nils-herrmann/clean_scirepeval huggingface dataset:
A Bayesian lower bound for parameter estimation of Poisson data including multiple changes

This paper derives lower bounds for the mean square errors of parameter estimators in the case of Poisson distributed data subjected to multiple abrupt changes. Since both change locations (discrete parameters) and parameters of the Poisson distribution (continuous parameters) are unknown, it is appropriate to consider a mixed Cramér-Rao/Weiss-Weinstein bound for which we derive closed-form expressions and illustrate its tightness by numerical simulations.
Classification of digital modulations by MCMC sampling

This paper addresses the problem of classification of digital modulations. The proposed solution uses the Bayes classifier, which is implemented by the Markov chain Monte Carlo scheme. The implementation considers classifications in the presence of phase and frequency offsets as well as residual filtering effects coming from imperfect channel equalization. The proposed approach has been tested for many scenarios and its performance has been compared with the maximum likelihood classifier and the 4/sup th/ order cumulant-based method. The obtained results show that our classifier outperforms the other methods considerably.
EEG source localization based on a structured sparsity prior and a partially collapsed Gibbs sampler

In this paper, we propose a hierarchical Bayesian model approximating the ℓ20 mixed-norm regularization by a multivariate Bernoulli Laplace prior to solve the EEG inverse problem by promoting spatial structured sparsity. The posterior distribution of this model is too complex to derive closed-form expressions of the standard Bayesian estimators. An MCMC method is proposed to sample this posterior and estimate the model parameters from the generated samples. The algorithm is based on a partially collapsed Gibbs sampler and a dual dipole random shift proposal for the non-zero positions. The brain activity and all other model parameters are jointly estimated in a completely unsupervised framework. The results obtained on synthetic data with controlled ground truth show the good performance of the proposed method when compared to the ℓ21 approach in different scenarios, and its capacity to estimate point-like source activity.
P- and T-Wave Delineation in ECG Signals Using a Bayesian Approach and a Partially Collapsed Gibbs Sampler

Detection and delineation of P- and T-waves are important issues in the analysis and interpretation of electrocardiogram (ECG) signals. This paper addresses this problem by using Bayesian inference to represent a priori relationships among ECG wave components. Based on the recently introduced partially collapsed Gibbs sampler principle, the wave delineation and estimation are conducted simultaneously by using a Bayesian algorithm combined with a Markov chain Monte Carlo method. This method exploits the strong local dependency of ECG signals. The proposed strategy is evaluated on the annotated QT database and compared to other classical algorithms. An important feature of this paper is that it allows not only for the detection of P- and T-wave peaks and boundaries, but also for the accurate estimation of waveforms for each analysis window. This can be useful for some ECG analysis that require wave morphology information.
A hybrid inter and intra subject model for FMRI analysis

Nowadays fMRI studies are generally conducted over cohorts of individuals in order to study brain activity across different areas and using various stimuli. This paper generalizes a joint parcellation detection estimation (JPDE) model in order to perform an inter and intra subject analysis. The proposed approach allows one to estimate group and subject-level parcellations as well as the corresponding underlying dynamics. Moreover, it allows detecting the evoked activity for each individual. The proposed model is evaluated using synthetic and real fMRI data from a group study.
Distributed boosting for cloud detection

The SPOT 6-7 satellite ground segment includes a systematic and automatic cloud detection step in order to feed a catalogue with a binary cloud mask and an appropriate confidence measure. In order to significantly improve the SPOT cloud detection and get rid of frequent manual re-labelings, we study a new automatic cloud detection technique that is adapted to large datasets. The proposed method is based on a modified distributed boosting algorithm. Experiments conducted using the framework Apache Spark on a SPOT 6 image database with various landscapes and cloud coverage show promising results.
Change-point detection in astronomical data by using a hierarchical model and a bayesian sampling approach

Detection of significant intensity variations in astronomical time-series can be achieved with a hierarchical Bayesian approach to a piecewise constant Poisson rate model. A Gibbs sampling strategy allows joint estimation of the unknown parameters and hyperparameters. Results with real and synthetic photon counting data illustrate the performance of the proposed algorithm. An extension to joint segmentation of multiple time series is also discussed
CRC-assisted error correction in a trellis coded system with bit stuffing

This paper introduces a new error correction strategy using cyclic redundancy checks (CRC) for a trellis coded system in the presence of bit stuffing. The proposed receiver is designed to simultaneously demodulate, decode and correct the received message in the presence of bit stuffing. It is based on a Viterbi algorithm exploiting the conditional transitions of an appropriate extended trellis. The receiver is evaluated with automatic identification system (AIS) messages constructed with a 16 bit CRC and a Gaussian Minimum Shift Keying (GMSK) modulation. The stuffed bits are inserted after any sequence of five consecutive bits 1 as requested by the AIS recommendation. Simulation results illustrate the algorithm performance in terms of packet error rate. A gain of more than 2.5dB is obtained when compared to the conventional GMSK receiver.
Bayesian Estimation of Altimeter Echo Parameters

This paper studies a Bayesian algorithm for estimating the parameters associated to Brown's model. The joint posterior distribution of the unknown parameter vector (amplitude, epoch and significant wave height) associated with this model is derived. This posterior is too complex to obtain closed form expressions of the minimum mean square error and the maximum a posteriori estimators. We propose to sample according to this distribution using an hybrid Metropolis within Gibbs algorithm. The simulated samples are then used to estimate the unknown parameters of Brown's model. The proposed strategy provides better estimations than the standard maximum likelihood estimator at the price of an increased computational cost.
Detection of variance changes and mean value jumps in measurement noise for multipath mitigation in urban navigation

This paper studies an urban navigation filter for land vehicles. Typical urban-canyon phenomena as multipath and GPS outages seriously degrade positioning performance. To deal with these scenarios a hybrid navigation system using GPS and dead-reckoning sensors is presented. This navigation system is complemented by a two-step detection procedure that classifies outliers according to their associated source of error. Two different situations will be considered in the presence of multipath. These situations correspond to the presence or absence of line of sight for the different GPS satellites. Therefore, two kinds of errors are potentially ldquocorruptingrdquo the pseudo-ranges, modeled as variance changes or mean value jumps in noise measurements. An original multiple model approach is proposed to detect, identify and correct these errors and provide a final consistent solution.
Optimal wavelet for abrupt change detection in multiplicative noise

This paper addresses abrupt change detection in multiplicative noise using the continuous wavelet transform. An optimal wavelet, maximizing a well-chosen time-scale contrast criterion is derived. The analytical optimization gives the optimal wavelet closed expression. The influence of the mother wavelet on signature-based detector performance is then demonstrated. Detection performance is characterized using receiver operating characteristic curves computed from Monte-Carlo simulations. The optimal wavelet obviously improves performance with respect to other wavelets classically used for singularity detection.
Hierarchical Bayesian Sparse Image Reconstruction With Application to MRFM

This paper presents a hierarchical Bayesian model to reconstruct sparse images when the observations are obtained from linear transformations and corrupted by an additive white Gaussian noise. Our hierarchical Bayes model is well suited to such naturally sparse image applications as it seamlessly accounts for properties such as sparsity and positivity of the image via appropriate Bayes priors. We propose a prior that is based on a weighted mixture of a positive exponential distribution and a mass at zero. The prior has hyperparameters that are tuned automatically by marginalization over the hierarchical Bayesian model. To overcome the complexity of the posterior distribution, a Gibbs sampling strategy is proposed. The Gibbs samples can be used to estimate the image to be recovered, e.g., by maximizing the estimated posterior distribution. In our fully Bayesian approach, the posteriors of all the parameters are available. Thus, our algorithm provides more information than other previously proposed sparse reconstruction methods that only give a point estimate. The performance of the proposed hierarchical Bayesian sparse reconstruction method is illustrated on synthetic data and real data collected from a tobacco virus sample using a prototype MRFM instrument.
Joint Segmentation and Deconvolution of Ultrasound Images Using a Hierarchical Bayesian Model Based on Generalized Gaussian Priors.

This paper proposes a joint segmentation and deconvolution Bayesian method for medical ultrasound (US) images. Contrary to piecewise homogeneous images, US images exhibit heavy characteristic speckle patterns correlated with the tissue structures. The generalized Gaussian distribution (GGD) has been shown to be one of the most relevant distributions for characterizing the speckle in US images. Thus, we propose a GGD-Potts model defined by a label map coupling US image segmentation and deconvolution. The Bayesian estimators of the unknown model parameters, including the US image, the label map, and all the hyperparameters are difficult to be expressed in a closed form. Thus, we investigate a Gibbs sampler to generate samples distributed according to the posterior of interest. These generated samples are finally used to compute the Bayesian estimators of the unknown parameters. The performance of the proposed Bayesian model is compared with the existing approaches via several experiments conducted on realistic synthetic data and in vivo US images.
Restoration of ultrasound images using a hierarchical Bayesian model with a generalized Gaussian prior

This paper addresses the problem of ultrasound image restoration within a Bayesian framework. The distribution of the ultrasound image is assumed to be a generalized Gaussian distribution (GGD). The main contribution of this work is to propose a hierarchical Bayesian model for estimating the GGD parameters. The Bayesian estimators associated with this model are difficult to be expressed in closed form. Thus we investigate a Markov chain Monte Carlo method which is used to generate samples asymptotically distributed according to the posterior of interest. These generated samples are finally used to compute the Bayesian estimators of the GGD parameters. The performance of the proposed Bayesian model is tested with synthetic data and compared with the performance obtained with the expectation maximization algorithm.
A new Bayesian unmixing algorithm for hyperspectral images mitigating endmember variability

This paper presents an unsupervised Bayesian algorithm for hyperspectral image unmixing accounting for endmember variability. Each image pixel is modeled by a linear combination of random endmembers to take into account endmember variability in the image. The coefficients of this linear combination (referred to as abundances) allow the proportions of each material (endmembers) to be quantified in the image pixel. An additive noise is also considered in the proposed model generalizing the normal compositional model. The proposed Bayesian algorithm exploits spatial correlations between adjacent pixels of the image and provides spectral information by achieving a spectral unmixing. It estimates both the mean and the covariance matrix of each endmember in the image. A spatial classification is also obtained based on the estimated abundances. Simulations conducted with synthetic and real data show the potential of the proposed model and the unmixing performance for the analysis of hyperspectral images.
Estimation of the degree of polarization in dual-polarized SAR imagery

Analysis of dual-polarized SAR imagery has gained new importance with the recent launches of ALOS PALSAR, RADARSAT-2, and TerraSAR-X polarimetric SAR systems. Information contained in polarimetric images, collected by these SAR systems, can be characterized by a scalar parameter called the degree of polarization. This parameter has long been recognized as one of the most important parameters characterizing a partially polarized electromagnetic wave. In this paper, we provide maximum likelihood and moment-based estimators of the degree of polarization in dual-polarized SAR imagery. We evaluate and compare the performance of these estimators on RADARSAT-2 polarimetric data, over various terrain types such as urban, vegetation, and ocean.
Nonlinearity Detection in Hyperspectral Images Using a Polynomial Post-Nonlinear Mixing Model

This paper studies a nonlinear mixing model for hyperspectral image unmixing and nonlinearity detection. The proposed model assumes that the pixel reflectances are nonlinear functions of pure spectral components contaminated by an additive white Gaussian noise. These nonlinear functions are approximated by polynomials leading to a polynomial post-nonlinear mixing model. We have shown in a previous paper that the parameters involved in the resulting model can be estimated using least squares methods. A generalized likelihood ratio test based on the estimator of the nonlinearity parameter is proposed to decide whether a pixel of the image results from the commonly used linear mixing model or from a more general nonlinear mixing model. To compute the test statistic associated with the nonlinearity detection, we propose to approximate the variance of the estimated nonlinearity parameter by its constrained Cramér-Rao bound. The performance of the detection strategy is evaluated via simulations conducted on synthetic and real data. More precisely, synthetic data have been generated according to the standard linear mixing model and three nonlinear models from the literature. The real data investigated in this study are extracted from the Cuprite image, which shows that some minerals seem to be nonlinearly mixed in this image. Finally, it is interesting to note that the estimated abundance maps obtained with the post-nonlinear mixing model are in good agreement with results obtained in previous studies.
Parameter Estimation for Multivariate Mixed Poisson Distributions

Estimating the parameters of multivariate distributions whose densities or masses cannot be expressed in tractable closed-form is a challenging problem. This paper concentrates on a family of such discrete distributions referred to as multivariate mixed Poisson distributions (MMPDs). These distributions are interesting for modeling correlations between adjacent pixels of active and astronomical images. Several estimators of MMPD parameters are investigated. These estimators include a composite likelihood estimator and a non-linear least squares estimator
Hyperspectral unmixing accounting for spatial correlations and endmember variability

This paper presents an unsupervised Bayesian algorithm for hyper-spectral image unmixing accounting for endmember variability. This variability is obtained by assuming that each pixel is a linear combination of random endmembers weighted by their corresponding abundances. An additive noise is also considered in the proposed model generalizing the normal compositional model. The proposed model is unsupervised since it estimates the abundances and both the mean and the covariance matrix of each endmember. A classification map indicating the class of each pixel is also obtained based on the estimated abundances. Simulations conducted on a real dataset show the potential of the proposed model in terms of unmixing performance for the analysis of hyperspectral images.
Joint Particle Filter and UKF Position Tracking in Severe Non-Line-of-Sight Situations

The performance of localization techniques in a wireless communication system is severely impaired by biases induced in the range and angle measures because of the non-line-of-sight (NLOS) situation, caused by obstacles in the transmitted signal path. However, the knowledge of the line-of-sight (LOS) or NLOS situation for each measure can improve the final accuracy. This paper studies the localization of mobile terminals (MT) based on a Bayesian model for the LOS-NLOS evolution. This Bayesian model does not require having a minimum number of LOS measures at each acquisition. A tracking strategy based on a particle filter (PF) and an unscented Kalman filter (UKF) is used both to estimate the LOS-NLOS situation and the MT kinetic variables (position and speed). The approach shows a remarkable reduction in positioning error and a high degree of scalability in terms of performance versus complexity.
Bayesian Estimation of Linear Mixtures Using the Normal Compositional Model. Application to Hyperspectral Imagery

This paper studies a new Bayesian unmixing algorithm for hyperspectral images. Each pixel of the image is modeled as a linear combination of so-called endmembers. These endmembers are supposed to be random in order to model uncertainties regarding their knowledge. More precisely, we model endmembers as Gaussian vectors whose means have been determined using an endmember extraction algorithm such as the famous N-finder (N-FINDR) or Vertex Component Analysis (VCA) algorithms. This paper proposes to estimate the mixture coefficients (referred to as abundances) using a Bayesian algorithm. Suitable priors are assigned to the abundances in order to satisfy positivity and additivity constraints whereas conjugate priors are chosen for the remaining parameters. A hybrid Gibbs sampler is then constructed to generate abundance and variance samples distributed according to the joint posterior of the abundances and noise variances. The performance of the proposed methodology is evaluated by comparison with other unmixing algorithms on synthetic and real images.
A post nonlinear mixing model for hyperspectral images unmixing

This paper studies estimation algorithms for nonlinear hyperspectral image unmixing. The proposed unmixing model assumes that the pixel reflectances are polynomial functions of linear mixtures of pure spectral components contaminated by an additive white Gaussian noise. A hierarchical Bayesian algorithm and an optimization method are proposed for solving the resulting unmixing problem. The parameters involved in the proposed model satisfy constraints that are naturally included in the estimation procedure. The performance of the unmixing strategies is evaluated thanks to simulations conducted on synthetic and real data.
Bayesian Estimation of the Multifractality Parameter for Image Texture Using a Whittle Approximation

Texture characterization is a central element in many image processing applications. Multifractal analysis is a useful signal and image processing tool, yet, the accurate estimation of multifractal parameters for image texture remains a challenge. This is due in the main to the fact that current estimation procedures consist of performing linear regressions across frequency scales of the 2D dyadic wavelet transform, for which only a few such scales are computable for images. The strongly non-Gaussian nature of multifractal processes, combined with their complicated dependence structure, makes it difficult to develop suitable models for parameter estimation. Here, we propose a Bayesian procedure that addresses the difficulties in the estimation of the multifractality parameter. The originality of the procedure is threefold. The construction of a generic semiparametric statistical model for the logarithm of wavelet leaders; the formulation of Bayesian estimators that are associated with this model and the set of parameter values admitted by multifractal theory; the exploitation of a suitable Whittle approximation within the Bayesian model which enables the otherwise infeasible evaluation of the posterior distribution associated with the model. Performance is assessed numerically for several 2D multifractal processes, for several image sizes and a large range of process parameters. The procedure yields significant benefits over current benchmark estimators in terms of estimation performance and ability to discriminate between the two most commonly used classes of multifractal process models. The gains in performance are particularly pronounced for small image sizes, notably enabling for the first time the analysis of image patches as small as 64 × 64 pixels.
Bilinear models for nonlinear unmixing of hyperspectral images

This paper compares several nonlinear models recently introduced for hyperspectral image unmixing. All these models consist of bilinear models that have shown interesting properties for hyperspectral images subjected to multipath effects. The first part of this paper presents different algorithms allowing the parameters of these models to be estimated. The relevance and flexibility of these models for spectral unmixing are then investigated by comparing the reconstruction errors and spectral angle mappers computed from synthetic and real dataset. This kind of study is important to determine which mixture model should be used in practical applications for hyper-spectral image unmixing.
Tracking articulated objects with physics engines

This paper presents a novel approach to track multiple articulated objects in a video sequence. The key idea is to define a model of the object using a set of geometrical primitives linked by physical constraints, and exploit physics engines to solve these constraints while the model adapts to the object under the influence of local mean-shift processes. This novel approach to object tracking has numerous advantages: the model provides rich geometric information about the object at the articulation level; multiple touching objects are implicitly distinguished using collision detection strategies; physics engines are able to efficicently manage both image-based and model-based constraints simultaneously for a neglectable computational cost, suggesting their potential interest for many more image processing applications.
Combining global tracking with statistical classification to analyze endocytosis dynamics using TIRF microscopy

In this work we set several mathematical tools to study the role of Dynamin and Endophilin in the Clathrin-mediated endocytosis process. Their different dynamics and co-localizations are observed by using TIRF microscopy. We define in particular a novel tracking method in order to track the Clathrin-coated pits and quantify their co-localization with the proteins involved in the process. Thereafter, we perform a statistical analysis of the lifetimes of Clathrin-coated pits tracks by a suitable mixture model. This allows to define different populations of interest and point out the role of Endophilin and Dynamin in the process.