nils-herrmann/clean_scirepeval huggingface dataset:
Deconvolving Images With Unknown Boundaries Using the Alternating Direction Method of Multipliers

The alternating direction method of multipliers (ADMM) has recently sparked interest as a flexible and efficient optimization tool for inverse problems, namely, image deconvolution and reconstruction under non-smooth convex regularization. ADMM achieves state-of-the-art speed by adopting a divide and conquer strategy, wherein a hard problem is split into simpler, efficiently solvable sub-problems (e.g., using fast Fourier or wavelet transforms, or simple proximity operators). In deconvolution, one of these sub-problems involves a matrix inversion (i.e., solving a linear system), which can be done efficiently (in the discrete Fourier domain) if the observation operator is circulant, i.e., under periodic boundary conditions. This paper extends ADMM-based image deconvolution to the more realistic scenario of unknown boundary, where the observation operator is modeled as the composition of a convolution (with arbitrary boundary conditions) with a spatial mask that keeps only pixels that do not depend on the unknown boundary. The proposed approach also handles, at no extra cost, problems that combine the recovery of missing pixels (i.e., inpainting) with deconvolution. We show that the resulting algorithms inherit the convergence guarantees of ADMM and illustrate its performance on non-periodic deblurring (with and without inpainting of interior pixels) under total-variation and frame-based regularization.
Parameter Estimation for Blind and Non-Blind Deblurring Using Residual Whiteness Measures

Image deblurring (ID) is an ill-posed problem typically addressed by using regularization, or prior knowledge, on the unknown image (and also on the blur operator, in the blind case). ID is often formulated as an optimization problem, where the objective function includes a data term encouraging the estimated image (and blur, in blind ID) to explain the observed data well (typically, the squared norm of a residual) plus a regularizer that penalizes solutions deemed undesirable. The performance of this approach depends critically (among other things) on the relative weight of the regularizer (the regularization parameter) and on the number of iterations of the algorithm used to address the optimization problem. In this paper, we propose new criteria for adjusting the regularization parameter and/or the number of iterations of ID algorithms. The rationale is that if the recovered image (and blur, in blind ID) is well estimated, the residual image is spectrally white; contrarily, a poorly deblurred image typically exhibits structured artifacts (e.g., ringing, oversmoothness), yielding residuals that are not spectrally white. The proposed criterion is particularly well suited to a recent blind ID algorithm that uses continuation, i.e., slowly decreases the regularization parameter along the iterations; in this case, choosing this parameter and deciding when to stop are one and the same thing. Our experiments show that the proposed whiteness-based criteria yield improvements in SNR, on average, only 0.15 dB below those obtained by (clairvoyantly) stopping the algorithm at the best SNR. We also illustrate the proposed criteria on non-blind ID, reporting results that are competitive with state-of-the-art criteria (such as Monte Carlo-based GSURE and projected SURE), which, however, are not applicable for blind ID.
Content-based hierarchical classification of vacation images

Grouping images into (semantically) meaningful categories using low level visual features is a challenging and important problem in content based image retrieval. Using binary Bayesian classifiers, we attempt to capture high level concepts from low level image features under the constraint that the test image does belong to one of the classes of interest. Specifically, we consider the hierarchical classification of vacation images; at the highest level, images are classified into indoor/outdoor classes, outdoor images are further classified into city/landscape classes, and finally, a subset of landscape images is classified into sunset, forest, and mountain classes. We demonstrate that a small codebook (the optimal size of codebook is selected using a modified MDL criterion) extracted from a vector quantizer can be used to estimate the class-conditional densities of the observed features needed for the Bayesian methodology. On a database of 6931 vacation photographs, our system achieved an accuracy of 90.5% for indoor vs. outdoor classification, 95.3% for city vs. landscape classification, 96.6% for sunset vs. forest and mountain classification, and 95.5% for forest vs. mountain classification. We further develop a learning paradigm to incrementally train the classifiers as additional training samples become available and also show preliminary results for feature size reduction using clustering techniques.
Adaptive B-splines and boundary estimation

This paper describes a boundary estimation scheme based on a new adaptive approach to B-spline curve fitting. The number of control points of the spline, their locations, and the observation parameters, are all considered unknown. The optimal number of control points is estimated via a new minimum description length (MDL) type criterion. The result is an adaptive parametrically deformable contour which also estimates the observation model parameters. Experiments on synthetic and real (medical) images confirm the adequacy and good performance of the approach.
Bayesian estimation of ventricular contours in angiographic images

A method for left ventricular contour determination in digital angiographic images is presented. The problem is formulated in a Bayesian framework, adopting as the estimation criterion the maximum a posterior probability (MAP). The true contour is modeled as a one-dimensional noncausal Gauss-Markov random field and the observed image is described as the superposition of an ideal image (deterministic function of the real contour) with white Gaussian noise. The proposed algorithm estimates simultaneously the contour and the model parameters by implementing an adaptive version of the iterated conditional modes algorithm. The convergence of this scheme is proved and its performance evaluated on both synthetic and real angiographic images. The method exhibits robustness against image artifacts and the contours obtained are considered good by expert clinicians. Being completely data-driven and fast, the proposed algorithm is suitable for routine clinical use.
Class-Adapted Blind Deblurring of Document Images

Deblurring of document images is an important problem, with several relevant applications, such as camera-based document acquisition and processing systems. Consequently, considerable attention has been given to this problem, namely in the blind image deblurring (BID) scenario, where the blurring filter is (partially or fully) unknown. Traditional BID methods can be used for document images, but this is far from optimal, since those methods are tailored to natural images, that is, they rely on statistical properties of natural images. This has lead to the proposal of a few special-purpose techniques, namely by exploiting properties of text images. In fact, in document images, the most prevalent type of content is text, but in some cases, it is not the only one, with the other types being very different from text. For example, identity documents typically contain faces and/or fingerprints, which are not adequately treated by methods designed for images of text. In this work, we propose a new method for BID of documents, supported on a class-adapted dictionary-based prior (learned from one or more sets of clean images of specific classes) for the image and a sparsity-inducing prior on the (unknown) blurring filter. This approach handles document images that contain two or more image classes (e.g., text and faces) which is a main contribution of our work. Experiments with document images containing both text and faces show the competitiveness of the proposed method in terms of restoration quality. Additionally, our experiments show that the proposed method is able to handle images with strong noise, outperforming state-of-the-art methods designed for BID of text images.
Semi-Supervised Learning of Switched Dynamical Models for Classification of Human Activities in Surveillance Applications

This work introduces a semi-supervised approach for learning generative models for classification/recognition of human trajectories, with application to surveillance. The classifier is based on switched dynamical models, with each model describing a specific motion regime. We present a semi-supervised modified version of the classical Baum-Welch algorithm, which is able to take into account a subset of known model labels. The experimental results reported, using both synthetic and real data, show that the classifier learned with semi-supervision leads to a higher classification accuracy than the fully unsupervised version, thus validating the proposed approach.
Novel fiducial and non-fiducial approaches to electrocardiogram-based biometric systems

The electrocardiogram (ECG) is a non-invasive and widely used technique for cardiac electrophysiological assessment. Although the ECG has traditionally only been used for functional diagnostic and evaluation, several advances in electrophysiological sensing have made available robust signal acquisition devices, particularly suited for ambulatory conditions, widening its range of applications. In particular, recent work has shown the potential of the ECG as a biometric trait, both for human identification and authentication. This study sets the ground for an ECG-based real-time biometric system. The authors describe an experimental setup and the evaluation of new fiducial and non-fiducial approaches, including data acquisition, signal processing, feature extraction and analysis and classification methodologies, showing the applicability of the ECG as a real-time biometric. Performance evaluation was done in clinical-grade ECG recording from 51 healthy control individuals (of a publicly available benchmark dataset) as well as on data collected from 26 healthy volunteers performing computer activities without any posture or motion limitations, thus simulating a regular computer usage scenario.
Unsupervised selection and estimation of finite mixture models

We describe a method for fitting mixture models to multivariate data which performs component selection and does not require external initialization. The novelty of our approach includes: an MML-like (minimum message length) model selection criterion; inclusion of the criterion into the expectation-maximization (EM) algorithm (increasing its ability to escape from local maxima); an initialization strategy supported on the interpretation of EM as a self-annealing algorithm.
On Total Variation Denoising: A New Majorization-Minimization Algorithm and an Experimental Comparisonwith Wavalet Denoising

Image denoising is a classical problem which has been addressed using a variety of conceptual frameworks and computational tools. Most approaches use some form of penalty/prior as a regularizer, expressing a preference for images with some form of (generalized) "smoothness". Total variation (TV) and wavelet-based methods have received a great deal of attention in the last decade and are among the state of the art in this problem. However, as far as we know, no experimental studies have been carried out, comparing the relative performance of the two classes of methods. In this paper, we present the results of such a comparison. Prior to that, we introduce a new majorization-minimization algorithm to implement the TV denoising criterion. We conclude that TV is outperformed by recent state of the art wavelet-based denoising methods, but performs competitively with older wavelet-based methods.
On Gaussian radial basis function approximations: interpretation, extensions, and learning strategies

We focus on an interpretation of Gaussian radial basis functions (GRBF) which motivates extensions and learning strategies. Specifically, we show that GRBF regression equations naturally result from representing the input-output joint probability density function by a finite mixture of Gaussian. Corollaries of this interpretation are: some special forms of GRBF representations can be traced back to the type of Gaussian mixture used; previously proposed learning methods based on input-output clustering have a new learning; and estimation techniques for finite mixtures (namely the EM algorithm and model selection criteria) can be invoked to learn GRBF regression equations.
Trajectory Classification Using Switched Dynamical Hidden Markov Models

This paper proposes an approach for recognizing human activities (more specifically, pedestrian trajectories) in video sequences, in a surveillance context. A system for automatic processing of video information for surveillance purposes should be capable of detecting, recognizing, and collecting statistics of human activity, reducing human intervention as much as possible. In the method described in this paper, human trajectories are modeled as a concatenation of segments produced by a set of low level dynamical models. These low level models are estimated in an unsupervised fashion, based on a finite mixture formulation, using the expectation-maximization (EM) algorithm; the number of models is automatically obtained using a minimum message length (MML) criterion. This leads to a parsimonious set of models tuned to the complexity of the scene. We describe the switching among the low-level dynamic models by a hidden Markov chain; thus, the complete model is termed a switched dynamical hidden Markov model (SD-HMM). The performance of the proposed method is illustrated with real data from two different scenarios: a shopping center and a university campus. A set of human activities in both scenarios is successfully recognized by the proposed system. These experiments show the ability of our approach to properly describe trajectories with sudden changes.
Recognition of human activities using space dependent switched dynamical models

This paper describes a new algorithm for the recognition of human activities. These activities are modelled using banks of switched dynamical models, each of which is tailored to a specific motion regime. Furthermore, it is assumed that model switching happens according to a space-dependent Markov chain, i.e., some transitions are more probable in specific regions of the image. Space dependence allows the model to represent the interaction between the person and static elements of the scene. The paper describes learning algorithms for space-dependent switched dynamical models and presents experimental results with synthetic and real data.
Adaptive Relaxed ADMM: Convergence Theory and Practical Implementation

Many modern computer vision and machine learning applications rely on solving difficult optimization problems that involve non-differentiable objective functions and constraints. The alternating direction method of multipliers (ADMM) is a widely used approach to solve such problems. Relaxed ADMM is a generalization of ADMM that often achieves better performance, but its efficiency depends strongly on algorithm parameters that must be chosen by an expert user. We propose an adaptive method that automatically tunes the key algorithm parameters to achieve optimal performance without user oversight. Inspired by recent work on adaptivity, the proposed adaptive relaxed ADMM (ARADMM) is derived by assuming a Barzilai-Borwein style linear gradient. A detailed convergence analysis of ARADMM is provided, and numerical results on several applications demonstrate fast practical convergence.
Wavelet-based adaptive image deconvolution

This paper introduces an adaptive expectation-maximization (EM) algorithm for image restoration (deconvolution) formulated in the wavelet domain. The observed image is assumed to be a convolved and noisy version of the original image to be estimated. The restoration process is supported on prior knowledge about the original image, expressed in the wavelet coefficients, taking advantage of the sparsity of wavelet representations. Although similar formulations have been considered before, the resulting optimization problems have been computationally demanding and require offline tuning. The EM algorithm herein proposed combines the efficient image/signal representation offered by the discrete wavelet transform (OWT) with the diagonalization of the convolution operator provided by the discrete Fourier transform (OFT). The result is a very efficient iterative algorithm that requires D (N log N) operations per iteration. Moreover, by using a recently proposed parameter-free wavelet-domain prior, and by including the estimation of the noise variance in the EM steps, the resulting algorithm is fully data-adaptive.
Image classification for content-based indexing

Grouping images into (semantically) meaningful categories using low-level visual features is a challenging and important problem in content-based image retrieval. Using binary Bayesian classifiers, we attempt to capture high-level concepts from low-level image features under the constraint that the test image does belong to one of the classes. Specifically, we consider the hierarchical classification of vacation images; at the highest level, images are classified as indoor or outdoor; outdoor images are further classified as city or landscape; finally, a subset of landscape images is classified into sunset, forest, and mountain classes. We demonstrate that a small vector quantizer (whose optimal size is selected using a modified MDL criterion) can be used to model the class-conditional densities of the features, required by the Bayesian methodology. The classifiers have been designed and evaluated on a database of 6931 vacation photographs. Our system achieved a classification accuracy of 90.5% for indoor/outdoor, 95.3% for city/landscape, 96.6% for sunset/forest and mountain, and 96% for forest/mountain classification problems. We further develop a learning method to incrementally train the classifiers as additional data become available. We also show preliminary results for feature reduction using clustering techniques. Our goal is to combine multiple two-class classifiers into a single hierarchical classifier.
Unsupervised image restoration and edge location using compound Gauss-Markov random fields and the MDL principle

Discontinuity-preserving Bayesian image restoration typically involves two Markov random fields: one representing the image intensities/gray levels to be recovered and another one signaling discontinuities/edges to be preserved. The usual strategy is to perform joint maximum a posterori (MAP) estimation of the image and its edges, which requires the specification of priors for both fields. Instead of taking an edge prior, we interpret discontinuities (in fact their locations) as deterministic unknown parameters of the compound Gauss-Markov random field (CGMRF), which is assumed to model the intensities. This strategy should allow inferring the discontinuity locations directly from the image with no further assumptions. However, an additional problem emerges: the number of parameters (edges) is unknown. To deal with it, we invoke the minimum description length (MDL) principle; according to MDL, the best edge configuration is the one that allows the shortest description of the image and its edges. Taking the other model parameters (noise and CGMRF variances) also as unknown, we propose a new unsupervised discontinuity-preserving image restoration criterion. Implementation is carried out by a continuation-type iterative algorithm which provides estimates of the number of discontinuities, their locations, the noise variance, the original image variance, and the original image itself (restored image). Experimental results with real and synthetic images are reported.
Bayesian learning of sparse classifiers

Bayesian approaches to supervised learning use priors on the classifier parameters. However, few priors aim at achieving "sparse" classifiers, where irrelevant/redundant parameters are automatically set to zero. Two well-known ways of obtaining sparse classifiers are: use a zero-mean Laplacian prior on the parameters, and the "support vector machine" (SVM). Whether one uses a Laplacian prior or an SVM, one still needs to specify/estimate the parameters that control the degree of sparseness of the resulting classifiers. We propose a Bayesian approach to learning sparse classifiers which does not involve any parameters controlling the degree of sparseness. This is achieved by a hierarchical-Bayes interpretation of the Laplacian prior, followed by the adoption of a Jeffreys' non-informative hyper-prior Implementation is carried out by an EM algorithm. Experimental evaluation of the proposed method shows that it performs competitively with (often better than) the best classification techniques available.
Unsupervised contour representation and estimation using B-splines and a minimum description length criterion

This paper describes a new approach to adaptive estimation of parametric deformable contours based on B-spline representations. The problem is formulated in a statistical framework with the likelihood function being derived from a region-based image model. The parameters of the image model, the contour parameters, and the B-spline parameterization order (i.e., the number of control points) are all considered unknown. The parameterization order is estimated via a minimum description length (MDL) type criterion. A deterministic iterative algorithm is developed to implement the derived contour estimation criterion, the result is an unsupervised parametric deformable contour: it adapts its degree of smoothness/complexity (number of control points) and it also estimates the observation (image) model parameters. The experiments reported in the paper, performed on synthetic and real (medical) images, confirm the adequate and good performance of the approach.
Adaptive contour estimation with genetic algorithms

We introduce a fully adaptive active contour which is based on elliptic Fourier contour description and on the minimum description length (MDL) principle. The proposed technique estimates the order of the contour description (number of Fourier coefficients), and the contour itself. The search is performed by means of a genetic algorithm. Preliminary results are presented for simple synthetic images with a specified pixel distribution, which testify the performance of the proposed scheme. These results are the first step towards a fully unsupervised contour estimation.
Robust Sparse Recovery in Impulsive Noise via Continuous Mixed Norm

This letter investigates the problem of sparse signal recovery in the presence of additive impulsive noise. The heavy-tailed impulsive noise is well modeled with stable distributions. Since there is no explicit formula for the probability density function of <inline-formula><tex-math notation="LaTeX">$S\alpha S$</tex-math></inline-formula> distribution, alternative approximations are used, such as, generalized Gaussian distribution, which imposes <inline-formula><tex-math notation="LaTeX">$\ell _{p}$</tex-math></inline-formula>-norm fidelity on the residual error. In this letter, we exploit a continuous mixed norm (CMN) for robust sparse recovery instead of <inline-formula> <tex-math notation="LaTeX">$\ell _{p}$</tex-math></inline-formula>-norm. We show that in blind conditions, i.e., in the case where the parameters of the noise distribution are unknown, incorporating CMN can lead to near-optimal recovery. We apply alternating direction method of multipliers for solving the problem induced by utilizing CMN for robust sparse recovery. In this approach, CMN is replaced with a surrogate function and the majorization–minimization technique is incorporated to solve the problem. Simulation results confirm the efficiency of the proposed method compared to some recent algorithms for robust sparse recovery in impulsive noise.