nils-herrmann/clean_scirepeval huggingface dataset:
Frozen-State Hierarchical Annealing

There is significant interest in the synthesis of discrete-state random fields, particularly those possessing structure over a wide range of scales. However, given a model on some finest, pixellated scale, it is computationally very difficult to synthesize both large- and small-scale structures, motivating research into hierarchical methods. In this paper, we propose a frozen-state approach to hierarchical modeling, in which simulated annealing is performed on each scale, constrained by the state estimates at the parent scale. This approach leads to significant advantages in both modeling flexibility and computational complexity. In particular, a complex structure can be realized with very simple, local, scale-dependent models, and by constraining the domain to be annealed at finer scales to only the uncertain portions of coarser scales; the approach leads to huge improvements in computational complexity. Results are shown for a synthesis problem in porous media.
Scalable learning for restricted Boltzmann machines

We propose Eigen-RBM, a scalable Restricted Boltzmann Machine (RBM) for visual recognition in which the number of free parameters to learn is independent of the image size. Eigen-RBM exploits the global structure of the image and does not impose any locality or translation-invariance assumption, and regularizes the network weights to be a linear combination of a set of predefined filters. We show that, compared to basic RBM, Eigen-RBM can achieve similar or better performance in both recognition and sample generation with significantly less training time.
Multi-modal image registration using structural features

Multi-modal image registration has been a challenging task in medical images because of the complex intensity relationship between images to be aligned. Registration methods often rely on the statistical intensity relationship between the images which suffers from problems such as statistical insufficiency. The proposed registration method works based on extracting structural features by utilizing the complex phase and gradient-based information. By employing structural relationships between different modalities instead of complex similarity measures, the multi-modal registration problem is converted into a mono-modal one. Therefore, conventional mono-modal similarity measures can be utilized to evaluate the registration results. This new registration paradigm has been tested on magnetic resonance (MR) brain images of different modes. The method has been evaluated based on target registration error (TRE) to determine alignment accuracy. Quantitative results demonstrate that the proposed method is capable of achieving comparable registration accuracy compared to the conventional mutual information.
Phase-based methods for Fourier shape matching

Interest in the problem of shape matching has been recently reignited by the huge proliferation of images on the Internet, particularly huge computer databases containing thousands or millions of images. Although many typical image database algorithms rely on a variety of shading, texture, and colour attributes, there are significant opportunities for the use of shape as a discriminator, particularly for binary or high-contrast images. Fourier shape descriptors have been studied extensively for shape comparison, however the descriptor phases have been mostly neglected - either ignored entirely or treated simplistically. This paper formalizes the use of phase in shape matching, and derives shape discriminators, applicable to both simple and complex shapes.
Adaptive color image segmentation using Markov random fields

A new framework for color image segmentation is introduced generalizing the concepts of point-based and spatially-based methods. This framework is based on Markov random fields using a continuous Gibbs sampler. The Markov random fields approach allows for a rigorous computational framework where local and global spatial constraints can be globally optimized. Using a continuous Gibbs sampler enables the algorithm to adapt continuous-valued regional prototypes in a manner analogous to vector quantization while the discrete Gibbs sampler is used to adjust region boundaries.
Sorted self-similarity for multi-modal image registration.

In medical image analysis, registration of multimodal images has been challenging due to the complex intensity relationship between images. Classical multi-modal registration approaches evaluate the degree of the alignment by measuring the statistical dependency of the intensity values between images to be aligned. Employing statistical similarity measures, such as mutual information, is not promising in those cases with complex and spatially dependent intensity relations. A new similarity measure is proposed based on the assessing the similarity of pixels within an image, based on the idea that similar structures in an image are more probable to undergo similar intensity transformations. The most significant pixel similarity values are considered to transmit the most significant self-similarity information. The proposed method is employed in a framework to register different modalities of real brain scans and the performance of the method is compared to the conventional multi-modal registration approach. Quantitative evaluation of the method demonstrates the better registration accuracy in both rigid and non-rigid deformations.
Sorted Random Projections for robust texture classification

This paper presents a simple and highly effective system for robust texture classification, based on (1) random local features, (2) a simple global Bag-of-Words (BoW) representation, and (3) Support Vector Machines (SVMs) based classification. The key contribution in this work is to apply a sorting strategy to a universal yet information-preserving random projection (RP) technique, then comparing two different texture image representations (histograms and signatures) with various kernels in the SVMs. We have tested our texture classification system on six popular and challenging texture databases for exemplar based texture classification, comparing with 12 recent state-of-the-art methods. Experimental results show that our texture classification system yields the best classification rates of which we are aware of 99.37% for CUReT, 97.16% for Brodatz, 99.30% for UMD and 99.29% for KTH-TIPS. Moreover, combining random features significantly outperforms the state-of-the-art descriptors in material categorization.
Modeling the correlation structure of images in the wavelet domain

In this paper we investigate the correlation structure of the wavelet coefficients corresponding to random fields. The context of this work is the study of Bayesian approaches to wavelet shrinkage for the purposes of image denoising. This paper concentrates on both within-scale and across-scale statistical dependencies for a variety of wavelets and random fields, with examples provided for both 1-D and 2-D signals. The results show the whitening effect of the wavelet transform to be quite clear-even for particular highly correlated spatial processes the within-scale correlation decays exponentially fast, however the correlation between scales is surprisingly substantial, even for separations several scales apart. Our goal, initiated in this paper, is the development of an efficient random field model, describing these statistical correlations, and the demonstration of its effectiveness in the context of Bayesian wavelet shrinkage for signal and image denoising.
Mammogram Image Superresolution Based on Statistical Moment Analysis

A novel super resolution method for enhancing the resolution of mammogram images based on statistical moment analysis (SMA) has been designed and implemented. The proposed SMA method enables high resolution mammogram images to be produced at lower levels of radiation exposure to the patient. The SMA method takes advantage of the statistical characteristics of the underlying breast tissues being imaged to produce high resolution mammogram images with enhanced fine tissue details such that the presence of masses and micro calcifications can be more easily identified. In the SMA method, the super resolution problem is formulated as a constrained optimization problem using an adaptive third-order Markov prior model, and solved efficiently using a conjugate gradient approach. The priors are adapted based on the inter-pixel likelihoods of the first moment about zero (mean), second central moment (variance), and third and fourth standardized moments (skewness and kurtosis) from the low resolution images. Experimental results demonstrate the effectiveness of the SMA method at enhancing fine tissue details when compared to existing resolution enhancement methods.
Apparent Ultra-High $b$-Value Diffusion-Weighted Image Reconstruction via Hidden Conditional Random Fields

A promising, recently explored, alternative to ultra-high b-value diffusion weighted imaging (UHB-DWI) is apparent ultra-high b-value diffusion-weighted image reconstruction (AUHB-DWR), where a computational model is used to assist in the reconstruction of apparent DW images at ultra-high b-values. Firstly, we present a novel approach to AUHB-DWR that aims to improve image quality. We formulate the reconstruction of an apparent DW image as a hidden conditional random field (HCRF) in which tissue model diffusion parameters act as hidden states in this random field. The second contribution of this paper is a new generation of fully connected conditional random fields, called the hidden stochastically fully connected conditional random fields (HSFCRF) that allows for efficient inference with significantly reduced computational complexity via stochastic clique structures. The proposed AUHB-DWR algorithms, HCRF and HSFCRF, are evaluated quantitatively in nine different patient cases using Fisher's criteria, probability of error, and coefficient of variation metrics to validate its effectiveness for the purpose of improving intensity delineation between expert identified suspected cancerous and healthy tissue within the prostate gland. The proposed methods are also examined using a prostate phantom, where the apparent ultra-high b-value DW images reconstructed using the tested AUHB-DWR methods are compared with real captured UHB-DWI. The results illustrate that the proposed AUHB-DWR methods has improved reconstruction quality and improved intensity delineation compared with existing AUHB-DWR approaches.
Saliency detection via statistical non-redundancy

A novel algorithm based on statistical non-redundancy is proposed for saliency detection in natural images. By modeling site neighbourhoods as realizations of other site neighbourhoods under a Gaussian process, the saliency of any arbitrary site can be characterized by the statistical non-redundancy of its site neighbourhood with respect to other site neighbourhoods in a given image. Preliminary results using natural images show that the proposed method provides improved precision vs. recall characteristics over previous methods such as spectral residuals and spectral whitening.
A hybrid algorithm using discrete cosine transform and Gabor filter bank for texture segmentation

Gabor filters have been widely used for texture segmentation and feature extraction, however there are important considerations regarding filter parameters, filter bank coverage in the frequency domain and feature dimensional reduction. In this paper, a texture segmentation algorithm based on a hybrid filter bank is presented. The proposed method uses a Gabor filter bank and discrete cosine transform (GDCT) to extract the optimal features for texture segmentation. To reduce the feature vector dimension a competitive network is trained to estimate the principal components of the extracted features. The feature vectors composing both Gabor and DCT features are quantized by estimated eigenvectors. The proposed method enables the use of multiple filter banks or larger filter banks consisting of a higher number of channels.
Statistical processing of large image sequences

The dynamic estimation of large-scale stochastic image sequences, as frequently encountered in remote sensing, is important in a variety of scientific applications. However, the size of such images makes conventional dynamic estimation methods, for example, the Kalman and related filters, impractical. We present an approach that emulates the Kalman filter, but with considerably reduced computational and storage requirements. Our approach is illustrated in the context of a 512 /spl times/ 512 image sequence of ocean surface temperature. The static estimation step, the primary contribution here, uses a mixture of stationary models to accurately mimic the effect of a nonstationary prior, simplifying both computational complexity and modeling. Our approach provides an efficient, stable, positive-definite model which is consistent with the given correlation structure. Thus, the methods of this paper may find application in modeling and single-frame estimation.
Bayesian inference on integrated continuity fluid flows and their application to dust aerosols

The significant role dust aerosols play in the earth's climate system and microbial nutrition cycles have lead to increased efforts in employing remote sensing to monitor their genesis, transport and deposition. In this contribution we considerably refine our earlier statistical models for aerosol detection and atmospheric transport that rely on latent Gaussian Markov random fields for inference. Based on explicitly satisfying the so-called integrated continuity equation we develop a Bayesian generalized linear model intrinsically expressing the divergence of the field as a multiplicative factor covering physical aspects such as compressibility and column projection. Alongside employing surface emissivity estimates for improved genesis detection, we conduct a simulation study clearly showing a reduction of errors in the estimated flow field. We conclude with a case study that relates this experimental finding back to a dust event over northern Africa.
Underground pipe cracks classification using image analysis and neuro-fuzzy algorithm

Pipeline surface defects such as cracks cause major problems for asset managers, particularly when the pipe is buried under the ground. The manual inspection of surface defects in the underground pipes has a number of drawbacks, including subjectivity, varying standards, and high costs. Automatic inspection systems using image processing and artificial intelligence techniques can overcome many of these disadvantages and offer asset managers an opportunity to significantly improve quality and reduce costs. A recognition and classification method for pipe cracks using image analysis and a neuro-fuzzy algorithm is proposed. In the pre-processing step, the cracks in the pipe are extracted from the homogenous background. Then, based on prior knowledge of cracks, five normalised features are extracted. In the classification step, a neuro-fuzzy algorithm is proposed that employs a trapezoidal fuzzy membership function and modified error backpropagation algorithm.
A Markov random fields model for hybrid edge- and region-based color image segmentation

A framework based on a Markov random field approach for color image segmentation enhanced by edge detection is presented. We use a previously developed methodology to transform the image into an R'G'B' space to remove any highlight components preserving the vector-angle component, representing color hue but not intensity, to remove shading effects. To improve the segmentation process we describe the idea of a line process. This allows for the integration of region segmentation with edge detection in a Markov random field framework. We discuss the advantages of this new model with respect to the previously developed image segmentation model.
A new Mercer sigmoid kernel for clinical data classification

In classification with Support Vector Machines, only Mercer kernels, i.e. valid kernels, such as the Gaussian RBF kernel, are widely accepted and thus suitable for clinical data. Practitioners would also like to use the sigmoid kernel, a non-Mercer kernel, but its range of validity is difficult to determine, and even within range its validity is in dispute. Despite these shortcomings the sigmoid kernel is used by some, and two kernels in the literature attempt to emulate and improve upon it. We propose the first Mercer sigmoid kernel, that is therefore trustworthy for the classification of clinical data. We show the similarity between the Mercer sigmoid kernel and the sigmoid kernel and, in the process, identify a normalization technique that improves the classification accuracy of the latter. The Mercer sigmoid kernel achieves the best mean accuracy on three clinical data sets, detecting melanoma in skin lesions better than the most popular kernels; while with non-clinical data sets it has no significant difference in median accuracy as compared with the Gaussian RBF kernel. It consistently classifies some points correctly that the Gaussian RBF kernel does not and vice versa.
Median Robust Extended Local Binary Pattern for Texture Classification

Local binary patterns (LBP) are considered among the most computationally efficient high-performance texture features. However, the LBP method is very sensitive to image noise and is unable to capture macrostructure information. To best address these disadvantages, in this paper, we introduce a novel descriptor for texture classification, the median robust extended LBP (MRELBP). Different from the traditional LBP and many LBP variants, MRELBP compares regional image medians rather than raw image intensities. A multiscale LBP type descriptor is computed by efficiently comparing image medians over a novel sampling scheme, which can capture both microstructure and macrostructure texture information. A comprehensive evaluation on benchmark data sets reveals MRELBP’s high performance—robust to gray scale variations, rotation changes and noise—but at a low computational cost. MRELBP produces the best classification scores of 99.82%, 99.38%, and 99.77% on three popular Outex test suites. More importantly, MRELBP is shown to be highly robust to image noise, including Gaussian noise, Gaussian blur, salt-and-pepper noise, and random pixel corruption.
Intervertebral Disc Segmentation and Volumetric Reconstruction From Peripheral Quantitative Computed Tomography Imaging

An automatic system for segmenting and constructing volumetric representations of excised intervertebral discs from peripheral quantitative computed tomography (PQCT) imagery is presented. The system is designed to allow for automatic quantitative analysis of progressive herniation damage to the intervertebral discs under flexion/extension motions combined with a compressive load. Automatic segmentation and volumetric reconstruction of intervertebral disc from PQCT imagery is a very challenging problem due to factors such as streak artifacts and unclear material density separation between contrasted intervertebral disc and surrounding bone in the PQCT imagery, as well as the formation of multiple contrasted regions under axial scans. To address these factors, a novel multiscale level set approach based on the Mumford-Shaft energy functional in iterative bilateral scale space is employed to segment the intervertebral disc regions from the PQCT imagery. A Delaunay triangulation is then performed based on the set of points associated with the intervertebral disc regions to construct the volumetric representation of the intervertebral disc. Experimental results show that the proposed system achieves segmentation and volumetric reconstructions of intervertebral discs with mean absolute distance error below 0.8 mm when compared to ground truth measurements. The proposed system is currently in operational use as a visualization tool for studying progressive intervertebral disc damage.
Recursive multiscale estimation of space-time random fields

We recently developed a multiscale recursive estimation procedure for the estimation of large-scale dynamic systems. The procedure propagates multiscale models for the estimation errors more efficiently than the Kalman filter's propagation of the error covariances, with a resulting computational complexity of 𝒪(N) and 𝒪(N3/2), where N is the number of variables estimated, for 1-D and 2-D dynamic systems, respectively. To further reduce the computational cost, we introduce in this paper a new class of reduced-order spatially-interpolated multiscale models, and demonstrate their use in remote.
An Elliptical Level Set Method for Automatic TRUS Prostate Image Segmentation

One of the most important tasks in prostate cancer diagnosis and treatment is segmentation of transrectal ultrasound (TRUS) prostate images. Due to the large volumes of TRUS prostate images, automatic segmentation systems are mandatory. Weak prostate boundaries, speckle noise and the short range of gray levels make the task more challenging and difficult. Deformable models have been considered as an effective approach for semi-automatic prostate segmentation. However the main problem toward a fully automatic segmentation system using deformable models is initialization of seed or control points. In this paper an automatic level set prostate segmentation is presented. A classification method is employed to locate the approximate location of the prostate which is used to initiate the proposed elliptical level set contour. The deformations of the level set are guided by a velocity function which is derived using the TRUS prostate image histogram
A perceptually adaptive approach to image denoising using anisotropic non-local means

This paper introduces a novel perceptually adaptive approach to image denoising using anisotropic non-local means. In the classical non-local means image denoising approach, the value of a pixel is determined based on the weighted average of other pixels, where the weights are determined based on a fixed isotropically weighted similarity function between the local neighborhoods. In the proposed algorithm, we demonstrate that noticeably improved perceptual quality can be achieved through the use of adaptive anisotropically weighted similarity functions between local neighborhoods. This is accomplished by adapting the similarity weighing function in an anisotropic manner based on the perceptual characteristics of the underlying image content derived efficiently based on the Mexican Hat wavelet. Experimental results show that the proposed method can be used to provide improved perceptual quality in the denoised image both quantitatively and qualitatively when compared to existing methods.
Background subtraction and dust storm detection

Mineral dust aerosols can influence the Earth's climate system to a significant degree and have a strong effect on terrestrial and oceanic biogeochemical cycles. As one step in quantifying dust sources, sinks, and transport, this paper seeks to quantify the presence of dust storms in the Sahara desert, which is the most active worldwide source of dust. Our work is based on the SEVIRI infrared imager on-board the geostationary Meteosat-8 satellite, providing three separate channels at a 3km by 3km resolution. The significant challenge is that the infrared channels are highly influenced by the presence of water clouds and surface temperatures, which complicate the identification of dust-cloud anomalies. This paper develops a method of spatio-temporal background estimation from sparse data as a way of recovering dust images and presents results on real data.