Use Of Histograms Research Articles

Noise Estimation for MRI Images with Revised Theory on Histograms of Second-order Derivatives

Previous research by the author in the use of histograms of second-order derivatives showed that the differences between pixels in MRI images can be determined without referring to the ground truth for the purpose of noise reduction. Yet, the results of the previous research also showed that the methodologies used could not prevent false positives and negatives. To address this problem, a technique has been developed to involve multiple conditions that utilize the statistics of the histograms and the circumstances of neighbours in the vicinity of a pixel. The confusion matrix for this method shows that the technique has marginal but consistent improvement across the noise levels that are tested, compared to prior methods

Learning HDR video reconstruction for dual-exposure sensors with temporally-alternating exposures

High-dynamic range (HDR) video reconstruction using conventional single-exposure sensors can be achieved by temporally alternating exposures. This, in turn, requires computing exposure alignment which is difficult to achieve due to the exposure differences that notoriously creates problems for moving content, in particular, in larger saturated and dis-occluded regions. An attractive alternative are dual-exposure sensors that capture, in a single-shot, differently exposed and spatially interleaved half-frames, so that they are perfectly spatially and temporally (up to varying motion blur) aligned by construction. In this work, we demonstrate that we successfully compensate for reduced spatial resolution and aliasing in such sensors, and we improve overall the quality and dynamic range of reconstructed HDR video with respect to single-exposure sensors for a given number of alternating exposures. Specifically, we consider low, mid, and high exposures, and we propose that the mid exposure is captured for every frame, and serves as a spatial and temporal reference. We capitalize here on neural networks for denoising, deblurring, and upsampling tasks, so that effectively we obtain two clean, sharp, and full-resolution exposures for every frame, which are then complemented by warping a missing third exposure. High-quality warping is achieved by learning optical flow that merges the individual flows found for each specific exposure. Such flow merging is instrumental in handling saturated/dis-occluded image regions, while dense temporal sampling of mid exposure improves motion quality reproduction between more sparsely sampled exposures. We demonstrate that by capturing only a limited amount of sensor-specific data and a novel use of histograms, instead of common parametric noise statistics, we are able to generate synthetic training data that lead to a better denoising and deblurring quality than can be achieved by existing state-of-the-art methods. As there is not enough high-quality HDR video available, we devise a method to learn from LDR video instead. Our approach compares favorably to several strong baselines, and can boost existing HDR image and video methods when they are re-trained on our data.

Application of Structural Entropy and Spatial Filling Factor in Colonoscopy Image Classification.

For finding colorectal polyps the standard method relies on the techniques and devices of colonoscopy and the medical expertise of the gastroenterologist. In case of images acquired through colonoscopes the automatic segmentation of the polyps from their environment (i.e., from the bowel wall) is an essential task within computer aided diagnosis system development. As the number of the publicly available polyp images in various databases is still rather limited, it is important to develop metaheuristic methods, such as fuzzy inference methods, along with the deep learning algorithms to improve and validate detection and classification techniques. In the present manuscript firstly a fuzzy rule set is generated and validated. The former process is based on a statistical approach and makes use of histograms of the antecedents. Secondly, a method for selecting relevant antecedent variables is presented. The selection is based on the comparision of the histograms computed from the measured values for the training set. Then the inclusion of the Rényi-entropy-based structural entropy and the spatial filling factor into the set of input variables is proposed and assessed. The beneficial effect of including the mentioned structural entropy of the entropies from the hue and saturation (H and S) colour channels resulted in 65% true positive and 60% true negative rate of the classification for an advantageously selected set of antecedents when working with HSV images.

Mean-shift Object Tracking Algorithm with Systematic Sampling Technique

Mean shift is a fast object tracking algorithm that only considers pixels in an object area, hence its relatively small computational load. This algorithm is suitable for use in real-time conditions in terms of execution time. The use of histograms causes this algorithm to be relatively resistant to rotation and changes in object size. However, its resistance to lighting changes is not optimal. This study aims to improve the performance of the algorithm under lighting changes and reduce its processing time. The proposed technique involves the use of sampling techniques to reduce the number of iterations, optimization of candidate search object locations using simulated annealing, and addition of tolerance parameter to optimize object location search and area-based weighting instead of the Epanechnikov kernel. The results of the one-tail t-test with two independent sample groups reveal that the average performance of the proposed algorithm is significantly better than that of the traditional mean-shift algorithm in terms of resistance to lighting changes and processing time per video frame. In the test involving 999 frames of video images, the average processing time of the proposed algorithm is 83.66 ms, whereas that of the traditional mean-shift algorithm is 116.86 ms.

On the use of histograms of oriented gradients for tremor detection from sinusoidal and spiral handwritten drawings of people with Parkinson's disease.

Parkinson's disease (PD), whose cardinal signs are tremor, rigidity, bradykinesia, and postural instability, gradually reduces the quality of life of the patient, making early diagnosis and follow-up of the disorder essential. This study aims to contribute to the objective evaluation of tremor in PD by introducing and assessing histograms of oriented gradients (HOG) to the analysis of handwriting sinusoidal and spiral patterns. These patterns were digitized and collected from handwritten drawings of people with PD (n = 20) and control healthy individuals (n = 20). The HOG descriptor was employed to represent relevant information from the data classified by three distinct machine-learning methods (random forest, k-nearest neighbor, support vector machine) and adeep learning method (convolutional neural network) to identify tremor in participants with PD automatically. The HOG descriptor allowed for the highest discriminating rates (accuracy 83.1%, sensitivity 85.4%, specificity 80.8%, area under the curve 91%) on the test set of sinusoidal patterns by using the one-dimensional convolutional neural network. In addition, ANOVA and Tukey analysis showed that the sinusoidal drawing is more appropriate than the spiral pattern, which is the most common drawing used for tremor detection. This research introduces a novel and alternative way of quantifying and evaluating tremor by means of handwritten drawings.

Infographics as a medical informing tool during the covid-19 pandemic

The article defines the features of infographics as a means of medical information during the COVID-19 pandemic. In particular, the possibilities of presenting the topic of coronavirus in infographics and the factors influencing on the target audience were identified. The article clarifies the main possibilities of using infographics during a pandemic, in particular, the use of infographics as a tool for communicating with the target audience and influencing the solution of social problems. Particular attention is paid to the specifics of infographics as a component of visual communications during a pandemic. The article defines the dominant colors in infographics, use of histograms, charts and maps, the ratio of «text-centric» and «graphic-centric» infographics. It was found that in infographics, images of characters of a certain gender are predominantly used, rather than gender-neutral symbols.

Improving Arabic Instant Machine Translation: The Case of Arabic Triangle of Language

Recently, instant translator applications would be a very useful applications when traveling especially when one knows little about the language of the country she/he is traveling to. Arabic to English instant translation has not yet been made available by most applications. In this Article, we attempt to provide mimic way of an application for instant Arabic to English translation. The system provides translation for Arabic Triangle of Language (ālmṯlṯātāllġwyh) which includes Arabic three words that are homographs. The process starts by capturing an image for a homograph using a mobile phone camera, after that the captured word is recognized, taking the diacritic markers into consideration, using an Arabic Optical Character Recognition (OCR). Finally, the system provides an English translation to the homograph. The researchers made use of Histograms of Oriented Gradients (HOG) features and a set of structural and geometrical features of Arabic word segmented and the SVM (multi class) classifier for classification, then providing the English meaning.

Univariate Outliers: A Conceptual Overview for the Nurse Researcher.

The presence of statistical outliers is a shared concern in research. If ignored or improperly handled, outliers have the potential to distort the estimate of the parameter of interest and thus compromise the generalizability of research findings. A variety of statistical techniques are available to assist researchers with the identification and management of outlier cases. The purpose of this paper is to provide a conceptual overview of univariate outliers with special focus on common techniques used to detect and manage univariate outliers. Specifically, this paper discusses the use of histograms, boxplots, interquartile range, and z-score analysis as common univariate outlier identification techniques. The paper also discusses the outlier management techniques of deletion, substitution, and transformation.

Implementación de un prototipo para captura y procesamiento digital de imágenes térmicas adquiridas desde un UAV

El presente trabajo se enfoca en el desarrollo de un prototipo para captura y procesamiento de imágenes térmicas desde un UAV (Vehículo Aéreo no Tripulado). El sistema está compuesto por dos partes: una etapa “aire” instalada en un dron DJI Phantom 3 Standard; y, otra programada en un PC receptor denominada “tierra”. El sistema aire permite la adquisición de imágenes con el uso de 3 elementos: una cámara térmica Flir Lepton, una tarjeta Raspberry Pi y un módulo GPS (para georreferenciación), mientras que para la transmisión de las imágenes a tierra se hace uso de una red Ad-Hoc. En la PC se efectúa un análisis de la información mediante el uso de histogramas y la detección de bordes (algoritmo de Canny), lo que desemboca en la generación de un filtro que permitirá discriminar en qué fotografías se puede definir claramente la ubicación de puntos de calor, evitando así el desperdicio de tiempo y potencia de procesamiento en la detección de falsos positivos en las imágenes. Finalmente, y para la comprobación de la correcta operación del sistema, el prototipo fue probado en condiciones climáticas adversas (neblina) en el sector del Volcán Pululahua.

Serviceability Performance Analysis of Concrete Box Girder Bridges Under Traffic-Induced Vibrations by Structural Health Monitoring: A Case Study

The perceptible vibration of concrete box girders under traffic loads is an important topic in existing bridges, on which vehicle movement often cause vibrations too strong from the viewpoints of travelers. In this paper, the results of an extensive program of full-scale ambient vibration tests involving a 380 m concrete box girder bridge, the Cannavino bridge in Italy, are presented. The human safety assessment procedure of the bridge includes ambient vibration testing, identification of modal parameters from ambient vibration data, comparison with a detailed finite element modeling as validation of experimental measurements, comparison of peak accelerations to reference values from technical standards/literature in order to estimate the vibration level, and evaluation of safety by the use of histograms. A total of nine modal frequencies are identified for the deck structure within the frequency range of 0–10 Hz. The results of the ambient vibration survey are compared to the modal frequencies computed by a detailed three-dimensional finite element model of the bridge, obtaining a very good agreement. It emerges that a linear finite element model appears to be capable of capturing the dynamic behavior of concrete box girder bridges with very good accuracy. For each direction, experimental peak accelerations are compared to acceptable human levels available in technical standards/literature, showing that traffic loads mainly induce a vertical component of vibration on the bridge deck. Finally, the elaboration of histograms allows to assess that the bridge is exposed to clearly perceptible vertical vibrations, requiring the adoption of suitable vibration reduction devices.

Attaining competitive positioning through SPC – an experimental investigation from SME

Purpose – The purpose of this paper is to provide a conceptual framework which connects theory with straightforward application of statistical process control (SPC) in discovering and analyzing causes of variation to eliminate quality problems, which not only helps small and medium enterprises (SMEs) to improve their processes but also helps to attain competitive positioning. Design/methodology/approach – Based on theory and methodological framework, an experimental study has been presented. Use of histograms, X (bar) and R control charts and process capability plots and cause-and-effect diagrams have been made to analyse the assignable causes. A case from an SME engaged in machining of automotive parts is investigated. Findings – The results demonstrate the effectiveness of SPC in evaluating and eliminating quality problems. The machine capability (CP) and the process capability (CPk) values are also obtained to know inherent variation in the process. If these quality tools are applied with management support and apt knowledge, attained through proper training and motivation, then in this cut-throat competitive world, SMEs can establish their market position by enhancing the quality and productivity of their products/processes. Practical limitations/implications – From the study, the authors conclude that application of SPC requires thorough preparation, management commitment and human resource management through proper training, teamwork and motivation embedded with a sound measurement and control system. Originality/value – The present study bridges the gap between theory and practice by developing a conceptual framework and providing a practical support by illustrating a case from an SME engaged in machining of automotive parts.

Histogram analysis reveals a better delineation of tumor volume from background in 18F-FET PET compared to CBV maps in a hybrid PET–MR studie in gliomas

Anatomical imaging with magnetic resonance imaging (MRI) is currently the method of first choice for diagnostic investigation of glial tumors. However, different MR sequences may over- or underestimate tumor size and thus it may not be possible to delineate tumor from adjacent brain. In order to compensate this confinement additonal MR sequences like perfusion weighted MRI (PWI) with regional cerebral blood volume (rCBV) or positron emission tomography (PET) with aminoacids are used to gain further information. Recent studies suggest that both of theses image modalities provide similar diagnostic information. For comparison tumor to brain ratios (TBR) with mean and maximum values are frequently used but results from different studies can often not be checked against each other. Furthermore, especially the maximum TBR in rCBV is at risk to be falsified by artifacts (e.g. blood vessels). These confinements are reduced by the use of histograms since all information of the VOIs are equally displayed. In this study we measured and compared the intersection of tumor and reference tissue histograms in 18F-FET PET and rCBV maps in glioma patients. MethodsTwenty-seven glioma patients with contrast enhancing lesion on T1-weighted MR images were investigated using static 18F-FET PET and rCBV in MRI using a PET–MR hybrid scanner. In all patients diagnosis was confirmed histologically (7 grade II gliomas, 6 grade III gliomas and 14 grade IV gliomas). We generated a set of tumor and reference tissue Volumes-of-Interest (VOIs) based on T1 weighted images in MRI with the tumor VOI defined by contrast enhancement and transferred these VOIs to the corresponding 18F-FET PET scans and rCBV maps. From these VOIs we generated tumor and reference tissue histograms with a unity of one for each curve integral and measured the proportion of the area under the tumor curve that falls into the reference curve for 18F-FET PET and rCBV maps for each patient. ResultsThe mean proportion of the area under the tumor curve that falls into the reference curve was smaller in FET PET than in rCBV maps (13.1±19.5% vs. 45.0±21.3%; p<0.001). ConclusionsMeasuring the intersection of tumor and reference tissue histograms in 18F-FET PET and rCBV maps based on identical VOIs defined in anatomical images with tumor marked by contrast enhancement in T1-weighted images can easily be performed and indicates that tumor volume can better be discriminated from background in 18F-FET PET compared to rCBV in MRI. With this approach artefacts (e.g. blood vessels) impair the results to a lesser degree compared to the mean and especially maximal TBRs which are often used when comparing these two image modalities.

Cholesterolaemia and triacylglycerolaemia in farmed rainbow trout,Oncorhynchus mykiss

Physiological (reference) value intervals determined by the lower 2.5% and upper 97.5% quantiles were calculated for blood plasma cholesterol (CHOL) and triacylglycerol (TGL) in farmed rainbow trout, Oncorhynchus mykiss, in raceway culture. The fish were given dry pelleted diets that contained 37–47% crude protein and 7–14% crude fat and were kept at a stocking density of 50 kg m−3 in tanks provided with running freshwater at an ambient temperature of 3–16°C and dissolved oxygen, 8.4–11.5 mg L−1. Blood was sampled between September and November at a photoperiod of 9:00–13:00 hours: 11:00–15:00 hours (light:dark). Cholesterol levels were significantly (P = 0.0001) greater in males (4.7–12.1 mmol L−1, n = 34, mean weight 406 ± 138 g) than immature females (3.2–9.7 mmol L−1, n = 386, mean weight 416 ± 147 g). Physiological range for TGL in immature females and males was 2.4–14.4 mmol L−1 (n = 249, mean weight 418 ± 149 g). The distribution and density of the quantiles in the tested reference group were made possible by the use of histograms, which showed normal distributions for CHOL in males and in females, but not for TGL, in which a sinistral asymmetry was found. Correlation and regression analyses indicated significant (P = 0.0000) dependence between the CHOL and total protein (r2 = 76.2%), CHOL and Fulton's condition factor (r2 = 43.3%) and CHOL and absolute weight of liver (r2 = 45.5%). Fluctuation in cholesterolaemia and triacylglycerolaemia, depending on nutrition and the aquaculture method is discussed below.

Constructing smooth potentials of mean force, radial distribution functions, and probability densities from sampled data

In this paper a method of obtaining smooth analytical estimates of probability densities, radial distribution functions, and potentials of mean force from sampled data in a statistically controlled fashion is presented. The approach is general and can be applied to any density of a single random variable. The method outlined here avoids the use of histograms, which require the specification of a physical parameter (bin size) and tend to give noisy results. The technique is an extension of the Berg-Harris method [B. A. Berg and R. C. Harris, Comput. Phys. Commun. 179, 443 (2008)], which is typically inaccurate for radial distribution functions and potentials of mean force due to a nonuniform Jacobian factor. In addition, the standard method often requires a large number of Fourier modes to represent radial distribution functions, which tends to lead to oscillatory fits. It is shown that the issues of poor sampling due to a Jacobian factor can be resolved using a biased resampling scheme, while the requirement of a large number of Fourier modes is mitigated through an automated piecewise construction approach. The method is demonstrated by analyzing the radial distribution functions in an energy-discretized water model. In addition, the fitting procedure is illustrated on three more applications for which the original Berg-Harris method is not suitable, namely, a random variable with a discontinuous probability density, a density with long tails, and the distribution of the first arrival times of a diffusing particle to a sphere, which has both long tails and short-time structure. In all cases, the resampled, piecewise analytical fit outperforms the histogram and the original Berg-Harris method.

Contextual Classification of Image Patches with Latent Aspect Models

We present a novel approach for contextual classification of image patches in complex visual scenes, based on the use of histograms of quantized features and probabilistic aspect models. Our approach uses context in two ways: (1) by using the fact that specific learned aspects correlate with the semantic classes, which resolves some cases of visual polysemy often present in patch-based representations, and (2) by formalizing the notion that scene context is image-specific--what an individual patch represents depends on what the rest of the patches in the same image are. We demonstrate the validity of our approach on aman-made versus natural patch classification problem. Experiments on an image collection of complex scenes show that the proposed approach improves region discrimination, producing satisfactory results and outperforming two noncontextual methods. Furthermore, we also show that co-occurrence and traditional (Markov random field) spatial contextual information can be conveniently integrated for further improved patch classification.

Manejo químico de ciperus rotundus e digitaria spp. em áreas comerciais de produção de cana-de-açúcar

sUMMARY: The control of Purple nutsedge (Cyperus rotundus) and Crabgrass (Digitaria spp) in the cultivation of sugar cane must be done in an integrated way, mainly in the reform or implantation of the sugar cane plantation. Among the measures that can be adopted for reduction of the potential of Purple nutsedge and Crabgrass infestation is the daily application of pre-emergence herbicides in the plantation of sugar cane. It was aimed with this work to study the effectiveness of the herbicides sulfentrazone and clomazone used in sugar cane areas in the control of the Cyperus rotundus L species respectively. For this, a survey of the areas with application of these herbicides was carried through and made a visual evaluation of control from 70 to 90 DAA with regard to the witness of each plantation spot. The data had been tabulated and analyzed with the use of histograms and polygon of frequency. The results allowed concluding that it was possible to happen areas the herbicide sulfentrazone in high infestations of the harmful plant Cyperus or when the applied doses are inferior to the recommended for the same one. The control of the herbicide over the capim-colchao allows assuming that it’s an option for the handling of this harmful plant.

Total calcium and inorganic phosphate in the blood plasma of farmed rainbow trout,Oncorhynchus mykiss

Reference (physiological) value intervals determined by the lower 2.5% and upper 97.5% quantiles were calculated for total calcium (Cat) and inorganic phosphate (P) in farmed rainbow trout, Oncorhynchus mykiss, at an age of 10–12 months in raceway culture. Total calcium levels were significantly (P≤0.0009) greater in males (2.63–4 mmol L−1, n=47, mean weight 359±142 g) than in immature females (2.48–3.97 mmol L−1, n=410, mean weight 400±145 g). The reference range for P in immature females and males was 3.09–9.32 mmol L−1 (n=467, mean weight 401±144 g). The distribution and density of the quantiles in the tested reference group were made possible by the use of histograms, which showed normal distributions for Cat in males but not for Cat and P in immature females and males, in which a sinistral asymmetry was found.

Statistical nonparametric methods for the study of fossil populations

The precise knowledge of the number and nature of the species belonging to a fossil assemblage as well as of the structure of each species (e.g., age, sex) is of great importance in paleontology. Mixture analysis based on the method of maximum likelihood is a modern statistical technique that concerns the problem of samples consisting of several components, the composition of which is not known. Nonparametric bootstrap and jackknife techniques are used to calculate a confidence interval for each estimated parameter (prior probability, mean, standard deviation) of each group. The bootstrap method is also used to evaluate mathematically how many groups are present in a sample. Experimental density smoothing using the kernel method appears to be a better solution than the use of histograms for the estimation of a distribution. This paper presents some basic concepts and procedures and discusses some preliminary results concerning sex ratios and mortality profile assessments using bones and tooth metric data of small (Ovis antiqua) and large (Bos primigenius) bovines from European Pleistocene sites.

Histograms analysis for image retrieval

This paper analyzes the use of histograms of low-level image features, such as color and luminance, as descriptors for image-retrieval purposes. A novel definition of histogram capacity curve taking into account the density distribution of histograms in the corresponding spaces is proposed and used to quantify the effectiveness of image descriptors and histogram dissimilarities in image retrieval applications. The results permit the design of scalable image-retrieval systems which make optimal use of computational and storage resources.

On the ab initio solution of the phase problem or macromolecules at very low resolution. II. Generalized likelihood based approach to cluster discrimination.

The multisolution strategies for direct phasing at very low resolution, such as the few atoms model technique, result in a number of alternative phase sets, each of them arising from a cluster of closely related models. Use of a Monte-Carlo type computer procedure is suggested to choose between the possible phase sets. It consists of generating a large number of pseudo-atom models inside the mask defined by a trial phase set and the use of histograms of magnitude correlation to evaluate the masks. It is shown that the procedure may be considered as a generalization of the statistical maximum-likelihood principle and may be used as a powerful supplementary tool in the likelihood-based approaches to the phase problem solution.