nils-herrmann/clean_scirepeval huggingface dataset:
An Unbiased Technique For Automatic Estimation Of Vessel Contours In Angiograms

AbatTactAccurate and fully automatic assessment of artery dimensions in angiograms has been sought as a diagnostic tool, in particular for coronary heart disease. We propose a new technique to estimate vessel borders in angiographic images. Unlike in previous approaches, the obtained edge estimates are unbiased, this being of primordial importance since quantitative analysis is the goal. Another important feature of the proposed estimator is that no constant background is assumed, making it well suited for noxisubtracted angiograms. The key aspect of our approach is that the smoothness constraint is not used to smooth or i n some other way modify the estimates directly derived from the image (which would introduce bias) but rather elect (without modifying) candidate estimates. As a result, tlie selected points, if correct, are unbiased estimates. Even at low contrast segments and in tlie vicinity of artifacts, the true border points still correspond to (possibly faint) local maxima of the edge operator, wliicli can be correctly chosen if the surrounding context is taken into account. Robustness against unknown background is provided by tlie use a morphological edge detector rather than some linear operator such as a matched filter wliicli assumes flat background.
Adaptive discontinuity location in image restoration

Discontinuity-preserving Bayesian image restoration, based on Markov random fields (MRF), involves an intensity field, representing the image to be restored, and an edge (discontinuity) field. The usual strategy is to perform joint maximum a posteriori (MAP) estimation of the intensity and discontinuity fields, this requiring the specification of Bayesian priors. Departing from this approach, we interpret the discontinuity locations as deterministic unknown parameters of the intensity field. This leads to a parameter estimation problem with the important feature of having an unknown number of parameters. We introduce a discontinuity-preserving image restoration criterion (and an algorithm to implement it) based on the minimum description length (MDL) principle and built upon a compound Gauss-Markov random field (CGMRF) model; the proposed formulation does not involve the specification of a prior for the edge field which is adaptively inferred from the data.<<ETX>>
Robust binary fused compressive sensing using adaptive outlier pursuit

We propose a new method, robust binary fused compressive sensing (RoBFCS), to recover sparse piece-wise smooth signals from 1-bit compressive measurements. The proposed method is a modification of our previous binary fused compressive sensing (BFCS) algorithm, which is based on the binary iterative hard thresholding (BIHT) algorithm. As in BIHT, the data term of the objective function is a one-sided norm. Experiments show that the proposed algorithm is able to take advantage of the piece-wise smoothness of the original signal and detect sign flips and correct them, achieving more accurate recovery than BFCS and BIHT.
Blind image deblurring using class-adapted image priors

Blind image deblurring (BID) is an ill-posed inverse problem, usually addressed by imposing prior knowledge on the (unknown) image and on the blurring filter. Most of the work on BID has focused on natural images, using image priors based on statistical properties of generic natural images. However, in many applications, it is known that the image being recovered belongs to some specific class (e.g., text, face, fingerprints), and exploiting this knowledge allows obtaining more accurate priors. In this work, we propose a method where a Gaussian mixture model (GMM) is used to learn a class-adapted prior, by training on a dataset of clean images of that class. Experiments show the competitiveness of the proposed method in terms of restoration quality when dealing with images containing text, faces, or fingerprints. Additionally, experiments show that the proposed method is able to handle text images at high noise levels, outperforming state-of-the-art methods specifically designed for BID of text images.
Automatic contour estimation in fetal ultrasound images

This paper describes a new method for automatic estimation of the contours of the femur and of the cranial cross-section in fetal ultrasound images. Our approach can be described as a region-based maximum likelihood formulation of parametric deformable contours. This formulation provides robustness against the poor image quality, and allows simultaneous estimation of the contour parameters together with other parameters of the model. Implementation is carried out by a deterministic iterative algorithm with minimal user intervention. Experimental results testify for the very good performance of the approach.
New stopping criteria for iterative blind image deblurring based on residual whiteness measures

Blind image deblurring (BID) is a challenging ill-posed inverse problem. Most BID methods work by imposing some regularization on the unknown blur, and use iterative optimization schemes. Recently, a method was proposed that, although not requiring prior knowledge on the blurring filter, achieves state-of-the-art performance on a wide class of real-world BID problems. The drawback of that method is that the iterations have to be manually stopped. In this paper, we propose new stopping criteria for iterative BID algorithms. The rationale behind the proposed criteria is that if the blur filter is well estimated, the residual will be spectrally white, whereas with a wrong filter, the image estimate will exhibit structured artifacts, which are not white. Comprehensive experiments using the proposed criteria to stop the method mentioned in the previous paragraph show that it yields, on average, an ISNR only 0.16dB (∼ 3%) below what is obtained by stopping the algorithm at the best ISNR (which, of course, can't be done in practice).
A nonsmoothing approach to the estimation of vessel contours in angiograms

Accurate and fully automatic assessment of vessel (stenoses) dimensions in angiographic images has been sought as a diagnostic tool, in particular for coronary heart disease. Here, the authors propose a new technique to estimate vessel borders in angiographic images, a necessary first step of any automatic analysis system. Unlike in previous approaches, the obtained edge estimates are not artificially smoothed; this is extremely important since quantitative analysis is the goal. Another important feature of the proposed technique is that no constant background is assumed, this making it well suited for nonsubtracted angiograms. The key aspect of the authors' approach is that continuity/smoothness constraints are not used to modify the estimates directly derived from the image (which would introduce distortion) but rather to elect (without modifying) candidate estimates. Robustness against unknown background is provided by the use a morphological edge operator, instead of some linear operator (such as a matched filter) which has to assume known background and known vessel shape.
Absolute phase image reconstruction: a stochastic nonlinear filtering approach

This paper formulates and proposes solutions to the problem of estimating/reconstructing the absolute (not simply modulo-2pi) phase of a complex random field from noisy observations of its real and imaginary parts. This problem is representative of a class of important imaging techniques such as interferometric synthetic aperture radar, optical interferometry, magnetic resonance imaging, and diffraction tomography. We follow a Bayesian approach; then, not only a probabilistic model of the observation mechanism, but also prior knowledge concerning the (phase) image to be reconstructed, are needed. We take as prior a nonsymmetrical half plane autoregressive (NSHP AR) Gauss-Markov random field (GMRF). Based on a reduced order state-space formulation of the (linear) NSHP AR model and on the (nonlinear) observation mechanism, a recursive stochastic nonlinear filter is derived, The corresponding estimates are compared with those obtained by the extended Kalman-Bucy filter, a classical linearizing approach to the same problem. A set of examples illustrate the effectiveness of the proposed approach.
One-Lead ECG-based Personal Identification Using Ziv-Merhav Cross Parsing

The advance of falsification technology increases security concerns and gives biometrics an important role in security solutions. The electrocardiogram (ECG) is an emerging biometric that does not need liveliness verification. There is strong evidence that ECG signals contain sufficient discriminative information to allow the identification of individuals from a large population. Most approaches rely on ECG data and the fiducia of different parts of the heartbeat waveform. However non-fiducial approaches have proved recently to be also effective, and have the advantage of not relying critically on the accurate extraction of fiducia data. In this paper, we propose a new % NEW DAV non-fiducial ECG biometric identification method based on data compression techniques, namely the Ziv-Merhav cross parsing algorithm for symbol sequences (strings). Our method relies on a string similarity measure derived from algorithmic cross complexity concept and its compression-based approximation. NEW DAV We present results on real data, one-lead ECG, acquired during a concentration task, from 19 healthy individuals. Our approach achieves 100% subject recognition rate despite the existence of differentiated stress states.
Simulated tearing: an algorithm for discontinuity-preserving visual surface reconstruction

An algorithm is introduced for discontinuity-preserving visual surface reconstruction, inspired by the formulation of the problem as the fitting of a weak membrane to the observed data. The method slowly applies the data 'force' to a weak membrane, which is allowed to tear when the tension exceeds a certain threshold. The algorithm is named simulated tearing (ST). Formally, ST is a deterministic continuation method, i.e., the problem to be solved is embedded in a family of problems, of which the first member has a simple solution. The proposed method is tested and compared with mean field annealing (MFA), using real and synthetic images. It is concluded that ST is simpler, faster, and slightly outperforms MFA. ST allows implementation based on integer arithmetic.<<ETX>>
On the Use of Suffix Arrays for Memory-Efficient Lempel-Ziv Data Compression

Much research has been devoted to optimizing algorithms of the Lempel-Ziv (LZ) 77 family, both in terms of speed and memory requirements. Binary search trees and suffix trees (ST) are data structures that have been often used for this purpose, as they allow fast searches at the expense of memory usage.In recent years, there has been interest on suffix arrays (SA), due to their simplicity and low memory requirements. One key issue is that an SA can solve the sub-string problem almost as efficiently as an ST, using less memory.  This paper proposes two new SA-based algorithms for LZ encoding, which require no modifications on the decoder side. Experimental results on standard benchmarks show that our algorithms, though not faster,  use 3 to 5 times less memory than the ST counterparts. Another important feature of our SA-based algorithms is that the amount of memory is independent of the text to search, thus the memory that has to be allocated can be defined a priori. These features of low and predictable memory requirements are of the utmost importance in several scenarios, such as embedded systems, where memory is at a premium and speed is not critical. Finally, we point out that the new algorithms are general, in the sense that they are adequate for applications other than LZ compression, such as text retrieval and forward/backward sub-string search.
Tsallis kernels on measures

Recent approaches to classification of text, images, and other types of structured data, launched the quest for positive definite (p.d.) kernels on probability measures. In particular, kernels based on the Jensen-Shannon (JS) divergence and other information-theoretic quantities have been proposed. We introduce new JS-type divergences, by extending its two building blocks: convexity and Shannonpsilas entropy. These divergences are then used to define new information-theoretic kernels on measures. In particular, we introduce a new concept of q-convexity, for which a Jensen q-inequality is proved. Based on this inequality, we introduce the Jensen-Tsallis q-difference, a nonextensive generalization of the Jensen-Shannon divergence. Furthermore, we provide denormalization formulae for entropies and divergences, which we use to define a family of nonextensive information-theoretic kernels on measures. This family, grounded in nonextensive entropies, extends Jensen-Shannon divergence kernels, and allows assigning weights to its arguments.
Navigating in Manhattan: 3D orientation from video without correspondences

The problem of inferring 3D orientation of a camera from video sequences has been mostly addressed by first computing correspondences of image features. This intermediate step is now seen as the main bottleneck of those approaches. In this paper, we propose a new 3D orientation estimation method for urban (indoor and outdoor) environments, which avoids correspondences between frames. The basic scene property exploited by our method is that many edges are oriented along three orthogonal directions; this is the recently introduced Manhattan world (MW) assumption. In addition to the novel adoption of the MW assumption for video analysis, we introduce the small rotation (SR) assumption, that expresses the fact that the video camera undergoes a smooth 3D motion. Using these two assumptions, we build a probabilistic estimation approach. We demonstrate the performance of our method using real video sequences.
Discriminative model selection using a modified Bayesian criterion: Application to trajectory modeling

In this paper we introduce a novel method to determine the model order of a stochastic model for moving objects. The main assumption is that we make use of the knowledge that the obtained model is going to be used for some task, specifically, for trajectory classification. Particularly, the object motion is described by trajectories performed by the objects (e.g., pedestrians), during their motion, by representing them by a small and meaningful mixtures of vector fields. We present a discriminative method for model selection without resort to computationally expensive cross-validation procedures. The idea is, thus, to select the generative model achieving the best classification performance. Although the topic of application is video surveillance, the proposed method can easily be extended to other practical situations. Experiments with both synthetic and real data concerning pedestrian activities illustrate the performance of the proposed approach.
Image super-segmentation: Segmentation with multiple labels from shuffled observations

This paper addresses an image labeling problem, in which it is assumed that there are multiple sensors available at each pixel with some of them possibly inactive. In addition to not being known which sensors are active or inactive, the sensor measurements are also obtained in random unknown order. Given these incomplete observations, we wish to identify which sensors are active at each site and which observations were produced by each sensor. This labeling problem extends classic image segmentation, since it allows multiple labels (i.e., region overlapping). The paper provides methods to solve this problem in two scenarios: known and unknown sensor models. A new minimization algorithm, inspired by hierarchical clustering, is introduced to minimize the energy function resulting from the proposed inference criterion.
Synthesis versus analysis in patch-based image priors

In global models/priors (for example, using wavelet frames), there is a well known analysis vs synthesis dichotomy in the way signal/image priors are formulated. In patch-based image models/priors, this dichotomy is also present in the choice of how each patch is modeled. This paper shows that there is another analysis vs synthesis dichotomy, in terms of how the whole image is related to the patches, and that all existing patch-based formulations that provide a global image prior belong to the analysis category. We then propose a synthesis formulation, where the image is explicitly modeled as being synthesized by additively combining a collection of independent patches. We formally establish that these analysis and synthesis formulations are not equivalent in general and that both formulations are compatible with analysis and synthesis formulations at the patch level. Finally, we present an instance of the alternating direction method of multipliers (ADMM) that can be used to perform image denoising under the proposed synthesis formulation, showing its computational feasibility. Rather than showing the superiority of the synthesis or analysis formulations, the contributions of this paper is to establish the existence of both alternatives, thus closing the corresponding gap in the field of patch-based image processing.
Bayesian wavelet-based signal estimation using non-informative priors

The sparseness and decorrelation properties of the discrete wavelet transform have been exploited to develop powerful signal denoising methods. Most existing schemes involve arbitrary thresholding nonlinearities and ad hoc threshold levels, or computationally expensive signal-adaptive procedures. Furthermore, because the DWT is not a translation-invariant (TI) transform, results of processing depend on the relative alignment between data and wavelets in a complicated manner. In the context of denoising, this non-stationarity can produce undesirable ("pseudo-Gibbs" or "blocking") artifacts. To overcome these deficiencies, we propose a new wavelet-based signal denoising technique derived using the theory of non-informative Bayesian priors. The resulting estimator is TI and employs a very simple fixed non-linear shrinkage/thresholding rule. Remarkably, our new approach is very computationally efficient and performs better than standard methods that are more computationally demanding.
Color identification in dermoscopy images using Gaussian mixture models

Development of Computer Aided Diagnosis systems that mimic the performance of dermatologists when diagnosing dermoscopy images is a challenging task. Despite the relevance of color in the diagnosis of melanomas, few of the proposed systems exploit this characteristic directly. In this paper we propose a new methodology for color identification in dermoscopy images. Our approach is to learn a statistical model for each color using Gaussian mixtures. The results show that the proposed method performs well, with an average Spearman correlation of 0.7981, with respect to a human expert.
Frame-based image deblurring with unknown boundary conditions using the alternating direction method of multipliers

The alternating direction method of multipliers (ADMM) is an efficient optimization tool that achieves state-of-the-art speed in several imaging inverse problems, by splitting the underlying problem into simpler, efficiently solvable sub-problems. In deconvolution, one of these sub-problems requires a matrix inversion, which has been shown to be efficiently computable (via the FFT), if the observation operator is circulant, i.e., under periodic boundary conditions. We extend ADMM-based image deconvolution to a more realistic scenario: unknown boundaries. The observation is modeled as the composition of a periodic convolution with a spatial mask that excludes the regions where the periodic convolution is invalid. We show that the resulting algorithms inherit the convergence guarantees of ADMM and illustrate its performance on non-periodic de-blurring under frame-based regularization.
Unsupervised learning of motion patterns using generative models

This work introduces a non-supervised algorithm for learning generative models for classification/recognition of human activities (specifically, pedestrian trajectories) with application to video surveillance. The proposed algorithm comprises two main features: (?) a set of low level dynamical models of the trajectories, estimated in unsupervised manner using the expectation-maximization (EM) algorithm and automatic model selection using the minimum message length (MML) criterion; (ii) a switching dynamical model described by an hidden Markov model (HMM) used to characterize the higher level activities. The hierarchical model with these two levels is herein denoted as switched dynamical hidden Markov model (SD-HMM). We illustrate the performance of the proposed technique for human activity recognition in a university campus.
Teaching a new trick to an old dog: Revisiting the quadratic programming formulation of sparse recovery using ADMM

One of the early successful approaches to deal with the now classical ℓ2 + ℓ1 optimization formulation of sparse signal recovery (often known as the LASSO) was based on re-writing it as a bound-constrained quadratic program (BCQP), which was then tackled using a gradient projection (GP) algorithm with a spectral (Barzilai-Borwein) step choice. The resulting algorithm (called gradient projection for sparse reconstruction - GPSR) exhibited state-of-the-art speed when it was introduced, but now, 6 years later, much faster alternatives exist. In this paper, we revisit the BCQP formulation and show how it can be efficiently dealt with using the alternating direction method of multipliers (ADMM). We give preliminary experimental evidence that this approach is competitive with the current state-of-the-art, in a set of benchmark problems.
Wavelet-based image estimation: an empirical Bayes approach using Jeffrey's noninformative prior

The sparseness and decorrelation properties of the discrete wavelet transform have been exploited to develop powerful denoising methods. However, most of these methods have free parameters which have to be adjusted or estimated. In this paper, we propose a wavelet-based denoising technique without any free parameters; it is, in this sense, a "universal" method. Our approach uses empirical Bayes estimation based on a Jeffreys' noninformative prior; it is a step toward objective Bayesian wavelet-based denoising. The result is a remarkably simple fixed nonlinear shrinkage/thresholding rule which performs better than other more computationally demanding methods.