nils-herrmann/clean_scirepeval huggingface dataset:
A joint inter- and intrascale statistical model for Bayesian wavelet based image denoising

This paper presents a new wavelet-based image denoising method, which extends a "geometrical" Bayesian framework. The new method combines three criteria for distinguishing supposedly useful coefficients from noise: coefficient magnitudes, their evolution across scales and spatial clustering of large coefficients near image edges. These three criteria are combined in a Bayesian framework. The spatial clustering properties are expressed in a prior model. The statistical properties concerning coefficient magnitudes and their evolution across scales are expressed in a joint conditional model. The three main novelties with respect to related approaches are (1) the interscale-ratios of wavelet coefficients are statistically characterized and different local criteria for distinguishing useful coefficients from noise are evaluated, (2) a joint conditional model is introduced, and (3) a novel anisotropic Markov random field prior model is proposed. The results demonstrate an improved denoising performance over related earlier techniques.
Non-negative matrix factorization with mixture of Itakura-Saito divergence for SAR images

Synthetic aperture radar (SAR) data are becoming more and more accessible and have been widely used in many applications. To effectively and efficiently represent multiple SAR images, we propose the mixture of Itakura-Saito (IS) divergence for non-negative matrix factorization (NMF) to perform the dimension reduction. Our proposed method incorporates the unit-mean Gamma mixture model into the NMF to model the multiplicative noise. To obtain the closed-form update equations as much as possible, we approximate the log-likelihood function with its lower bound. Finally, we apply Expectation-Maximization (EM) algorithm to estimate the parameters, resulting in the closed-form multiplicative update rules for the two matrix factors. Experimental results on real SAR dataset demonstrate the effectiveness of the proposed method and its applicability to post applications (e.g., classification) with improved performances over the conventional dimension reduction methods.
Using a Linear Sectioned Bus And a Communication Processor to Reduce Energy Costs in Synchronous On-Chip Communication

The sectioned bus is an energy-optimal architecture for system-on-chip (SoC) communication, where we save energy by consequently switching off unused bus sections on a cycle-by-cycle basis. The communication processor is a paradigm for the control of such a bus by means of software. Synchronous communication takes place within the tiles of a SoC in the deep sub-micron technology domain. We explore design alternatives for a linear software-controlled sectioned bus while building the hardware model of a single-instruction-issue processor, and run, in simulation, a media benchmark on it. We determine the energy cost of controlling this bus, compare it with the energy gain obtained from the sectioning, and find it favorable. The control cost is only 5% of the bus transport energy, leaving us with a gain by segmentation of 81%. We demonstrate the feasibility of the control of a low-power synchronous communication system by the processor. Starting out from this case study at the low-end to medium range of network complexity, we consider the implications of growing complexity that will arise from using multiple sectioned buses on multiple-issue computers (VLIWs). We find that control of linear bus topologies of medium-level complexity is now well understood. Further work is needed at the high-end of non-linear topologies.
Spatially Coherent Fuzzy Clustering for Accurate and Noise-Robust Image Segmentation

In this letter, we present a new FCM-based method for spatially coherent and noise-robust image segmentation. Our contribution is twofold: 1) the spatial information of local image features is integrated into both the similarity measure and the membership function to compensate for the effect of noise; and 2) an anisotropic neighborhood, based on phase congruency features, is introduced to allow more accurate segmentation without image smoothing. The segmentation results, for both synthetic and real images, demonstrate that our method efficiently preserves the homogeneity of the regions and is more robust to noise than related FCM-based methods.
Robust monocular visual odometry by uncertainty voting

GPS by itself is not dependable in urban environments, due to signal reception issues such as multi-path effects or occlusion. Other sensor data is required to keep track of the vehicle in absence of a reliable GPS signal. We propose a new method to use a single on-board consumer-grade camera for vehicle motion estimation. The method is based on the tracking of ground plane features, taking into account the uncertainty on their backprojection as well as the uncertainty on the vehicle motion. A Hough-like parameter space vote is employed to extract motion parameters from the uncertainty models. The method is easy to calibrate and designed to be robust to outliers and bad feature quality. Experimental results show good accuracy and high reliability, with a positional estimate within 2 metres for a 400 metre elapsed distance.
Combining feature fusion and decision fusion for classification of hyperspectral and LiDAR data

This paper proposes a method to combine feature fusion and decision fusion together for multi-sensor data classification. First, morphological features which contain elevation and spatial information, are generated on both LiDAR data and the first few principal components (PCs) of original hyper-spectral (HS) image. We got the fused features by projecting the spectral (original HS image), spatial and elevation features onto a lower subspace through a graph-based feature fusion method. Then, we got four classification maps by using spectral features, spatial features, elevation features and the graph fused features individually as input of SVM classifier. The final classification map was obtained by fusing the four classification maps through the weighted majority voting. Experimental results on fusion of HS and LiDAR data from the 2013  Data Fusion Contest demonstrate effectiveness of the proposed method. Compared to the methods using single data source or only feature fusion, with the proposed method, overall classification accuracies were improved by 10% and 2%, respectively.
Morphological Attribute Profiles With Partial Reconstruction

Extended attribute profiles (EAPs) have been widely used for the classification of high-resolution hyperspectral images. EAPs are obtained by computing a sequence of attribute operators. Attribute filters (AFs) are connected operators, so they can modify an image by only merging its flat zones. These filters are effective when dealing with very high resolution images since they preserve the geometrical characteristics of the regions that are not removed from the image. However, AFs, being connected filters, suffer the problem of “leakage” (i.e., regions related to different structures in the image that happen to be connected by spurious links will be considered as a single object). Objects expected to disappear at a certain threshold remain present when they are connected with other objects in the image. The attributes of small objects will be mixed with their larger connected objects. In this paper, we propose a novel framework for morphological AFs with partial reconstruction and extend it to the classification of high-resolution hyperspectral images. The ultimate goal of the proposed framework is to be able to extract spatial features which better model the attributes of different objects in the remote sensed imagery, which enables better performances on classification. An important characteristic of the presented approach is that it is very robust to the ranges of rescaled principal components, as well as the selection of attribute values. Our experimental results, conducted using a variety of hyperspectral images, indicate that the proposed framework for AFs with partial reconstruction provides state-of-the-art classification results. Compared to the methods using only single EAP and stacking all EAPs computed by existing attribute opening and closing together, the proposed framework benefits significant improvements in overall classification accuracy.
A comparative study on the optimization of global optimum active contours

The active contour framework is widely used for segmentation of images. Specially active contours with a convex energy gained a lot of interest, since they are independent of initialization. Several methods have been proposed to minimise the active contour energy, all claiming to be fast and efficient. In this paper we study and compare the convergence and constraints of these methods. We also propose a new optimization method specially designed for the optimization of convex energy active contours. The proposed method is a combination of a line search method with a Newton-Raphson optimization technique. The proposed method shows good convergence results, while maintaining a low computation cost.
Adaptive base line correction of EEGs using warped polynomials

This paper presents an adaptive method for removing base line wander due to eye movements in electroencephalograms (EEGs). The EEG is adaptively low-pass filtered to extract the base line, which is then subtracted. The adaptive filter is new and is called the time-warped polynomial filter (TWPF). The TWPF has some important advantages over existing adaptive techniques for filtering biomedical signals: it reacts immediately to changes in the signal's properties, independently of the relative amplitudes of the EEG and its baseline; it does not require a reference signal and can be applied to non-periodical signals. An example illustrates the superiority of TWPF for base line correction over traditional procedures.
Estimating the probability of the presence of a signal of interest in multiresolution single- and multiband image denoising

We develop three novel wavelet domain denoising methods for subband-adaptive, spatially-adaptive and multivalued image denoising. The core of our approach is the estimation of the probability that a given coefficient contains a significant noise-free component, which we call "signal of interest". In this respect, we analyze cases where the probability of signal presence is 1) fixed per subband, 2) conditioned on a local spatial context, and 3) conditioned on information from multiple image bands. All the probabilities are estimated assuming a generalized Laplacian prior for noise-free subband data and additive white Gaussian noise. The results demonstrate that the new subband-adaptive shrinkage function outperforms Bayesian thresholding approaches in terms of mean-squared error. The spatially adaptive version of the proposed method yields better results than the existing spatially adaptive ones of similar and higher complexity. The performance on color and on multispectral images is superior with respect to recent multiband wavelet thresholding.
Fast segmented image coding using weakly separable bases

In segmented image coding (SIC) images are segmented and the texture of a segment is expanded as a weighted sum of polynomial base functions, orthogonal on that segment. At very low bit rates, SIC produces images of better subjective quality than standard techniques such as JPEG. The computational requirements of traditional SIC are huge since the number of operations for computing b orthogonal base functions increases faster than quadratically with b. This paper presents a new class of orthogonal bases which can be computed extremely quickly because they are weakly separable (WS). The number of operations needed for generating them grows nearly linearly with b. Typically, WS bases can be generated 8 to 30 times faster than their traditional counterparts. A coding example shows that their use does not affect subjective image quality.<<ETX>>
Noise estimation for video processing based on spatio-temporal gradients

We propose an efficient and accurate wavelet-based noise estimation method for white Gaussian noise in video sequences. The proposed method analyzes the distribution of spatial and temporal gradients in the video sequence in order to estimate the noise variance. The estimate is derived from the most frequent gradient in the two distributions and is compensated for the errors due to the spatio-temporal image sequence content, by a novel correction function. The spatial and temporal gradients are determined from the finest scale of the spatial and temporal wavelet transform, respectively. The main application of the noise estimation algorithm is in wavelet-based video processing. The results show that the proposed method is more accurate than other state-of-the-art noise estimation techniques and less sensitive to varying spatio-temporal content and noise level.
A modified dense displacement field estimation algorithm combined with block-matching method

We presented a displacement field estimation algorithm based on a relaxed smoothness constraint; this algorithm can preserve discontinuities in the displacement field to some extent. Because image data is irregular and the images are noisy, the method produces some large residual errors in the residual maps. We propose an improved displacement field estimation algorithm which uses the displacement information obtained using block-matching to modify the matching result. Experimental results show, this leads to smaller residual error maps, without introducing block artefacts, as would happen in the case of simple block-matching when there is much noise in the background. Also the displacement filed using this method is more consistent than using a method without additional block-matching.
Combinedwavelet Domain and Motion Compensated Filtering Compliant with Video Codecs

In this paper, we introduce the idea of using motion estimation resources from a video codec for video denoising. This is not straightforward because the motion estimators aimed for video compression and coding, tolerate errors in the estimated motion field and hence are not directly applicable to video denoising. To solve this problem, we propose a novel motion field filtering step that refines the accuracy of the motion estimates to a degree that is required for denoising. We illustrate the use of the proposed motion estimation method within a wavelet-based video denoising scheme. The resulting video denoising method is of low-complexity and receives comparable results with respect to the latest video denoising methods.
Classification of multi-source images using color morphological profiles

In the remote sensing domain data from many different sources are often available. Each of these data sources are characterized by their own sensor- and platform-specific properties, i.e. spectral range, or spatial and spectral resolution. In this paper we consider a low spatial, but high spectral resolution satellite image, together with its high spatial resolution RGB color image, e.g. obtained by UAV. Spatial features are extracted from the color image by combining the three color bands R, G and B, ordering these color vectors, and presenting color mathematical morphological profiles accordingly. This way the spatial information contained in the correlation between the different bands is completely taken into account and thus also totally preserved in the feature extraction. In a classification experiment these color morphological profiles are combined with the spectral features of the hyperspectral image, and we show that the spatial characterization of the color image is improved.
Consistent joint photometric and geometric image registration

In this paper, we derive a novel robust image alignment technique that performs joint geometric and photometric registration in the total least square sense. The main idea is to use the total least square metrics instead of the ordinary least square metrics, which is commonly used in the literature. While the OLS model indicates that the target image may contain noise and the reference image should be noise-free, this puts a severe limitation on practical registration problems. By introducing the TLS model, which allows perturbations in both images, we can obtain mutually consistent parameters. Experimental results show that our method is indeed much more consistent and accurate in presence of noise compared to existing registration algorithms.
Non-Local Text Image Reconstruction

In this paper we present a novel method for reconstructing low-resolution text images. Unlike other conventional interpolation methods, the unknown pixel value is not estimated based on its local surrounding neighbourhood, but on the whole text image. In particularly, we exploit the repetitive behaviour of the characters. A great advantage of our proposed approach is that we have more information at our disposal, which leads to a better reconstruction of the interpolated image. Results show the effectiveness of our proposed method and its superiority at very large magnifications to traditional interpolation methods.
Hex-splines: a novel spline family for hexagonal lattices

This paper proposes a new family of bivariate, nonseparable splines, called hex-splines, especially designed for hexagonal lattices. The starting point of the construction is the indicator function of the Voronoi cell, which is used to define in a natural way the first-order hex-spline. Higher order hex-splines are obtained by successive convolutions. A mathematical analysis of this new bivariate spline family is presented. In particular, we derive a closed form for a hex-spline of arbitrary order. We also discuss important properties, such as their Fourier transform and the fact they form a Riesz basis. We also highlight the approximation order. For conventional rectangular lattices, hex-splines revert to classical separable tensor-product B-splines. Finally, some prototypical applications and experimental results demonstrate the usefulness of hex-splines for handling hexagonally sampled data.
Two-stage fusion of thermal hyperspectral and visible RGB image by PCA and guided filter

Nowadays, advanced technology in remote sensing allows us to get multi-sensor and multi-resolution data from the same region. Fusion of these data sources for classification remains challenging problems. In this paper, we propose a novel algorithm for hyperspectral (HS) image pansharpening with two-stage guided filtering in PCA (principal component analysis) domain. In the first stage, we first downsample the highresolution RGB image to the same spatial resolution of original low-resolution HS image, and use guided filter to transfer the image details (e.g. edge) of the downsampled RGB image to the original HS image in the PCA domain. In the second stage, we perform upsampling on the resulting HS image from the first stage by using original high-resolution RGB image and guided filter in PCA domain. This yields a clear improvement over an older approach with one stage guided filtering in PCA domain. Experimental results on fusion of a low spatial-resolution Thermal Infrared HS image and a high spatial-resolution visible RGB image from the 2014  Data Fusion Contest, are very encouraging.
Automated wear label assessment in carpets by using local binary pattern statistics on depth and intensity images

Carpet customers want a product of which the appearance lasts for years. Therefore, carpet manufacturers certify their products with labels that represent the expected change in appearance after the first year of installation. No automated system exists yet for objectively assigning these ranks. In this approach, we present an automated method for assessing carpet wear based on image analysis. For this, depth and intensity information are captured from eight types of carpet samples. The results show that the method correctly assigns wear labels from 1 to 5 in steps of 1 for six of the eight carpet types.
Dempster-Shafer based multi-view occupancy maps

Presented is a method for calculating occupancy maps with a set of calibrated and synchronised cameras. In particular, Dempster-Shafer based fusion of the ground occupancies computed from each view is proposed. The method yields very accurate occupancy detection results and in terms of concentration of the occupancy evidence around ground truth person positions it outperforms the state-of-the-art probabilistic occupancy map method and fusion by summing.
Image de-noising in the wavelet domain using prior spatial constraints

This paper describes an algorithm for the noise removal in the wavelet domain, which takes into account not only the local noise measure, but also prior spatial constraints. These prior spatial or geometrical constraints express the fact that meaningful wavelet coefficients appear in spatially connected clusters, forming edges of given directions. Existing techniques that exploit this kind of prior knowledge are computationally expensive, while the proposed method exploits the spatial constraints in a different and simple manner. We are mostly interested in de-noising infrared images of natural terrain, for the purpose of landmine detection.
A fast algorithm for the generation of orthogonal base functions on an arbitrarily shaped region

In region-oriented transform coding, an image is divided into several nonrectangular regions. The image intensity in each of these regions is represented as a weighted sum of orthogonal base functions. These base functions depend on the shape of the image region and are obtained by orthogonalizing a nonorthogonal starting base. This can be done by using the Gramm-Schmidt procedure. In that case, if base functions of degree up to N are required, a number of O(N/sup 4/) multiplications and additions must be computed for each pixel in the image region. This paper presents a new orthogonalization algorithm, in which the complexity is reduced from O(N/sup 4/) to O(N/sup 3/). The algorithm applies in the important cases, where the starting base consists of polynomials or sinewaves.<<ETX>>
Estimating blur at the brain gray-white matter boundary for FCD detection in MRI

Focal cortical dysplasia (FCD) is a frequent cause of epilepsy and can be detected using brain magnetic resonance imaging (MRI). One important MRI feature of FCD lesions is the blurring of the gray-white matter boundary (GWB), previously modelled by the gradient strength. However, in the absence of additional FCD descriptors, current gradient-based methods may yield false positives. Moreover, they do not explicitly quantify the level of blur which prevents from using them directly in the process of automated FCD detection. To improve the detection of FCD lesions displaying blur, we develop a novel algorithm called iterating local searches on neighborhood (ILSN). The novelty is that it measures the width of the blurry region rather than the gradient strength. The performance of our method is compared with the gradient magnitude method using precision and recall measures. The experimental results, tested on MRI data of 8 real FCD patients, indicate that our method has higher ability to correctly identify the FCD blurring than the gradient method.