Local Coefficient Of Variation Research Articles

Screening Image Features of Collapsed Buildings for Operational and Rapid Remote Sensing Identification

The accurate detection of collapsed buildings is of great significance for post-disaster rescue and reconstruction. High-resolution optical images are important data sources for identifying collapsed buildings, and the identification accuracy mainly depends on the features extracted from the images. However, existing research lacks a comprehensive screening and general evaluation of the ability of remote sensing features to detect collapsed buildings, and there is still a considerable gap in the operational process of rapid identification of collapsed buildings in remote sensing. Based on 2630 pairs of building samples distributed in 6 regions worldwide, this study evaluated the ability of 25 remote sensing features (including spectral and spatial features) to detect collapsed buildings and select the most capable ones. Then, we test the application effect of selected features in identifying collapsed buildings on large-scale remote sensing images. Based on the two experiments above, an operational process for rapid identification of collapsed buildings was suggested. The result shows that Homogeneity, Energy, Local Entropy, Local Standard Deviation, and Gradient can effectively and stably distinguish collapsed buildings from non-collapsed buildings (Jeffries-Matusita distances are greater than 1.59 and Transformed Divergences are greater than 1.60) and have high recognition accuracy for collapsed buildings on large-scale remote sensing images (F1-scores are 0.71–0.94). In addition, Contrast, Local Coefficient of Variation, Edge Density, and Global Entropy can also distinguish collapsed buildings from non-collapsed buildings at a normal level (Jeffries-Matusita distances are 1.14–1.28, and Transformed Divergences are 1.24–1.48), while Gradient Orientation Entropy, Fractal Dimension, Local Binary Patterns, Edge, Local Mean, Correlation, Gradient Orientation Standard Deviation, Global Coefficient of Variation, Gabor feature, Local Moran’I, and six spectral features have relatively weak abilities (Jeffries-Matusita distances are less than 0.73, and Transformed Divergences are less than 1.07). The selected remote sensing features can support rapid identification of potential collapsed building areas from post-disaster remote sensing images.

Progressive Keypoint Detection with Dense Siamese Network for SAR Image Registration

SAR image registration plays a key role in the applications such as change detection, image mosaic, which is one of the most challenging tasks in recent years due to speckle noise, geometric distortion and nonlinear radiation differences between SAR images. In the feature-based SAR image registration methods, the repeatability of keypoints and the effectiveness of feature descriptors could directly affect the registration accuracy. In this paper, we propose a stable feature intersection-based (FI) keypoint detector, which contains three progressive detectors, i.e., phase coherency (PC) detector, horizontal/vertical and major/minor diagonal oriented gradient detectors, and local coefficient of variation (LCoV) detector. Our proposed keypoint detector can not only effectively extract keypoints with high repeatability, but also greatly reduce the number of false keypoints, thus reducing the computational cost of feature description and matching. Then we propose the Cross Stage Partial Siamese Network (CSPSNet) to rapidly extract feature descriptors containing both deep and shallow features, which can be used to obtain more correct matching point pairs than traditional man-made shallow descriptors. In addition, we design a Hard Example Mining Loss (HEMLoss) to minimize the matching distance between the matching descriptor and adaptive selected non-matching descriptors. Experimental results on different pairs of SAR images demonstrate that our proposed method achieves better performance than other state-of-the-art methods.

Infrared Small Target Detection Based on Weighted Local Coefficient of Variation Measure.

Robust infrared (IR) small target detection is critical for infrared search and track (IRST) systems and is a challenging task for complicated backgrounds. Current algorithms have poor performance on complex backgrounds, and there is a high false alarm rate or even missed detection. To address this problem, a weighted local coefficient of variation (WLCV) is proposed for IR small target detection. This method consists of three stages. First, the preprocessing stage can enhance the original IR image and extract potential targets. Second, the detection stage consists of a background suppression module (BSM) and a local coefficient of variation (LCV) module. BSM uses a special three-layer window that combines the anisotropy of the target and differences in the grayscale distribution. LCV exploits the discrete statistical properties of the target grayscale. The weighted advantages of the two modules complement each other and greatly improve the effect of small target enhancement and background suppression. Finally, the weighted saliency map is subjected to adaptive threshold segmentation to extract the true target for detection. The experimental results show that the proposed method is more robust to different target sizes and background types than other methods and has a higher detection accuracy.

Glulam – closed form solution using statistical smearing, truncating, calibrating

ABSTRACT This paper describes a relatively simple closed form formula for predicting the strength of glulam assemblies in bending about the major axis based on statistical engineering mechanics using the tension strength of finger jointed of bonded lamination pairs. It has been calibrated against the strengths of full-size glulam beams ranging in depths from 150 mm to 1200 mm over spans of 2.7 m up to 19.5 m. Comparisons are made at both the mean strength and 5th percentile strength levels with the latter drawing attention to the fact that all test series invariably involve observation of minimum values that means the statistical distributions are invariably truncated. For Weibull distributions, this has an impact on the shape factor and, therefore, the local coefficient of variation of the lower tail, which plays an important role in size effects in glulam and potentially has wider implications.

Quantitative Detection of Minor Defects in Metal Materials Based on Variation Coefficient of CT Image

In order to perform precise quantitative detection of internal minor defects in additive manufacturing of metal materials based on industrial CT, a quantitative detection method for minor defects in metal materials based on gray-scale variation coefficient of minor defects in CT image is presented. Firstly, based on the analysis of gray-scale features of minor defect regions in CT image, a minor defect image response model is constructed by combining the point spread function (PSF) and noise approximate Gaussian distribution, and the correspondence between the minor defects of different sizes and the local variation coefficient of CT image is derived, and a quantitative detection method based on the coefficient of variation of minor defect region in CT image is established for minor defects of AM metal materials. Industrial CT experiments were then conducted by processing a series of minor defect reference test blocks of different sizes with TC4 titanium. Experiment results show that the defect size and the coefficient of variation reflect a good linear relationship of monotonous increase, with correlation more than 0.996, and the quantitative results of minor defects based on the coefficient of variation method accord well with the measurement results of the microscope, with relative error controlled within 4%. Obviously, this method characterized by simple measurement process and high practical value has improved the quantitative precision of minor defects in industrial CT.

Partial Seeding Policy for Controlling the Crystal Quality in Batch Cooling Crystallization

AbstractPartial seeding, in which the nuclei originating from seed crystals grow to yield the product crystals, was simulated and optimized for controlling the batch cooling crystallization process of L‐arginine. The product quality was evaluated by the coefficient of variation (CV) of the crystal size distribution. First, the optimum seed amount for partial seeding was estimated by simulation. Then, the simulated values of the optimum seed amount and the resulting local minimum CV were correlated with the seed crystal size and the cooling rate. The correlation can be utilized for estimating the seed amount in the case that the seed crystals are added in a slurry. Finally, these simulated values were compared to the measured ones. Consequently, the optimum seed amount was suggested to be reasonably predicted.

Infrared and visible image fusion based on oversampled graph filter banks

The infrared image (RI) and visible image (VI) fusion method merges complementary information from the infrared and visible imaging sensors to provide an effective way for understanding the scene. The graph filter bank-based graph wavelet transform possesses the advantages of the classic wavelet filter bank and graph representation of a signal. Therefore, we propose an RI and VI fusion method based on oversampled graph filter banks. Specifically, we consider the source images as signals on the regular graph and decompose them into the multiscale representations with M-channel oversampled graph filter banks. Then, the fusion rule for the low-frequency subband is constructed using the modified local coefficient of variation and the bilateral filter. The fusion maps of detail subbands are formed using the standard deviation-based local properties. Finally, the fusion image is obtained by applying the inverse transform on the fusion subband coefficients. The experimental results on benchmark images show the potential of the proposed method in the image fusion applications.

Large-scale analysis of human gene expression variability associates highly variable drug targets with lower drug effectiveness and safety.

MotivationThe effectiveness of drugs tends to vary between patients. One of the well-known reasons for this phenomenon is genetic polymorphisms in drug target genes among patients. Here, we propose that differences in expression levels of drug target genes across individuals can also contribute to this phenomenon.ResultsTo explore this hypothesis, we analyzed the expression variability of protein-coding genes, and particularly drug target genes, across individuals. For this, we developed a novel variability measure, termed local coefficient of variation (LCV), which ranks the expression variability of each gene relative to genes with similar expression levels. Unlike commonly used methods, LCV neutralizes expression levels biases without imposing any distribution over the variation and is robust to data incompleteness. Application of LCV to RNA-sequencing profiles of 19 human tissues and to target genes of 1076 approved drugs revealed that drug target genes were significantly more variable than protein-coding genes. Analysis of 113 drugs with available effectiveness scores showed that drugs targeting highly variable genes tended to be less effective in the population. Furthermore, comparison of approved drugs to drugs that were withdrawn from the market showed that withdrawn drugs targeted significantly more variable genes than approved drugs. Last, upon analyzing gender differences we found that the variability of drug target genes was similar between men and women. Altogether, our results suggest that expression variability of drug target genes could contribute to the variable responsiveness and effectiveness of drugs, and is worth considering during drug treatment and development.Availability and implementationLCV is available as a python script in GitHub (https://github.com/eyalsim/LCV).Supplementary information Supplementary data are available at Bioinformatics online.

Behavioral Context Determines Network State and Variability Dynamics in Monkey Motor Cortex.

Variability of spiking activity is ubiquitous throughout the brain but little is known about its contextual dependance. Trial-to-trial spike count variability, estimated by the Fano Factor (FF), and within-trial spike time irregularity, quantified by the coefficient of variation (CV), reflect variability on long and short time scales, respectively. We co-analyzed FF and the local coefficient of variation (CV2) in monkey motor cortex comparing two behavioral contexts, movement preparation (wait) and execution (movement). We find that the FF significantly decreases from wait to movement, while the CV2 increases. The more regular firing (expressed by a low CV2) during wait is related to an increased power of local field potential (LFP) beta oscillations and phase locking of spikes to these oscillations. In renewal processes, a widely used model for spiking activity under stationary input conditions, both measures are related as FF ≈ CV2. This expectation was met during movement, but not during wait where FF ≫ CV22. Our interpretation is that during movement preparation, ongoing brain processes result in changing network states and thus in high trial-to-trial variability (expressed by a high FF). During movement execution, the network is recruited for performing the stereotyped motor task, resulting in reliable single neuron output. Our interpretation is in the light of recent computational models that generate non-stationary network conditions.

Despeckling PolSAR images with an adaptive bilateral filter

An improved bilateral filter with adaptive parameters estimation in space domain and polarimetric domain for polarimetric synthetic aperture radar (PolSAR) image despeckling, named PolSAR adaptive bilateral filtering (PABF), is proposed. On one hand, PABF sets the spatial parameter adaptively according to the local coefficient of variation. On the other hand, the polarimetric parameter is adjusted adaptively on the basis of the noise variance estimated from the convolution between the intensity image and Laplacian template. The experiments performed on simulated and real PolSAR data show that PABF effectively suppresses speckles while maintaining important details of images.

Denoising the Underwater Images by using Adaptive Filters

The Sound Navigation And Ranging and synthetic aperture radar images are perturbed by the multiplicative noise called speckle noise . The presence of speckle noise leads to incorrect analysis and has to be handled carefully. Images have a strong variation from one pixel to another which reduces the efficiency of the algorithms for detection and classification. In this paper, the most well-known filters are analyzed by using underwater images. It is shown that they are based on a test related to the local coefficient of variation of the observed image, which describes the scene heterogeneity. Linear noise removing models can remove noise but are not able to preserve edges of the images in an efficient manner . On the other hand, Non-linear models can handle edges in a much better way than linear models. It is found that the linear filters and nonlinear filters can remove noise from small area objects and homogeneous areas but not in heterogeneous areas. Adaptive filters are used to remove noise not only from homogeneous area but also from heterogeneous areas. This paper presents an optimum filter by using locally estimated parameter values to provide minimum mean square error in order to smoothen the sonar images. It is also shown that the optimum filter is computationally efficient. The performance of this adaptive filter is compared (qualitatively) with other filters and found that the complexity of the frost filter is reduced.

Investigating the time clustering of induced microseismicity generated by hydraulic fracturing

By using the global and local coefficient of variation and the Allan Factor we investigated the time-clustering properties of the time dynamics of fluid-injection–induced microseismicity. The experiment consists of a microseismic monitoring through a nearly vertical borehole of 12 receivers of a hydraulic fracturing stimulation along a horizontal well separated into more than 20 sections (stages) The main finding of the applied methodology is the discrimination between fault triggering and new fracturing, being the first characterized by a clusterization of the induced microseismic events and the second by a Poissonian behaviour of the generated events.

Multiparametric statistical investigation of seismicity occurred at El Hierro (Canary Islands) from 2011 to 2014

A detailed statistical investigation of the seismicity occurred at El Hierro volcano (Canary Islands) from 2011 to 2014 has been performed by analysing the time variation of four parameters: the Gutenberg-Richter b-value, the local coefficient of variation, the scaling exponent of the magnitude distribution and the main periodicity of the earthquake sequence calculated by using the Schuster's test. These four parameters are good descriptors of the time and magnitude distributions of the seismic sequence, and their variation indicate dynamical changes in the volcanic system. These variations can be attributed to the causes and types of seismicity, thus allowing to distinguish between different host-rock fracturing processes caused by intrusions of magma at different depths and overpressures. The statistical patterns observed among the studied unrest episodes and between them and the eruptive episode of 2011–2012 indicate that the response of the host rock to the deformation imposed by magma intrusion did not differ significantly from one episode to the other, thus suggesting that no significant local stress changes induced by magma intrusion occurred when comparing between all them. Therefore, despite the studied unrest episodes were caused by intrusions of magma at different depths and locations below El Hierro island, the mechanical response of the lithosphere was similar in all cases. This suggests that the reason why the first unrest culminated in an eruption while the other did not, may be related to the role of the regional/local tectonics acting at that moment, rather than to the forceful of magma intrusion.

Brain tissue segmentation using fuzzy clustering techniques.

Medical image segmentation is an essential step for most consequent image analysis tasks. Medical images can be segmented manually, but the accuracy of image segmentation using the automated segmentation algorithms is more when compared with the manual calculations. In this paper, an automated segmentation and classification of tissues are proposed for MR brain images. To classify MR brain image into three segments such as Grey Matter (GM), White Matter (WM) and Cerebro-Spinal Fluid (CSF). Classification of brain into tissues helps to diagnose several diseases such as tumors, Alzheimer's disease, stroke, multiple sclerosis. An unsupervised clustering technique such as Fuzzy C-Means (FCM) algorithm has been widely used in segmenting the images. The spatial information is not fully utilized by the conventional clustering algorithm and hence it is not applicable for clustering a noisy image. We incorporate a method for image clustering called out as Reformulated Fuzzy Local information C-Means Clustering algorithm [RFLICM] which is a variant of traditional Clustering algorithm by considering both spatial and gray level information. In RFLICM, spatial distance is replaced by local coefficient of variation in a fuzzy manner. Experiments are conducted on brain images to validate the performance of the proposed technique in segmenting the medical images and the efficiency achieved in the presence of salt and pepper noise is 99.86%. Standard FCM, Fuzzy Local information C-means clustering algorithm [FLICM], Reformulated Fuzzy Local information C-means clustering algorithm [RFLICM] are compared to explore the accuracy of our proposed approach. Clustering results show that RFLICM segmentation method is appropriate for classifying tissues in brain MR image.

Effects of guided random sampling of TCCs on blood flow values in CT perfusion studies of lung tumors.

Tissue perfusion is commonly used to evaluate lung tumor lesions through dynamic contrast-enhanced computed tomography (DCE-CT). The aim of this study was to improve the reliability of the blood flow (BF) maps by means of a guided sampling of the tissue time-concentration curves (TCCs). Fourteen selected CT perfusion (CTp) examinations from different patients with lung lesions were considered, according to different degrees of motion compensation. For each examination, two regions of interest (ROIs) referring to the target lesion and the arterial input were manually segmented. To obtain the perfusion parameters, we computed the maximum slope of the Hill equation, describing the pharmacokinetics of the contrast agent, and the TCC was fitted for each voxel. A guided iterative approach based on the Random Sample Consensus method was used to detect and exclude samples arising from motion artifacts through the assessment of the confidence level of each single temporal sample of the TCC compared to the model. Removing these samples permits to refine the model fitting, thus exploiting more reliable data. Goodness-of-fit measures of the fitted TCCs to the original data (eg, root mean square error and correlation distance) were used to assess the reliability of the BF values, so as to preserve the functional structure of the resulting perfusion map. We devised a quantitative index, the local coefficient of variation (lCV), to measure the spatial coherence of perfusion maps, from local to regional and global resolution. The effectiveness of the algorithm was tested under three different degrees of motion yielded by as many alignment procedures. At pixel level, the proposed approach improved the reliability of BF values, quantitatively assessed through the correlation index. At ROI level, a comparative analysis emphasized how our approach "replaced" the noisy pixels, providing smoother parametric maps while preserving the main functional structure. Moreover, the implemented algorithm provides a more meaningful effect in correspondence of a higher motion degree. This was confirmed both quantitatively, using the lCV, and qualitatively, through visual inspection by expert radiologists. Perfusion maps achieved with the proposed approach can now be used as a valid tool supporting radiologists in DCE-CTp studies. This represents a step forward to clinical utilization of these studies for staging, prognosis, and monitoring values of therapeutic regimens.

SAR Image Despeckling Based on Adaptive PDE Filter and Histogram

PDE is suitable for dealing with degraded SAR image with speckle noise because it can enhance local structure and preserve background and shadow. However, PDE does not take into account the non stationary property and inherent imprecision of SAR image. In this paper, we propose an improved algorithm which combines PDE with histogram of image local coefficient of variation to make different strategies on different regions. The proposed technique compares w.r.t. enhancing Gamma Map filter with better results both in evaluation criteria and visual effect.

Two-Phase Image Segmentation with Nonlocal Mean Filter and Kernel Evolutionary Clustering in Local Learning

In this study, a novel two-phase image segmentation algorithm (TPIS) by using nonlocal mean filter and kernel evolutionary clustering in local learning is proposed. Currently, the difficulties for image segmentation lie in its vast pixels with overlapping characteristic and the noise in the different process of imaging. Here, we want to use nonlocal mean filter to remove different types of noise in the image, and then, two kernel clustering indices are designed in evolutionary optimization. Besides, the local learning strategy is designed using local coefficient of variation of each local pixels or image patch is employed to update the quality of the local segments. The new algorithm is used to solve different image segmentation tasks. The experimental results show that TPIS is competent for segmenting majority of the test images with high quality.

On the existence of spontaneous neuronal bursting activity at the periphery of the amphibian auditory pathway

The firing activity of 104 neurons from the medullar dorsal nucleus (homologous to the mammalian cochlear nucleus complex) of immobilized common frogs (Rana t. temporaria) was recorded extracellularly in the absence of external auditory stimuli. This background activity was analyzed by using stochastic point process parameters such as the coefficient of variation, local coefficient of variation, interdependence of neighboring intervals, interpulse interval distribution, autocorrelation, and hazard function. All these parameters were put in correspondence with bursting rate, i.e., relative number of interpulse intervals grouped in clusters, or bursts, determined by the proposed method. Neurons with pronounced refractoriness, as a rule, exhibited lower bursting rates whereas neurons with a distinct maximum of the correlation function showed higher values than expected from a random Poisson process. However, even in the latter group of cells, bursting rates usually did not decrease after random shuffling of all the interpulse intervals. As a result, we must admit the absence of clustered spontaneous activity in second-order auditory neurons in the amphibian species studied. These data are compared with the specificities of spontaneous firing activity in different parts of the mammalian auditory pathway.

SAR Image Despeckling Using a Space-Domain Filter With Alterable Window

In this letter, a modified bilateral filter suitable for synthetic aperture radar (SAR) image despeckling, named space-domain filter with alterable window (SFAW), is proposed. SFAW features geometric adaptivity, in both spatial and similarity functions, respectively driven by the following: 1) the local coefficient of variation ( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CV</i> ) and 2) the joint probability density function model of two pixels having the same reflectivity. Tests on synthetic and real SAR images show that SFAW notably smoothes speckle with unperceivable detail blurring and achieves better performances than other related methods.

Characterization of Land Cover Types in TerraSAR-X Images by Combined Analysis of Speckle Statistics and Intensity Information

The appearance of objects and surfaces in synthetic aperture radar (SAR) images significantly differs from the human perception of the environment. In addition, the quality of SAR data is degraded by speckle noise, superposing the true radiometric and textural information of the radar image. Hence, the interpretation of SAR images is considered to be more challenging compared to the analysis of optical data. However, in this paper, we demonstrate how information on the local development of speckle can be used for the differentiation of basic land cover (LC) types in a single-polarized SAR image. For that purpose, we specify the speckle characteristics of the following LC types: 1) water; 2) open land (farmland, grassland, bare soil); 3) woodland; and 4) urban area by means of an unsupervised analysis of scatter plots and standardized histograms of the local coefficient of variation. Next, we use this information for the implementation of a straightforward preclassification of single-polarized TerraSAR-X stripmap images by combining information on the local speckle behavior and local backscatter intensity. The output is either provided as a discrete classification or as a color composite image whose bands can be interpreted in terms of a fuzzy classification. The results of this paper show that unsupervised speckle analysis in high-resolution SAR images supplies valuable information for a differentiation of the water, open land, woodland, and urban area LC types. While the color composite image supports the visual interpretation of SAR data, the outcome of the fully automated discrete LC classification procedure represents a valuable preclassification image, showing overall accuracies of 77%-86%.