nils-herrmann/clean_scirepeval huggingface dataset:
Regularized adaptive long autoregressive spectral analysis

This paper is devoted to adaptive long autoregressive spectral analysis when i) very few data are available and ii) information does exist beforehand concerning the spectral smoothness and time continuity of the analyzed signals. The contribution is founded on two papers by Kitagawa and Gersch (1985). The first one deals with spectral smoothness in the regularization framework, while the second one is devoted to time continuity in the Kalman formalism. The present paper proposes an original synthesis of the two contributions. A new regularized criterion is introduced that takes bath pieces of information into account. The criterion is efficiently optimized by a Kalman smoother. One of the major features of the method is that it is entirely unsupervised. The problem of automatically adjusting the hyperparameters that balance data-based versus prior-based information is solved by maximum likelihood (ML). The improvement is quantified in the field of meteorological radar.
Unsupervised Bayesian EMG decomposition algorithm using tabu search

We introduce here the statistical model of the electromyographic (EMG) signal decomposition problem and then propose an algorithm with a Bayesian approach followed by its simulation data test results. We consider the classical problem of the multi-source impulse discharge separation from intramuscular EMG signals in the case where the impulse responses (motor unit action potentials, MUAPs) are supposed to be known to a certain degree. The main contribution of this work is the proposal of a fully unsupervised EMG decomposition algorithm that exploits both the signal model likelihood and the regularity of the motor unit discharge patterns in a Bayesian framework. The latter, though well-proven properties in the past, is essentially used as auxiliary information in an interactive procedure  involving human interventions. Another contribution consists of using the Tabu metaheuristics to solve the NP-hard problem over the complete search space of overlapped MUAP, unlike the existing methods that either performed on the restrained search spaces [2, 3] due to complexity or based on recursive algorithms [4, 1] with certain trial strategy and residual threshold estimations.
Deconvolution of sparse spike trains accounting for wavelet phase shifts and colored noise

The problem of the restoration of spiky sequences when the usual convolution model is corrupted by nonstationary wavelet phase-shifts is addressed. To this end, an extended convolution model driven by a Bernoulli-Gaussian (BG)-like process is introduced. This setting lends itself to easy extension of algorithms designed for BG deconvolution. A comparison of practical results obtained with this new method and BG deconvolution is provided. Numerical experiments indicate an increased robustness compared with standard BG methods.<<ETX>>
Design of a Chromatic 3D Camera with an End-to-End Performance Model Approach

In this paper we present a new method for the design of a 3D single-lens single-frame passive camera. This camera has a chromatic lens and estimates depth based on a depth from defocus technique (DFD). First we develop an original calculation of the Cramér Rao Bound to predict the theoretical camera accuracy. This model takes into account the optical parameters through the camera Point Spread Function (PSF) and the algorithms parameters applied to the raw image for depth estimation and image restoration. This model is then used for the end-to-end design of a chromatic camera, dedicated to a small UAV, that is realized and experimentally validated.
Algorithms for Nonnegative Matrix Factorization with the β-Divergence

This letter describes algorithms for nonnegative matrix factorization (NMF) with the β-divergence (β-NMF). The β-divergence is a family of cost functions parameterized by a single shape parameter β that takes the Euclidean distance, the Kullback-Leibler divergence, and the Itakura-Saito divergence as special cases (β = 2, 1, 0 respectively). The proposed algorithms are based on a surrogate auxiliary function (a local majorization of the criterion function). We first describe a majorization-minimization algorithm that leads to multiplicative updates, which differ from standard heuristic multiplicative updates by a β-dependent power exponent. The monotonicity of the heuristic algorithm can, however, be proven for β ∈ (0, 1) using the proposed auxiliary function. Then we introduce the concept of the majorization-equalization (ME) algorithm, which produces updates that move along constant level sets of the auxiliary function and lead to larger steps than MM. Simulations on synthetic and real data illustrate the faster convergence of the ME approach. The letter also describes how the proposed algorithms can be adapted to two common variants of NMF: penalized NMF (when a penalty function of the factors is added to the criterion function) and convex NMF (when the dictionary is assumed to belong to a known subspace).
Robust estimation of the hemodynamic response function in asynchronous multitasks multisessions event-related fMRI paradigms

Hemodynamic Response Function (HRF) estimation in noisy functional Magnetic Resonance Imaging (fMRI) is essential for a better understanding of cerebral activations. Previous works have proposed robust non-parametric estimates of the HRF within a regularized framework [C. Goutte et al., 2000, G. Marrelec et al. 2001]. They are not adapted for event-related paradigms that are either asynchronous (in which the onsets of the conditions are not synchronized with the data), or designed with several kinds of trials, or with several sessions. In this paper, we extend the previous works to these three situations. We introduce temporal prior information on the underlying physiological process of the brain hemodynamic response to accurately estimate the HRF in a Bayesian framework. The proposed unsupervised approach is validated on both synthetic and real fMRI data.
3D reconstruction of localized objects from radiographs and based on multiresolution and sparsity

We address the reconstruction of a 3D image from a set of incomplete X-ray tomographic data. In the case where the image is composed of one or several objects lying in a uniform background, we define a sparse parameterization by considering the active voxels, i.e., the voxels that do not lay inside the background. Estimation of the active voxel densities is performed using the maximum a posteriori (MAP) estimator. In order to implement sparse parameter estimation, we design an original multiresolution scheme, which handles coarse to fine resolution images. This scheme affords automatic selection of active voxels at each resolution level, and provides a drastic decrease of the computation time. We finally show the performance of our method on synthetic data.
Fast MCMC computations for the estimation of sparse processes from noisy observations

The paper presents a fast MCMC (Markov chain Monte Carlo) algorithm specially designed for high dimensional models with block structure. Such models are often met in Bayesian inference problems in signal processing, such as spectral estimation, harmonic analysis, blind deconvolution or signal classification. Our algorithm generates samples distributed according to a posterior distribution. We show that sampling the amplitudes together with the remaining model parameters leads to quicker computations than sampling from the marginal posterior, where amplitudes have been integrated out. Simulation results demonstrate the soundness of this approach for high dimensional models.
Primal-dual interior point optimization for a regularized reconstruction of NMR relaxation time distributions

This paper deals with the reconstruction of relaxation time distributions in Nuclear Magnetic Resonance (NMR) spectroscopy. This large scale and ill-posed inverse problem is solved by the iterative minimization of a regularized objective function allowing to encode some prior assumptions on the sought distribution. The numerical optimization of the criterion is performed using a primal-dual interior point algorithm allowing to handle the non-negativity constraint. The performances of the proposed approach are illustrated through the processing of real data from a two-dimensional NMR experiment.
Unsupervised robust nonparametric estimation of the hemodynamic response function for any fMRI experiment

This paper deals with the estimation of the blood oxygen level-dependent response to a stimulus, as measured in functional magnetic resonance imaging (fMRI) data. A precise estimation is essential for a better understanding of cerebral activations. The most recent works have used a nonparametric framework for this estimation, considering each brain region as a system characterized by its impulse response, the so-called hemodynamic response function (HRF). However, the use of these techniques has remained limited since they are not well-adapted to real fMRI data. Here, we develop a threefold extension to previous works. We consider asynchronous event-related paradigms account for different trial types and integrate several fMRI sessions into the estimation. These generalizations are simultaneously addressed through a badly conditioned observation model. Bayesian formalism is used to model temporal prior information of the underlying physiological process of the brain hemodynamic response. By this way, the HRF estimate results from a tradeoff between information brought by the data and by our prior knowledge. This tradeoff is modeled with hyperparameters that are set to the maximum-likelihood estimate using an expectation conditional maximization algorithm. The proposed unsupervised approach is validated on both synthetic and real fMRI data, the latter originating from a speech perception experiment.
Spatially unsupervised analysis of within-subject FMRI data using multiple extrapolations of 3D Ising field Partition Functions

In this paper, we present a fast numerical scheme to estimate Partition Functions (PF) of symmetric Ising fields. Our strategy is first validated on 2D Ising fields. and then applied to the joint detection-estimation of brain activity fromfunctional Magnetic Resonance Imaging (fMRI) data, where the goal is to automatically recover activated regions and estimate the region-dependent hemodynamic filter. For any region, a specific 3D Ising field may embody spatial correlation over the hidden states of the voxels by modeling whether they are activated or not. To make spatial regularization adaptive, our approach is first based upon a classical path sampling method to approximate a small subset of reference PFs corresponding to prespecified regions. Then, we propose an extrapolation method that allows us to approximate the PFs associated to the Ising fields defined over the remaining brain regions. In comparisonwith preexisting approaches, ourmethod is robust against grid inhomogeneities within the reference PFs and remains efficient irrespective of the topological configurations of the reference and test regions. Our contribution strongly alleviates the computational cost and makes spatially adaptive regularization of whole brain fMRI datasets feasible.
Semi-blind deconvolution of neural impulse response in event-related fMRI using Gibbs sampler

Identification of the hemodynamic response function (HRF) in functional magnetic resonance imaging (fMRI) is essential for a better understanding of cerebral activity. In a given region-of-interest (ROI) of the brain, several works have demonstrated that neighbor voxels have close shape responses, but with potentially different magnitude. This property of similarity also holds between HRFs corresponding to different stimuli in the paradigm. In this paper, we account for these features in a given ROI and estimate a single HRF shape but several magnitude coefficients per voxel and condition, which may be thought of representing the neural response level. Identified as a semiblind deconvolution inverse problem, we use a Gibbs sampling approach to estimate the unknown quantities. Our method, validated both on synthetic and real fMRI data provides spatial activation maps in a given ROI with no assumptions on the exact shape of the HRF.
Spike Sorting by Stochastic Simulation

The decomposition of multiunit signals consists of the restoration of spike trains and action potentials in neural or muscular recordings. Because of the complexity of automatic decomposition, semiautomatic procedures are sometimes chosen. The main difficulty in automatic decomposition is the resolution of temporally overlapped potentials. In a previous study , we proposed a Bayesian model coupled with a maximum a posteriori (MAP) estimator for fully automatic decomposition of multiunit recordings and we showed applications to intramuscular EMG signals. In this study, we propose a more complex signal model that includes the variability in amplitude of each unit potential. Moreover, we propose the Markov Chain Monte Carlo (MCMC) simulation and a Bayesian minimum mean square error (MMSE) estimator by averaging on samples that converge in distribution to the joint posterior law. We prove the convergence property of this approach mathematically and we test the method representatively on intramuscular multiunit recordings. The results showed that its average accuracy in spike identification is greater than 90% for intramuscular signals with up to 8 concurrently active units. In addition to intramuscular signals, the method can be applied for spike sorting of other types of multiunit recordings.
Parallel implementation of a primal-dual interior-point optimization method for fast abundance maps estimation

Abundance maps estimation in hyperspectral imaging requires the resolution of a large constrained optimization problem. Recently, a primal-dual interior-point algorithm has been proven to solve efficiently this problem. Despite its relatively fast convergence, the computation time remains a major issue for large scale images. We propose here an optimization of the GPU (Graphics Processing Unit) implementation of this method. The discussion covers the difference between parallelizing the entire algorithm or every step separately, the way of using the best of both the GPU and the CPU, and the memory organization issues. Simulated data are used to illustrate the effect of these modifications on computing time. An extensive application involving a large set of data from the European planetary mission Mars Express is presented.
Eigenregions for image classification

For certain databases and classification tasks, analyzing images based on region features instead of image features results in more accurate classifications. We introduce eigenregions, which are geometrical features that encompass area, location, and shape properties of an image region, even if the region is spatially incoherent. Eigenregions are calculated using principal component analysis (PCA). On a database of 77,000 different regions obtained through the segmentation of 13,500 real-scene photographic images taken by nonprofessional, eigenregions improved the detection of localized image classes by a noticeable amount. Additionally, eigenregions allow us to prove that the largest variance in natural image region geometry is due to its area and not to shape or position.
Semantic-Improved Color Imaging Applications: It Is All About Context

Multimedia data with associated semantics is omnipresent in today's social online platforms in the form of keywords, user comments, and so forth. This article presents a statistical framework designed to infer knowledge in the imaging domain from the semantic domain. Note that this is the reverse direction of common computer vision applications. The framework relates keywords to image characteristics using a statistical significance test. It scales to millions of images and hundreds of thousands of keywords. We demonstrate the usefulness of the statistical framework with three color imaging applications: 1) semantic image enhancement: re-render an image in order to adapt it to its semantic context; 2) color naming: find the color triplet for a given color name; and 3) color palettes: find a palette of colors that best represents a given arbitrary semantic context and that satisfies established harmony constraints.
Preserving perceptual contrast in decolorization with optimized color orders

Converting a color image to a grayscale image, namely decolorization, is an important process for many real-world applications. Previous methods build contrast loss functions to minimize the contrast differences between the color images and the resultant grayscale images. In this paper, we improve upon a widely used decolorization method with two extensions. First, we relax the need for heuristics on color orders, which the baseline method relies on when computing the contrast differences. In our method, the color orders are incorporated into the loss function and are determined through optimization. Moreover, we apply a nonlinear function on the grayscale contrast to better model human perception of contrast. Both qualitative and quantitative results on the standard benchmark demonstrate the effectiveness of our two extensions.
Single Image Reflection Suppression

Reflections are a common artifact in images taken through glass windows. Automatically removing the reflection artifacts after the picture is taken is an ill-posed problem. Attempts to solve this problem using optimization schemes therefore rely on various prior assumptions from the physical world. Instead of removing reflections from a single image, which has met with limited success so far, we propose a novel approach to suppress reflections. It is based on a Laplacian data fidelity term and an l-zero gradient sparsity term imposed on the output. With experiments on artificial and real-world images we show that our reflection suppression method performs better than the state-of-the-art reflection removal techniques.
Image aesthetic predictors based on weighted CNNs

Convolutional Neural Networks (CNNs) have been widely adopted for many imaging applications. For image aesthetics prediction, state-of-the-art algorithms train CNNs on a recently-published large-scale dataset, AVA. However, the distribution of the aesthetic scores on this dataset is extremely unbalanced, which limits the prediction capability of existing methods. We overcome such limitation by using weighted CNNs. We train a regression model that improves the prediction accuracy of the aesthetic scores over state-of-the-art algorithms. In addition, we propose a novel histogram prediction model that not only predicts the aesthetic score, but also estimates the difficulty of performing aesthetics assessment for an input image. We further show an image enhancement application where we obtain an aesthetically pleasing crop of an input image using our regression model.
Automatic detection of dust and scratches in silver halide film using polarized dark-field illumination

We present a method to automatically detect dust and scratches on photographic material, in particular silver halide film, where traditional methods for detecting and removing defects fail. The film is digitized using a novel setup involving cross-polarization and dark-field illumination in a cardinal light configuration, which compresses the signal and highlights the parts that are due to defects in the film. Applying a principal component analysis (PCA) on the four cardinal images allows us to further separate the signal part of the film from the defects. Information from all four principal components is combined to produce a surface defect mask, which can be used as input to inpainting methods to remove the defects. Our method is able to detect most of the dust and scratches while keeping false-detections low.
Higher Order SVD Analysis for Dynamic Texture Synthesis

Videos representing flames, water, smoke, etc., are often defined as dynamic textures: "textures" because they are characterized by the redundant repetition of a pattern and "dynamic" because this repetition is also in time and not only in space. Dynamic textures have been modeled as linear dynamic systems by unfolding the video frames into column vectors and describing their trajectory as time evolves. After the projection of the vectors onto a lower dimensional space by a singular value decomposition (SVD), the trajectory is modeled using system identification techniques. Synthesis is obtained by driving the system with random noise. In this paper, we show that the standard SVD can be replaced by a higher order SVD (HOSVD), originally known as Tucker decomposition. HOSVD decomposes the dynamic texture as a multidimensional signal (tensor) without unfolding the video frames on column vectors. This is a more natural and flexible decomposition, since it permits us to perform dimension reduction in the spatial, temporal, and chromatic domain, while standard SVD allows for temporal reduction only. We show that for a comparable synthesis quality, the HOSVD approach requires, on average, five times less parameters than the standard SVD approach. The analysis part is more expensive, but the synthesis has the same cost as existing algorithms. Our technique is, thus, well suited to dynamic texture synthesis on devices limited by memory and computational power, such as PDAs or mobile phones.
Face recognition in real-world images

Face recognition systems are designed to handle well-aligned images captured under controlled situations. However real-world images present varying orientations, expressions, and illumination conditions. Traditional face recognition algorithms perform poorly on such images. In this paper we present a method for face recognition adapted to real-world conditions that can be trained using very few training examples and is computationally efficient. Our method consists of performing a novel alignment process followed by classification using sparse representation techniques. We present our recognition rates on a difficult dataset that represents real-world faces where we significantly outperform state-of-the-art methods.
Frequency-tuned salient region detection

Detection of visually salient image regions is useful for applications like object segmentation, adaptive compression, and object recognition. In this paper, we introduce a method for salient region detection that outputs full resolution saliency maps with well-defined boundaries of salient objects. These boundaries are preserved by retaining substantially more frequency content from the original image than other existing techniques. Our method exploits features of color and luminance, is simple to implement, and is computationally efficient. We compare our algorithm to five state-of-the-art salient region detection methods with a frequency domain analysis, ground truth, and a salient object segmentation application. Our method outperforms the five algorithms both on the ground-truth evaluation and on the segmentation task by achieving both higher precision and better recall.
Seam Carving for Text Line Extraction on Color and Grayscale Historical Manuscripts

We propose a novel algorithm for automatic text line extraction on color and gray scale manuscript pages without prior binarization. Our algorithm is based on seam carving to compute separating seams between text lines. Seam carving is likely to produce seams that move through gaps between neighboring lines, if no information about the text geometry is incorporated into the problem. By constraining the optimization procedure inside the region between two consecutive text lines, we can produce robust separating seams that do not cut through word and line components. Extensive experimental evaluations on diverse manuscript pages show that we improve upon the state-of-the-art for grayscale text line extraction.