nils-herrmann/clean_scirepeval huggingface dataset:
Image deblurring using a pyramid-based Richardson-Lucy algorithm

In image deblurring, it is important to reconstruct images with small error, high perception quality, and less computational time. In this paper, a blurred image reconstruction algorithm, which is a combination of the Richardson-Lucy (RL) deconvolution approach and a pyramid structure, is proposed. The RL approach has good performance in image reconstruction. However, it requires an iterative process, which costs a lot of computation time, and the reconstructed image may suffer from a ringing effect. In the proposed algorithm, we decompose a blurred image from a coarse scale to a fine scale and progressively utilize the RL approach with different number of iterations for each scale. Since the number of iterations is smaller for the large scale part, the computation time can be reduced and the ringing effect caused from details can be avoided. Simulation results show that our proposed algorithm requires less computation time and has good performance in blurred image reconstruction.
Jacket Haar transform

As the Walsh (Hadamard) transform can be generalized into the Jacket transform, in this paper, we generalize the Haar transform into the Jacket-Haar transform. The entries of the Jacket-Haar transform are 0 and ±2k. Compared with the original Haar transform, the basis of the Jacket-Haar transform is general and more suitable for signal processing. Furthermore, with the proposed generalization algorithm, it is possible to define the N-point Jacket-Haar transform, where N is not a power of 2. From our simulations, the proposed Jacket Haar transform has better performance in ECG signal analysis.
Improved Harris' Algorithm for Corner and Edge Detections

A more accurate algorithm for corner and edge detections that is the improved form of the well-known Harris' algorithm is introduced. First, instead of approximating |L[m+x, n+y]-L[m, n]|2 just in terms of x2, xy, and y2, we will approximate |L[m+x, n+y]-L[m, n]|(L[m+x, n+y]-L[m, n]) by the linear combination of x2, xy, y2, x, y, and 1. With the modifications, we can observe the sign of variation. It can avoid misjudging the pixel at a dot or on a ridge as a corner and is also helpful for increasing the robustness to noise. Moreover, we also use orthogonal polynomial expansion and table looking up and define the comity as the "integration" of the quadratic function to further improve the performance. From simulations, our algorithm can much reduce the probability of regarding a non-corner pixel as a corner. In addition, our algorithm is also effective for edge detection.
A perfect reconstruction filter bank with irrational down-sampling factors

Most of the existing methods designed to implement fractional interpolation and decimation are limited by rational scaling factors such as L/M, where L and M are positive integers. The general procedure is usually done with up-sampling by L first, and then followed by down-sampling by M. This scheme, however, requires two steps to fulfill and is not capable in dealing with irrational scaling factors, which can not be represented by L/M. In this paper, we propose a new method to solve the above two difficulties by a single step. Furthermore, a perfect reconstruction filter bank is derived using this new method and the experimental results are presented.
Advanced contour compression algorithm using weighted curvature, Lagrange curve approximation, and improvement adapitive arithmetic coding

Contour compression is to encode the boundaries of objects and important for object-orient image compression and cartoon-like image compression. In this paper, we propose an advanced algorithm to perform lossy compression for contours. First, we find that among all curves, the 3rd order polynomial is most suitable for contour compression. Second, we suggest that, instead of encoding the coefficients of curves directly, it is more efficient to encode the heights of curves. Moreover, we apply an alternative definition of curvature to find the dominant points. Furthermore, we apply the improved version of adaptive arithmetic coding, including increasing the probabilities of the values with the same sign or with similar amplitudes, to encode the heights of curves. Simulations show that, when the error is similar, the proposed algorithm requires much less number of bits than other existing methods for contour compression.
DFT-Commuting Matrix With Arbitrary or Infinite Order Second Derivative Approximation

Recently, Candan introduced higher order DFT-commuting matrices whose eigenvectors are better approximations to the continuous Hermite-Gaussian functions (HGFs). However, the highest order 2k of the O(h2k) NtimesN DFT-commuting matrices proposed by Candan is restricted by 2k+1 les N. In this paper, we remove this order upper bound restriction by developing two methods to construct arbitrary-order DFT-commuting matrices. Computer experimental results show that the Hermite-Gaussian-like (HGL) eigenvectors of the new proposed DFT-commuting matrices outperform those of Candan. In addition, the HGL eigenvectors of the infinite-order DFT-commuting matrix are shown to be the same as those of the n2 DFT-commuting matrix recently discovered in the literature.
Efficient edge-oriented based image interpolation algorithm for non-integer scaling factor

Though image interpolation has been developed for many years, most of state-of-the-art methods, including machine learning based methods, can only zoom the image with the scaling factor of 2, 3, 2k, or other integer values. Hence, the bicubic interpolation method is still a popular method for the non-integer scaling problem. In this paper, we propose a novel interpolation algorithm for image zooming with non-integer scaling factors based on the gradient direction. The proposed method first estimates the gradient direction for each pixel in the low resolution image. Then, we construct the gradient map for the high resolution image by the spline interpolation method. Finally, the intensity of missing pixels can be computed by the weighted sum of the pixels in the pre-defined window. To preserve the edge information during the interpolation process, the weight is determined by the inner product of the estimated gradient vector and the vector from the missing pixel to the known data point. Simulations show that the proposed method has higher performance than other non-integer time scaling methods and is helpful for superresolution.
New corner detection algorithm by tangent and vertical axes and case table

In this paper, we introduce a new algorithm for corner detection. Instead of calculating the gradients along x and y-axes, which is the common step of many existed algorithms, we use the sum of differences to observe the variations along the adaptive vertical and tangent axes. We classify the variations into 36 types and use the 'case table' to determine whether a pixel is a corner. We do some experiments and show that our algorithm can detect almost all the corners of a complicated natural image (such as Lena image and Fruit image) successfully. In addition to corner detection, it is also possible to use our algorithm to detect the edges, ridges, valleys, isolated dots, saddles, and plain regions of a natural image.
Closed-Form Orthogonal Number Theoretic Transform Eigenvectors and the Fast Fractional NTT

In this paper, we propose a new method to find the closed-form solution of Number Theoretic Transform (NTT) eigenvectors. We construct the complete generalized Legendre sequence over the finite field (CGLSF) and use it to solve the NTT eigenvector problem. We derive the CGLSF-like NTT eigenvectors successfully, including the case where the operation field is defined over the Fermat and Mersenne numbers. The derived NTT eigenvector set is orthogonal and has a closed form. It is suitable for constructing sub-NTT building blocks for NTT implementation. In addition, with different eigenvalue assignment rule, we can construct the fractional number theoretic transform (FNTT), including the fractional Fermat number transform (FFNT), the fractional complex Mersenne number transform (FCMNT), and the fractional new Mersenne number transform (FNMNT). They are the generalizations of the original transforms and all have the complexities of O(Nlog2N).
Generalized atom and dictionary design and compressive sensing for vocal signal expansion

Compressive sensing is to apply the sparsity of a signal in some domain to compress a signal. Its performance highly depends on the dictionary and the atoms one applies in the matching pursuit or the basis pursuit processes. In this paper, we generalize the atoms used in the dictionary by converting the phase into the higher order form, using Hermite-Gaussian functions instead of Gaussian functions, and applying harmonic combination. We call them higher-order atoms. This generalization can maximize the sparsity of a signal and is very helpful for improving the coding efficiency. Moreover, we also propose the time-frequency pursuit, which can be viewed as a mixture of the matching pursuit and the basis pursuit, to determine expansion coefficients. Simulations show that the proposed algorithm can much improve the coding efficiency of vocal signals and the bit rate is much less than that of MP3 and other compressive sensing based methods.
A Comparison of Algorithms for Connected Set Openings and Closings

The implementation of morphological connected set operators for image filtering and pattern recognition is discussed. Two earlier algorithms based on priority queues and hierarchical queues, respectively, are compared to a more recent union-find approach. Unlike the earlier algorithms which process regional extrema in the image sequentially, the union-find method allows simultaneous processing of extrema. In the context of area openings, closings, and pattern spectra, the union-find algorithm outperforms the previous methods on almost all natural and synthetic images tested. Finally, extensions to pattern spectra and the more general class of attribute operators are presented for all three algorithms, and memory usages are compared.
Connected Shape-Size Pattern Spectra for Rotation and Scale-Invariant Classification of Gray-Scale Images

In this paper, we describe a multiscale and multishape morphological method for pattern-based analysis and classification of gray-scale images using connected operators. Compared with existing methods, which use structuring elements, our method has three advantages. First, in our method, the time needed for computing pattern spectra does not depend on the number of scales or shapes used, i.e., the computation time is independent of the dimensions of the pattern spectrum. Second, size and strict shape attributes can be computed, which we use for the construction of joint 2D shape-size pattern spectra. Third, our method is significantly less sensitive to noise and is rotation-invariant. Although rotation invariance can also be approximated by methods using structuring elements at different angles, this tends to be computationally intensive. The classification performance of these methods is discussed using four image sets: Brodatz, COIL-20, COIL-100, and diatoms. The new method obtains better or equal classification performance to the best competitor with a 5 to 9-fold speed gain
Condition monitoring and fault diagnosis of a wind turbine with a synchronous generator using wavelet transforms

Some large wind turbines use a low speed synchronous generator, directly-coupled to the turbine, and a fully rated converter to transform power from the turbine to mains electricity. This paper considers the condition monitoring and diagnosis of mechanical and electrical faults in such a variable speed machine. The application of wavelet transforms is investigated because of the disadvantages of conventional spectral techniques in processing instantaneous information in turbine signals derived from the wind, which is variable and noisy. A new condition monitoring technique is proposed which removes the negative influence of variable wind in machine condition monitoring. The technique has a versatile function to detect mechanical and electrical faults in the wind turbine. Its effectiveness is validated by experiments on a wind turbine condition monitoring test rig using a permanent-magnet synchronous generator, which can be driven by aerodynamic forces from a drive motor controlled by an external model, representing wind and turbine rotor behaviour. Within the technique wavelet transforms are employed for noise cancellation and are extended to diagnose faults by taking advantage of their powerful capabilities in analysing non-stationary signals. The diagnosis of wind turbine rotor imbalance in the will be used as an illustrative example, heralding the possibility of detecting a wind turbine mechanical faults by power signal analysis.
VOIDD: automatic vessel of intervention dynamic detection in PCI procedures

In this article, we present the work towards improving the overall workflow of the Percutaneous Coronary Interventions (PCI) procedures by capacitating the imaging instruments to precisely monitor the steps of the procedure. In the long term, such capabilities can be used to optimize the image acquisition to reduce the amount of dose or contrast media employed during the procedure. We present the automatic VOIDD algorithm to detect the vessel of intervention which is going to be treated during the procedure by combining information from the vessel image with contrast agent injection and images acquired during guidewire tip navigation. Due to the robust guidewire tip segmentation method, this algorithm is also able to automatically detect the sequence corresponding to guidewire navigation. We present an evaluation methodology which characterizes the correctness of the guide wire tip detection and correct identification of the vessel navigated during the procedure. On a dataset of 2213 images from 8 sequences of 4 patients, VOIDD identifies vessel-of-intervention with accuracy in the range of 88% or above and absence of tip with accuracy in range of 98% or above depending on the test case.
Towards the Zero Maintenance Wind Turbine

Renewable sources are set to form an increasingly important component of electricity generation in the UK and worldwide. Wind turbines are the most developed renewable technology and are now the largest renewable source for electricity in the UK. Most new turbines are currently being sited onshore but offshore sites could harness greater resource with higher wind speeds and lower turbulence. However, the offshore environment means that access to the turbines is more difficult and attendance for maintenance will be extremely limited, especially during the winter months. This means to ensure high turbine availability, and therefore a high cash return for operators, high reliability turbines with condition-based maintenance will be essential for offshore wind turbines. The paper will present ongoing work at Durham on three inter-related projects, first studying the reliability of existing onshore turbine, then analysing the failure modes and effects on turbines and finally designing a condition monitoring scheme for offshore turbines based on this knowledge
Automatic Attribute Threshold Selection for Blood Vessel Enhancement

Attribute filters allow enhancement and extraction of features without distorting their borders, and never introduce new image features. These are highly desirable properties in biomedical imaging, where accurate shape analysis is paramount. However, setting the attribute-threshold parameters has to date only been done manually. This paper explores simple, fast and automated methods of computing attribute threshold parameters based on image segmentation, thresholding and data clustering techniques. Though several techniques perform well on blood-vessel filtering, the choice of technique appears to depend on the imaging mode.
Condition monitoring and fault diagnosis of a wind turbine synchronous generator drive train

Some large grid connected wind turbines use a low-speed synchronous generator, directly coupled to the turbine, and a fully rated converter to transform power from the turbine to mains electricity. The condition monitoring and diagnosis of mechanical and electrical faults in such a machine are considered, bearing in mind that it has a slow variable speed and is subject to the stochastic, aerodynamic effects of the wind. The application of wavelet transforms is investigated in the light of the disadvantages of spectral analysis in processing signals subject to such stochastic effects. The technique can be used to monitor generator electrical and drive train mechanical faults. It is validated experimentally on a wind turbine condition monitoring test rig using a three-phase, permanent-magnet, slow-speed, synchronous generator, driven by a motor controlled by a model representing the aerodynamic forces from a wind turbine. The possibility of detecting mechanical and electrical faults in wind turbines by electrical signal and particularly power analysis is heralded.
A Hybrid Shared-Memory Parallel Max-Tree Algorithm for Extreme Dynamic-Range Images

Max-trees, or component trees, are graph structures that represent the connected components of an image in a hierarchical way. Nowadays, many application fields rely on images with high-dynamic range or floating point values. Efficient sequential algorithms exist to build trees and compute attributes for images of any bit depth. However, we show that the current parallel algorithms perform poorly already with integers at bit depths higher than 16 bits per pixel. We propose a parallel method combining the two worlds of flooding and merging max-tree algorithms. First, a <italic>pilot</italic> max-tree of a quantized version of the image is built in parallel using a flooding method. Later, this structure is used in a parallel leaf-to-root approach to compute efficiently the final max-tree and to drive the merging of the sub-trees computed by the threads. We present an analysis of the performance both on simulated and actual 2D images and 3D volumes. Execution times are about <inline-formula><tex-math notation="LaTeX"> $20\times$</tex-math><alternatives><inline-graphic xlink:href="moschini-ieq1-2689765.gif"/></alternatives> </inline-formula> better than the fastest sequential algorithm and speed-up goes up to <inline-formula> <tex-math notation="LaTeX">$30-40$</tex-math><alternatives><inline-graphic xlink:href="moschini-ieq2-2689765.gif"/> </alternatives></inline-formula> on 64 threads.
Cost-Effective Condition Monitoring for Wind Turbines

Cost-effective wind turbine (WT) condition monitoring assumes more importance as turbine sizes increase and they are placed in more remote locations, for example, offshore. Conventional condition monitoring techniques, such as vibration, lubrication oil, and generator current signal analysis, require the deployment of a variety of sensors and computationally intensive analysis techniques. This paper describes a WT condition monitoring technique that uses the generator output power and rotational speed to derive a fault detection signal. The detection algorithm uses a continuous-wavelet-transform-based adaptive filter to track the energy in the prescribed time-varying fault-related frequency bands in the power signal. The central frequency of the filter is controlled by the generator speed, and the filter bandwidth is adapted to the speed fluctuation. Using this technique, fault features can be extracted, with low calculation times, from direct- or indirect-drive fixed- or variable-speed WTs. The proposed technique has been validated experimentally on a WT drive train test rig. A synchronous or induction generator was successively installed on the test rig, and both mechanical and electrical fault like perturbations were successfully detected when applied to the test rig.
Connected filtering by reconstruction: Basis and new advances

Openings-by-reconstruction are the oldest connected filters, and indeed, reconstruction methodology lies at the heart of many connected operators such as levelings. Starting out from the basic reconstruction principle of iterated geodesic dilations, extensions such as the use of reconstruction criteria, which constrain the reconstruction process, are discussed. The latter prevent linking distinct objects connected by narrow bridges during the reconstruction process, whilst maintaining as much edge preservation as possible. A far faster variant of filtering with reconstruction criteria is presented, which can be implemented in an O(N) algorithm in stead of O(N2).
Research on a simple, cheap but globally effective condition monitoring technique for wind turbines

Vibration measurement and lubrication oil analysis are used in wind turbines (WT) as condition monitoring systems (CMS). However, they do not provide a complete solution to the WT CMS problem. The former measurement is sophisticated with high hardware costs, suffering from spurious alarms; the latter monitors the wear and fatigue of gears and bearings, but cannot detect electrical abnormalities occurring in the WT generator and electrical system. So, a simpler, cheaper but moreover globally comprehensive WT CMS is still needed, especially if the WTs are to go offshore, where they are confronted with higher risks and difficulties of access. To meet this requirement, a new WT condition monitoring technique has been researched in this paper. As the WT operates over a widely varying power range, dependant on the stochastic variations of the wind, the monitoring signals are usually non-stationary. In view of this, a wavelet-based adaptive filter is designed to extract the power energy at prescribed, fault-related frequencies which vary with time. The energy information obtained is then used as an indicator of WT condition. The central frequency of the filter is adaptive to the average rotational speed of the generator, and the filter bandwidth depends upon the fluctuation of wind speed. By using this filter, fault features can be extracted whether the WT runs at fixed or variable speed. The proposed technique has been experimentally validated on a WT Test Rig using both synchronous and induction generators as exemplars. Experiments prove that the proposed technique is efficient in assessing the WT condition for both mechanical and electrical abnormalities.
Shape representation and recognition through morphological curvature scale spaces

A multiscale, morphological method for the purpose of shape-based object recognition is presented. A connected operator similar to the morphological hat-transform is defined, and two scale-space representations are built, using the curvature function as the underlying one-dimensional signal. Each peak and valley of the curvature is extracted and described by its maximum and average heights and by its extent and represents an entry in the top or bottom hat-transform scale spaces. We demonstrate object recognition based on hat-transform scale spaces for three large data sets, a set of diatom contours, the set of silhouettes from the MPEG-7 database and the set of two-dimensional views of three-dimensional objects from the COIL-20 database. Our approach outperforms other methods for which comparative results exist.
Morphological hat-transform scale spaces and their use in texture classification

In this paper we present a multiscale morphological method for use in texture classification. A connected operator similar to the morphological hat-transform is defined, and two scale-space representations are built. The most important features are extracted from the scale spaces by unsupervised cluster analysis, and the resulting pattern vectors provide the input of a decision tree classifier. We obtain 93.5 % correct classification for the Brodatz texture database.