nils-herrmann/clean_scirepeval huggingface dataset:
Concurrent Computation of Attribute Filters on Shared Memory Parallel Machines

Morphological attribute filters have not previously been parallelized, mainly because they are both global and non-separable. We propose a parallel algorithm which achieves efficient parallelism for a large class of attribute filters, including attribute openings, closings, thinnings and thickenings, based on Salembier's Max-Trees and Min-trees. The image or volume is first partitioned in multiple slices. We then compute the Max-trees of each slice using any sequential Max-Tree algorithm. Subsequently, the Max-trees of the slices can be merged to obtain the Max-tree of the image. A C-implementation yielded good speed-ups on both a 16-processor MIPS 14000 parallel machine, and a dual-core Opteron-based machine. It is shown that the speed-up of the parallel algorithm is a direct measure of the gain with respect to the sequential algorithm used. Furthermore, the concurrent algorithm shows a speed gain of up to 72% on a single-core processor, due to reduced cache thrashing.
Condition Monitoring of Generators & Other Subassemblies in Wind Turbine Drive Trains

Offshore wind turbines, incorporating electrical generators and converters, operate in locations where accessibility can lead to long mean times to repair. Condition-based maintenance is therefore essential if cost-effective availability targets are to be reached. As yet the condition monitoring techniques appropriate for offshore wind turbines have not been resolved. Reliability studies have shown that the majority of failure modes in wind turbines are concentrated in drive train subassemblies, including the electrical generator and converter, and are heavily affected by wind conditions. A 30 kW test rig has been constructed, with features similar to a wind turbine drive train, to enable the development of the signal processing techniques necessary for this variable speed, high torque variation application. The test rig includes a low speed shaft, high speed shaft, gearbox and an electrical generator and can be driven by simulated wind conditions. The test rig can also be used to inform the selection of appropriate monitoring instrumentation for offshore wind turbines. A series of condition monitoring approaches have been investigated on this test rig, using measured torque, speed, shaft displacement and gearbox vibration to detect faults. By the use of appropriate signal processing techniques, changes to load conditions, properties of the gearbox and coil faults can be detected.
Wind turbine condition monitoring and fault diagnosis using both mechanical and electrical signatures

Some large wind turbines use a synchronous generator, directly-coupled to the turbine, and a fully rated converter to transform power from the turbine to the mains. This paper considers condition monitoring and diagnosis of mechanical and electrical faults in such a variable speed machine. A new condition monitoring technique is proposed in this paper, which removes the negative influence of variable wind in machine condition monitoring. This technique has a versatile function, able to detect both the mechanical and electrical faults in the wind turbine. Its effectiveness is validated by the experiments on a wind turbine condition monitoring test rig. Furthermore, a potential approach for diagnosing wind turbine drive-train mechanical faults using wind turbine generator electrical signals is introduced. The diagnosis of rotor imbalance in the wind turbine will be used as an illustrative example, heralding the detection of wind turbine electromechanical faults by power analysis. The paper offers a simpler and cheaper condition monitoring and fault diagnosis system for wind turbines.
Short local descriptors from 2D connected pattern spectra

We propose a local region descriptor based on connected pattern spectra, and combined with normalized central moments. The descriptors are calculated for MSER regions of the image, and their performance compared against SIFT. The MSER regions were chosen because they can be efficiently selected by constructing a max-tree, a structure used to calculate both descriptors and region moments. Experiments on the UCID database show an improvement over SIFT in two out of five experimental setups, and comparable performance in two other experiments. The new descriptors are only half the size of SIFT, resulting in 4 times faster query times when performing exact search on descriptor index built from 262 images.
Hyperconnected Attribute Filters Based on k-Flat Zones

In this paper, we present a new method for attribute filtering, combining contrast and structural information. Using hyperconnectivity based on k-flat zones, we improve the ability of attribute filters to retain internal details in detected objects. Simultaneously, we improve the suppression of small, unwanted detail in the background. We extend the theory of attribute filters to hyperconnectivity and provide a fast algorithm to implement the new method. The new version is only marginally slower than the standard Max-Tree algorithm for connected attribute filters, and linear in the number of pixels or voxels. It is two orders of magnitude faster than anisotropic diffusion. The method is implemented in the form of a filtering rule suitable for handling both increasing (size) and nonincreasing (shape) attributes. We test this new framework on nonincreasing shape filters on both 2D images from astronomy, document processing, and microscopy, and 3D CT scans, and show increased robustness to noise while maintaining the advantages of previous methods.
Efficient 2-D Grayscale Morphological Transformations With Arbitrary Flat Structuring Elements

An efficient algorithm is presented for the computation of grayscale morphological operations with arbitrary 2D flat structuring elements (S.E.). The required computing time is independent of the image content and of the number of gray levels used. It always outperforms the only existing comparable method, which was proposed in the work by Van Droogenbroeck and Talbot, by a factor between 3.5 and 35.1, depending on the image type and shape of S.E. So far, filtering using multiple S.E.s is always done by performing the operator for each size and shape of the S.E. separately. With our method, filtering with multiple S.E.s can be performed by a single operator for a slightly reduced computational cost per size or shape, which makes this method more suitable for use in granulometries, dilation-erosion scale spaces, and template matching using the hit-or-miss transform. The discussion focuses on erosions and dilations, from which other transformations can be derived.
Diatom identification: a double challenge called ADIAC

This paper introduces the project ADIAC (Automatic Diatom Identification and Classification), which started in May 1998 and which is financed by the European MAST (Marine Science and Technology) programme. The main goal is to develop algorithms for an automatic identification of diatoms using image information, both valve shape (contour) and ornamentation. The paper presents the goals of the project as well as first results on shape modeling and contour extraction. Public data are available in order to create student projects beyond the ADIAC partnership.
A fast component-tree algorithm for high dynamic-range images and second generation connectivity

Component trees are important data structures for computation of connected attribute filters. Though some of the available algorithms are suitable for high-dynamic range, and in particular floating point data, none are suitable for computation of component trees for so-called second-generation, and mask-based connectivity. The latter allow generalization of the traditional notion of connected components, to allow considering e.g. a star cluster as a single entity. This paper provides an O(N log N) algorithm for component trees, suitable for standard and mask-based connectivity. At 24 bits per pixel, the new algorithm outperforms the existing by a factor of 20 to 77 in cpu-time, on 3 megapixel images, depending on the image content.
Improving background estimation for faint astronomical object detection

Estimation of the background is an essential step in automated extraction of faint, extended objects from large-scale, optical surveys in astronomy. In this paper we present an improvement on the background estimation method of a commonly used tool in this field: Source Extractor (SEx-tractor). We show that the original method suffers from bias caused by presence of extended sources, and present an alternative which greatly reduces this effect, leading to much better preservation of faint extended structures.
Connected rotation-invariant size-shape granulometries

In this paper, we describe a rotation-invariant multi-scale morphological method for texture analysis. Compared with the existing methods, our method has three advantages. First, it can be implemented efficiently. Furthermore, our method can be used for the computation of size and strict shape attributes, which we use for the computation of 2-D size-shape pattern spectra. Finally, our method is rotation-invariant. Although the latter can also be approximated by morphological methods by using structuring elements at different angles, this tends to be computationally intensive.
Robust extraction of urinary stones from CT data using attribute filters

In medical imaging, anatomical and other structures such as urinary stones, are often extracted with the aid of active contour/ surface models. Active surface-based methods have robustness limitations and are computationally expensive. In this paper we present a morphological method based on attribute filters and the newly presented sphericity attribute. The operators involved, extract the targeted objects in their entirety without shape/size distortions and proceed rapidly. Experiments on three real 3D data-sets demonstrate their efficiency and their performance is discussed.
Finding and visualizing relevant subspaces for clustering high-dimensional astronomical data using connected morphological operators

Data sets in astronomy are growing to enormous sizes. Modern astronomical surveys provide not only image data but also catalogues of millions of objects (stars, galaxies), each object with hundreds of associated parameters. Exploration of this very high-dimensional data space poses a huge challenge. Subspace clustering is one among several approaches which have been proposed for this purpose in recent years. However, many clustering algorithms require the user to set a large number of parameters without any guidelines. Some methods also do not provide a concise summary of the datasets, or, if they do, they lack additional important information such as the number of clusters present or the significance of the clusters. In this paper, we propose a method for ranking subspaces for clustering which overcomes many of the above limitations. First we carry out a transformation from parametric space to discrete image space where the data are represented by a grid-based density field. Then we apply so-called connected morphological operators on this density field of astronomical objects that provides visual support for the analysis of the important subspaces. Clusters in subspaces correspond to high-intensity regions in the density image. The importance of a cluster is measured by a new quality criterion based on the dynamics of local maxima of the density. Connected operators are able to extract such regions with an indication of the number of clusters present. The subspaces are visualized during computation of the quality measure, so that the user can interact with the system to improve the results. In the result stage, we use three visualization toolkits linked within a graphical user interface so that the user can perform an in-depth exploration of the ranked subspaces. Evaluation based on synthetic as well as real astronomical datasets demonstrates the power of the new method. We recover various known astronomical relations directly from the data with little or no a priori assumptions. Hence, our method holds good prospects for discovering new relations as well.
Mask-Based Second-Generation Connectivity and Attribute Filters

Connected filters are edge-preserving morphological operators, which rely on a notion of connectivity. This is usually the standard 4 and 8-connectivity, which is often too rigid since it cannot model generalized groupings such as object clusters or partitions. In the set-theoretical framework of connectivity, these groupings are modeled by the more general second-generation connectivity. In this paper, we present both an extension of this theory, and provide an efficient algorithm based on the max-tree to compute attribute filters based on these connectivities. We first look into the drawbacks of the existing framework that separates clustering and partitioning and is directly dependent on the properties of a preselected operator. We then propose a new type of second-generation connectivity termed mask-based connectivity which eliminates all previous dependencies and extends the ways the image domain can be connected. A previously developed dual-input max-tree algorithm for area openings is adapted for the wider class of attribute filters on images characterized by second-generation connectivity. CPU-times for the new algorithm are comparable to the original algorithm, typically deviating less than 10 percent either way
Color processing using max-trees: A comparison on image compression

This paper proposes a new method of processing color images using mathematical morphology techniques. It adapts the Max-tree image representation to accommodate color and other vectorial images. The proposed method introduces three new ways of transforming the color image into a gray scale image that is filtered using conventional methods. Three new color reconstruction mechanisms are also proposed. The best method improves color fidelity by as much as 15%. The performance of six attribute filters are also compared on a jpeg compression operation.
Volumetric Attribute Filtering and Interactive Visualization Using the Max-Tree Representation

The Max-Tree designed for morphological attribute filtering in image processing, is a data structure in which the nodes represent connected components for all threshold levels in a data set. Attribute filters compute some attribute describing the shape or size of each connected component and then decide which components to keep or to discard. In this paper, we augment the basic Max-Tree data structure such that interactive volumetric filtering and visualization becomes possible. We introduce extensions that allow (1) direct, splatting-based, volume rendering; (2) representation of the Max-Tree on graphics hardware; and (3) fast active cell selection for isosurface generation. In all three cases, we can use the Max-Tree representation for visualization directly, without needing to reconstruct the volumetric data explicitly. We show that both filtering and visualization can be performed at interactive frame rates, ranging between 2.4 and 32 frames per seconds. In contrast, a standard texture-based volume visualization method manages only between 0.5 and 1.8 frames per second. For isovalue browsing, the experimental results show that the performance is comparable to the performance of an interval tree, where our method has the advantage that both filter threshold browsing and isolevel browsing are fast. It is shown that the methods using graphics hardware can be extended to other connected filters.
CPM: a deformable model for shape recovery and segmentation based on charged particles

A novel, physically motivated deformable model for shape recovery and segmentation is presented. The model, referred to as the charged-particle model (CPM), is inspired by classical electrodynamics and is based on a simulation of charged particles moving in an electrostatic field. The charges are attracted towards the contours of the objects of interest by an electrostatic field, whose sources are computed based on the gradient-magnitude image. The electric field plays the same role as the potential forces in the snake model, while internal interactions are modeled by repulsive Coulomb forces. We demonstrate the flexibility and potential of the model in a wide variety of settings: shape recovery using manual initialization, automatic segmentation, and skeleton computation. We perform a comparative analysis of the proposed model with the active contour model and show that specific problems of the latter are surmounted by our model. The model is easily extendable to 3D and copes well with noisy images.
Efficient 2-D Gray-Scale Dilations and Erosions with Arbitrary Flat Structuring Elements

An efficient algorithm is presented for the computation of gray-scale morphological operations with 2-D structuring elements (S.E.). The required computing time is independent of the image content and of the number of gray levels used. For circular S.E.s, it always outperforms the only existing comparable method, which was proposed by Van Droogenbroeck and Talbot (1996), by a factor between 1.8 and 8.6, depending on the image type. So far, filtering using multiple S.E.s is always done by performing the operator for each size and shape of the S.E. separately. With our method filtering with multiple S.E.s can be performed by a single operator for a reduced computational cost per size or shape, which makes this method more suitable for use in granulometries, dilation-erosion scale spaces, and template matching using the hit-or-miss transform.
Model intestinal microflora in computer simulation: a simulation and modeling package for host-microflora interactions

The ecology of the human intestinal microflora and its interaction with the host are poorly understood. Though more and more data are being acquired, in part using modern molecular methods, development of a quantitative theory has not kept pace with this increase in observing power. This is in part due to the complexity of the system and to the lack of simulation environments in which to test what the ecological effect of a hypothetical mechanism of interaction would be, before resorting to laboratory experiments. The MIMICS project attempts to address this through the development of a cellular automaton for simulation of the intestinal microflora. In this paper, the design and evaluation of this simulator is discussed.
Comparison of methods for wind turbine condition monitoring with SCADA data

Wind turbine operational costs can be reduced by monitoring the condition of major components in the drivetrain. SCADA-based condition monitoring is attractive because the data are already collected, resulting in rapid deployment and modest set-up cost. Three SCADA-based monitoring methods were reviewed: signal trending; self-organising maps and physical model. The physical model was identified as being the most reliable at predicting impending component failures. A validation study on this method using five operational wind farms showed that it is possible to achieve a high detection rate and good detection accuracy. An advance detection period of between 1 month and 2 years was achieved by the method. The study has also highlighted limitations and areas for further development.
3D ultrasound reconstruction of spinal images using an improved Olympic Hole-filling method

We propose a new Hole-filling algorithm by improving the Olympic operator, and we also apply it to generate the volume in our freehand 3D ultrasound reconstruction of the spine. First, the ultrasound frames and position information are compounded into a 3D volume using the Bin-filling method. Then, the Hole-filling method is used to repair gaps in the volume. The conventional Olympic operator defines the empty voxels by sorting the neighboring voxels, removing the n% of the upper and lower values, and averaging them to attain the value to fill the empty voxels. The empty voxel estimation can be improved by thresholding the range width of its neighboring voxels and adjusting it to the average values. The method is tested on a hole-manipulated volume derived from a cropped 3D ultrasound volume of a part of the spine. Our MAE calculation on the proposed technique shows improved result compared to all tested existing methods.
Blood vessel segmentation using moving-window robust automatic threshold selection

Two moving-window methods, using either flat or Gaussian weighted windows, for local thresholding with robust automatic threshold selection are developed. The results show that fast segmentation of blood vessels against a varying background and noise is possible at modest computational cost. Volumes of 128 x 256/sup 2/ and 256/sup 3/ can be segmented in 3.1 s and 6.6 s, for flat, and 12.6 s and 30.8 s for Gaussian windows, respectively, on a 1.9 GHz Pentium 4.
Watermarking for position-mapping-based halftoning

Processing efficiency can be a key factor which dominates the value of a commercial product and its value-added application such as security. In this study, a computationally reduced halftoning-based watermarking is proposed. In encoder, the Efficient Direct Binary Search (EDBS) is employed to generate reference table to ensure the output is in halftone format. Subsequently, a number of optimized compressive tables with various texture angles are established for subsequent table lookup. In decoder, the Least-Mean-Square (LMS) enlarges the differences among those phenotypes of the embedded angles and the required number of dimensions for each angle. Finally, the naïve Bayes classifier is employed to collect the possibility information for classifying various angles. As documented in the experimental results, good image quality and correct detect rate can be obtained simultaneously. Moreover, a high processing efficiency of 0.6 milliseconds for an image of size 512×512 can also be achieved, which can substantially increase the commercial competitive strength in printing market, in particular the security issue is well addressed.
Impact of the Lips for Biometrics

In this paper, the impact of the lips for identity recognition is investigated. In fact, it is a challenging issue for identity recognition solely by the lips. In the first stage of the proposed system, a fast box filtering is proposed to generate a noise-free source with high processing efficiency. Afterward, five various mouth corners are detected through the proposed system, in which it is also able to resist shadow, beard, and rotation problems. For the feature extraction, two geometric ratios and ten parabolic-related parameters are adopted for further recognition through the support vector machine. Experimental results demonstrate that, when the number of subjects is fewer or equal to 29, the correct accept rate (CAR) is greater than 98%, and the false accept rate (FAR) is smaller than 0.066% . Moreover, the processing speed of the overall system achieves 34.43 frames per second, which meets the real-time requirement. Thus, the proposed system can be an effective candidate for facial biometrics applications when other facial organs are covered or when it is applied for an access control system.
Continuous-tone Watermark Hiding in Halftone Images

In this paper, a low computational complexity method is proposed to embed a multi-tone visual pattern using the concept of secret sharing. One halftone image is obtained using regular error diffusion, and the other image is obtained using the proposed Generalized Noise-Balanced Error Diffusion (GNBEDF) in considering the properties of the watermark and the first halftone image in the same time. With the proposed method, the bi-level watermark embedding becomes a special case to this technology. The bi-level watermark decoding can be simply achieved via printing the two halftone images onto two transparencies and then superimposing them together to reveal the watermark pattern. However, as the multi-tone watermark is involved, the proposed Gaussian Difference (GD) approach is needed to perform the decoding to produce a decoded multi-tone watermark. Throughout this work, a lowpass filter obtained by Least-Mean-Square is employed to assess the image quality.