CNR ExploRA
2009
Conference article
Unknown
Fast MCMC separation for MRF modelled astrophysical components
Kayabol K., Kuruoglu E. E., Sankur B., Salerno E., Bedini L.
We propose an adaptive Monte Carlo Markov Chain (MCMC) simulation for the Bayesian source separation problem and apply it to the unmixing of astrophysical components. In this method, we use the Langevin stochastic equation for transitions, which enables parallel drawing of random samples from the posterior, and which reduces the computation time significantly (by two orders of magnitude). In addition to this, the parameters of the Markov Random Field (MRF) model are updated via Maximum Likelihood (ML) throughout the iterations.Source: IEEE 16th International Conference on Image Processing, pp. 2769–2772, Cairo, Egypt, 7-10 November 2009
Kayabol K., Kuruoglu E. E., Sankur B., Salerno E., Bedini L.
We propose an adaptive Monte Carlo Markov Chain (MCMC) simulation for the Bayesian source separation problem and apply it to the unmixing of astrophysical components. In this method, we use the Langevin stochastic equation for transitions, which enables parallel drawing of random samples from the posterior, and which reduces the computation time significantly (by two orders of magnitude). In addition to this, the parameters of the Markov Random Field (MRF) model are updated via Maximum Likelihood (ML) throughout the iterations.Source: IEEE 16th International Conference on Image Processing, pp. 2769–2772, Cairo, Egypt, 7-10 November 2009
See at: CNR ExploRA
2009
Contribution to conference
Unknown
Brass instrument making in Milan: 1800 - 1850
Carreras F., Meroni C.
Source: Romantic Brass Symposium, Berna, 2009
Carreras F., Meroni C.
Source: Romantic Brass Symposium, Berna, 2009
See at: CNR ExploRA
2009
Contribution to conference
Open Access
Un dispositivo portatile basato su DSP per un migliore ascolto della musica in soggetti ipoacusici
Bertini G., Massimo M., Paolini F.
Source: DSP Application Day 2009 e-Conference & Webinar, Milano, 2009
Bertini G., Massimo M., Paolini F.
Source: DSP Application Day 2009 e-Conference & Webinar, Milano, 2009
See at:
ISTI Repository | CNR ExploRA