nils-herrmann/clean_scirepeval huggingface dataset:
Spectral–Spatial Classification of Hyperspectral Images Using ICA and Edge-Preserving Filter via an Ensemble Strategy

To obtain accurate classification results of hyperspectral images, both spectral and spatial information should be fully exploited in the classification process. In this paper, we propose a novel method using independent component analysis (ICA) and edge-preserving filtering (EPF) via an ensemble strategy for the classification of hyperspectral data. First, several subsets are randomly selected from the original feature space. Second, ICA is used to extract spectrally independent components followed by an effective EPF method, to produce spatial features. Two strategies (i.e., parallel and concatenated) are presented to include the spatial features in the analysis. The spectral-spatial features are then classified with a random forest or a rotation forest classifier. Experimental results on two real hyperspectral data sets demonstrate the effectiveness of the proposed methods. A sensitivity analysis of the new classifiers is also performed.
Parametric spectral analysis tools for the characterization of A/D converters

This paper focuses on the estimation problem of typical parameters of the A/D converters. As basic assumption, the data output consists of a sum of real sinusoids. In this framework, we investigate the interest of the parametric spectral methods in the ADC testing. We give the detail of the three major estimation steps in order to retrieve all signal output parameters. Finally, we present the major advantage of this estimation scheme: the good results obtained in spite of the noncoherent frequency feature used in the test.
Semi-supervised learning for location recognition from wearable video

This paper tackles the problem of image-based indoor location recognition. The context of the present work is activity monitoring using a wearable video camera data. Because application constraints necessitate weak supervision, a semi-supervised approach has been adopted which leverages the large amount of unlabeled images. The proposed method is based on the Bag of Features approach for image description followed by spectral dimensionality reduction in a transductive setup. Additional information from geometrical verification constraints are also considered which allowed to reach higher performance levels. The considered algorithms are compared experimentally on the data acquired in the wearable camera setup.
Wavelet Packets of Nonstationary Random Processes: Contributing Factors for Stationarity and Decorrelation

The paper addresses the analysis and interpretation of second order random processes by using the wavelet packet transform. It is shown that statistical properties of the wavelet packet coefficients are specific to the filtering sequences characterizing wavelet packet paths. These statistical properties also depend on the wavelet order and the form of the cumulants of the input random process. The analysis performed points out the wavelet packet paths for which stationarization, decorrelation and higher order dependency reduction are effective among the coefficients associated with these paths. This analysis also highlights the presence of singular wavelet packet paths: the paths such that stationarization does not occur and those for which dependency reduction is not expected through successive decompositions. The focus of the paper is on understanding the role played by the parameters that govern stationarization and dependency reduction in the wavelet packet domain. This is addressed with respect to semi-analytical cumulant expansions for modeling different types of nonstatonarity and correlation structures. The characterization obtained eases the interpretation of random signals and time series with respect to the statistical properties of their coefficients on the different wavelet packet paths.
Multivariate texture retrieval using the SIRV representation and the geodesic distance

This paper presents a new wavelet based retrieval approach based on Spherically Invariant Random Vector (SIRV) modeling of wavelet subbands. Under this multivariate model, wavelet coefficients are considered as a realization of a random vector which is a product of the square root of a scalar random variable (called multiplier) with an independent Gaussian vector. We propose to work on the joint distribution of the scalar multiplier and the multivariate Gaussian process. For measuring similarity between two texture images, the geodesic distance is provided for various multiplier priors. A comparative study between the proposed method and conventional models on the VisTex image database is conducted and indicates that SIRV modeling combined with geodesic distance achieves higher recognition rates than classical approaches.
Joint linear-circular stochastic models for texture classification

In this paper, we investigate both linear and circular stochastic models in the context of texture discrimination. These models aim at representing the magnitudes and orientations obtained by a complex wavelet decomposition, such as the steerable pyramid.The novelty consists in considering specific parametric models for circular data such as von Mises and ψ- distributions to describe the distributions of orientations. Particular attention is paid to the choice of a metric and to its adequation to the models. Indexing experiments are conducted to quantitatively evaluate the performances of the proposed models and of the chosen matrices, i.e. the L1 and Kullback-Leibler distances.
Texture Reconstruction Guided by a High-Resolution Patch

In this paper, we aim at super-resolving a low-resolution texture under the assumption that a high-resolution patch of the texture is available. To do so, we propose a variational method that combines two approaches that are texture synthesis and image reconstruction. The resulting objective function holds a nonconvex energy that involves a quadratic distance to the low-resolution image, a histogram-based distance to the high-resolution patch, and a nonlocal regularization that links the missing pixels with the patch pixels. As for the histogram-based measure, we use a sum of Wasserstein distances between the histograms of some linear transformations of the textures. The resulting optimization problem is efficiently solved with a primal-dual proximal method. Experiments show that our method leads to a significant improvement, both visually and numerically, with respect to the state-of-the-art algorithms for solving similar problems.
Spectral–Spatial Rotation Forest for Hyperspectral Image Classification

Forest (RoF) is a recent powerful decision tree (DT) ensemble classifier of hyperspectral images. RoF exploits random feature selection and data transformation techniques to improve both the diversity and accuracy of DT classifiers. Conventional RoF only considers data transformation on spectral information. To overcome this limitation, we propose a spectral and spatial RoF (SSRoF), to further improve the performance. In SSRoF, pixels are first smoothed by the multiscale (MS) spatial weight mean filtering. Then, spectral–spatial data transformation, which is based on a joint spectral and spatial rotation matrix, is introduced into the RoF. Finally, classification results obtained from each scale are integrated by a majority voting rule. Experimental results on two datasets indicate the competitive performance of the proposed method when compared to other state-of-the-art methods.
Dynamic magnetic field compensation for micro UAV attitude estimation

This paper presents a method to calibrate and compensate dynamically the magnetic field created by wires of different devices in the context of micro robotic localization. The compensation step consists in mapping available information on the vehicle state to the dynamic magnetic perturbation created by each device and compensate this perturbation. The model parameters used for this mapping are estimated in a prior step thanks to calibration data. The compensation is independent of the calibration of the static errors and does not require current sensors. The proposed method improves the quality of magnetic measurements and relaxes the distance restriction between sensors and power electronics which is primordial to develop compact systems. We illustrate and validate experimentally our approach on a micro UAV with four rotors.
Structuring of large and heterogeneous texture databases

Processing large databases is intricate for databases involving several types of textures. In particular, for content-based image retrieval, a query has to be compared with all the samples pertaining to the database in order to identify its content/class and this is time consuming. Furthermore, modeling of a large database is a difficult task for databases involving several types of textures since accurate models for certain textures are not guaranteed tobe very relevant for other types of textures and vice-versa. In order to save computational time and increase performance in processing of large texture databases, the present paper proposes structuring texture databases by using stochasticity metaclasses.
Spectral-Spatial Rotation Forest for Hyperspectral Image Classification

Forest (RoF) is a recent powerful decision tree (DT) ensemble classifier of hyperspectral images. RoF exploits random feature selection and data transformation techniques to improve both the diversity and accuracy of DT classifiers. Conventional RoF only considers data transformation on spectral information. To overcome this limitation, we propose a spectral and spatial RoF (SSRoF), to further improve the performance. In SSRoF, pixels are first smoothed by the multiscale (MS) spatial weight mean filtering. Then, spectral–spatial data transformation, which is based on a joint spectral and spatial rotation matrix, is introduced into the RoF. Finally, classification results obtained from each scale are integrated by a majority voting rule. Experimental results on two datasets indicate the competitive performance of the proposed method when compared to other state-of-the-art methods.
Gaussian Copula Multivariate Modeling for Texture Image Retrieval Using Wavelet Transforms

In the framework of texture image retrieval, a new family of stochastic multivariate modeling is proposed based on Gaussian Copula and wavelet decompositions. We take advantage of the copula paradigm, which makes it possible to separate dependence structure from marginal behavior. We introduce two new multivariate models using, respectively, generalized Gaussian and Weibull densities. These models capture both the subband marginal distributions and the correlation between wavelet coefficients. We derive, as a similarity measure, a closed form expression of the Jeffrey divergence between Gaussian copula-based multivariate models. Experimental results on well-known databases show significant improvements in retrieval rates using the proposed method compared with the best known state-of-the-art approaches.
Parameters estimate of Riemannian Gaussian distribution in the manifold of covariance matrices

The study of Pm, the manifold of m × m symmetric positive definite matrices, has recently become widely popular in many engineering applications, like radar signal processing, mechanics, computer vision, image processing, and medical imaging. A large body of literature is devoted to the barycentre of a set of points in Pm and the concept of barycentre has become essential to many applications and procedures, for instance classification of SPD matrices. However this concept is often used alone in order to define and characterize a set of points. Less attention is paid to the characterization of the shape of samples in the manifold, or to the definition of a probabilistic model, to represent the statistical variability of data in Pm. Here we consider Gaussian distributions and mixtures of Gaussian distributions on Pm. In particular we deal with parameter estimation of such distributions. This problem, while it is simple in the manifold P2, becomes harder for higher dimensions, since there are some quantities involved whose analytic expression is difficult to derive. In this paper we introduce a smooth estimate of these quantities using convex cubic splines, and we show that in this case the parameters estimate is coherent with theoretical results. We also present some simulations and a real EEG data analysis.
How to perform texture recognition from stochastic modeling in the wavelet domain

The paper addresses content-based image retrieval from texture databases, by using stochastic modeling in the wavelet domain. It proposes an analysis of the key parameters involved in such a content-based texture retrieval. These parameters are the wavelet order and the goodness-of-fit measure used to select the best family of distributions for modeling the subband wavelet coefficients. It is shown that taking suitable parameters into consideration makes it possible to attain high retrieval rates in content-based texture retrieval.
Multivariate texture retrieval using the geodesic distance between elliptically distributed random variables

This paper presents a new texture retrieval algorithm based on elliptical distributions for the modeling of wavelet sub-bands. For measuring similarity between two texture images, the geodesic distance (GD) is considered. A closed form for fixed shape parameters and an approximation when assuming the geodesic coordinate functions as straight lines are given. Taken into various elliptical choices, the multivariate Laplace and G0 distributions are introduced for modeling respectively the color cue and spatial dependencies of the wavelet coefficients. A multi-model classification approach is then proposed to combine the similarity measures. A comparative study between some multivariate models on the VisTex image database is conducted and reveals that the combination of the multivariate Laplace modeling for the color dependency and the multivariate G0 modeling for spatial one achieves higher recognition rates than other approaches.
Parametric Gaussianization procedure of wavelet coefficients for texture retrieval

In this paper, we deal with the problem of feature extraction in content-based image retrieval (CBIR) using statistical approach. A Gaussianization procedure based on parametric density assumptions of steerable pyramid coefficients is proposed. The extraction method of features including the Gaussianization step allows us to limit the order of the statistical model used to characterize the image textures. The performances of the proposed method are analyzed on a database of texture images and compared with the performances of other texture features proposed in previous works.
An efficient subband decomposition based on the Hilbert transform for high-resolution spectral estimation

This paper deals with high-resolution frequency estimation for narrow-band plane waves. We propose an approach based on subband decomposition in the spectral domain to improve the performance of high-resolution analysis. This decomposition is based on the Hilbert transform for one and two-dimensional signals. This improvement is tested on ESPRIT and MEMP techniques. We first present the subband decomposition based on the Hilbert transform (SDBHT) for one-dimensional (1D) signals. Secondly the SDBHT method is extended to the two-dimensional (2D) case. Finally the advantages of such a method are illustrated with simulation examples.
New methods for determining the number of effective bits of A/D converters based on eigendecomposition

For calculating the number of effective bits, we measure the rms
noise by fitting the ADC's output signal to a curve. Thus, a nonlinear
equation system is obtained and several Newton type iterative algorithms
are used to resolve it. We established that a good estimation of the
frequency for the first computation of an iterative process is
necessary. To remove this problem, we proposed new methods based on the
eigendecomposition of the autocorrelation matrix in the input signal.
The first, using the MUSIC method gives an accurate prime estimation of
the frequency for the model which fits the output data. Then, this value
is introduced in a Newton type algorithm for a good estimation of the
noise. The second, more attractive, provides a direct accurate
estimation of the noise. Condition testing and simulations will be given
to validate these new methods
Unsupervised hyperspectral band selection via multi-feature information-maximization clustering

This paper presents a new approach for unsupervised band selection in the context of hyperspectral imaging. The hyperspectral band selection (HBS) task is considered as a clustering problem: bands are clustered in the image space; one representative image is then kept for each cluster, to be part of the set of selected bands. The proposed clustering method falls into the family of information-maximization clustering, where mutual information between data features and cluster assignments is maximized. Inspired by a clustering method of this family, we adapt it to the HBS problem and extend it to the case of multiple image features. A pixel selection step is also integrated to reduce the spatial support of the feature vectors, thus mitigating the curse of dimensionality. Experiments with different standard data sets show that the bands selected with our algorithm lead to higher classification performance, in comparison with other state-of-the-art HBS methods.
Three-Dimensional Textured Image Blocks Model Based on Wold Decomposition

In this paper, we present a new parametric modeling of three-dimensional homogeneous volumes of textured images (3D texture) based on 3D Wold decomposition for discrete processes. This decomposition additively separates any 3D discrete homogeneous process into three mutually orthogonal components: a purely indeterministic, a "remote past" - type deterministic and an evanescent component. The evanescent component can be further decomposed into two orthogonal components called type 1 evanescent and type 2 evanescent. Assuming that the 3D image volume is a finite realization of a discrete homogeneous stochastic process, the 3D texture field can be uniquely represented as a sum of four mutually orthogonal components with different spectral properties. The aim of this paper is to present new parametric models able to describe both the spectral support and spatial characteristics of each component. A spectral decomposition algorithm to separate a 3D texture into structured and unstructured parts is also proposed.
Texture image classification with Riemannian fisher vectors

This paper introduces a generalization of the Fisher vectors to the Riemannian manifold. The proposed descriptors, called Riemannian Fisher vectors, are defined first, based on the mixture model of Riemannian Gaussian distributions. Next, their expressions are derived and they are applied in the context of texture image classification. The results are compared to those given by the recently proposed algorithms, bag of Riemannian words and R-VLAD. In addition, the most discriminant Riemannian Fisher vectors are identified.
Bidirectional Composition on Lie Groups for Gradient-Based Image Alignment

In this paper, a new formulation based on bidirectional composition on Lie groups (BCL) for parametric gradient-based image alignment is presented. Contrary to the conventional approaches, the BCL method takes advantage of the gradients of both template and current image without combining them a priori. Based on this bidirectional formulation, two methods are proposed and their relationship with state-of-the-art gradient based approaches is fully discussed. The first one, i.e., the BCL method, relies on the compositional framework to provide the minimization of the compensated error with respect to an augmented parameter vector. The second one, the projected BCL (PBCL), corresponds to a close approximation of the BCL approach. A comparative study is carried out dealing with computational complexity, convergence rate and frequence of convergence. Numerical experiments using a conventional benchmark show the performance improvement especially for asymmetric levels of noise, which is also discussed from a theoretical point of view.
Multiple features learning via rotation strategy

Images are usually represented by different groups of features, such as color, shape and texture attributes. In this paper, we propose a classification approach that integrates multiple features, such as spectral and spatial information. We refer this approach to multiple feature learning via rotation (MFL-R) strategy, which adopt a rotation-based ensemble method by using a data transformation approach. Five data transformation methods, including principal component analysis (PCA), neighborhood preserving embedding (NPE), linear local tangent space alignment (LLTSA), linearity preserving projection (LPP) and multiple feature combination via manifold learning and patch alignment (MLPA) are used in the MFL-R framework. Experimental results over two hyperspectral remote sensing images demonstrate that MFL-R with MLPA gains better performances and is not sensitive to the tuning parameters.
Texture classification using Rao's distance: An EM algorithm on the poincaré half plane

This paper presents a new Bayesian approach to texture classification, yielding enhanced performance in the presence of intraclass diversity. From a mathematical point of view, it specifies an original EM algorithm for mixture estimation on Riemannian manifolds, generalising existing, non probabilistic, clustering analysis methods. For texture classification, the chosen feature space is the Riemannian manifold known as the Poincaré half plane, here denoted H, (this is the set of univariate normal distributions, equipped with Rao's distance). Classes are modelled as finite mixtures of Riemannian priors, (Riemannian priors are probability distributions, recently introduced by the authors, which represent clusters of points in H). During the training phase of classification, the EM algorithm, proposed in this paper, computes maximum likelihood estimates of the parameters of these mixtures. The algorithm combines the structure of an EM algorithm for mixture estimation, with a Riemannian gradient descent, for computing weighted Riemannian centres of mass.
Multivariate statistical modeling for texture analysis using wavelet transforms

In the framework of wavelet-based analysis, this paper deals with texture modeling for classification or retrieval systems using non-Gaussian multivariate statistical features. We propose a stochastic model based on Spherically Invariant Random Vectors (SIRVs) joint density function with Weibull assumption to characterize the dependences between wavelet coefficients. For measuring similarity between two texture images, the Kullback-Leibler divergence (KLD) between the corresponding joint distributions is provided. The evaluation of model performance is carried out in the framework of retrieval system in terms of recognition rate. A comparative study between the proposed model and conventional models such as univariate Generalized Gaussian distribution and Multivariate Bessel K forms (MBKF) is conducted.
Stochastic EM algorithm for mixture estimation on manifolds

This paper presents a novel algorithm for estimating parameters of a mixture of Gaussian laws when data lie in a Riemannian manifold. We consider the stochastic variant of the well-known Expectation-Maximization (EM) algorithm in the case of Riemannian geometry. The Riemannian mixture is devoted, here, to the case of Riemannian manifold of Symmetric Positive Definite (SPD) matrices. With a slight modification, the stochastic EM algorithm developed originally for Euclidean case can also be derived for SPD manifold. We provide some MonteCarlo numerical simulations in order to analyse, in details, the proposed algorithm in comparison with the conventional EM one.