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Journal articles on the topic 'Histogram'
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1
Kala, Zdeněk. "FUZZY PROBABILITY ANALYSIS OF THE FATIGUE RESISTANCE OF STEEL STRUCTURAL MEMBERS UNDER BENDING/FUZI TIKIMYBINĖ ANALIZĖ VERTINANT LENKIAMŲ PLIENINIŲ ELEMENTŲ ATSPARĮ NUOVARGIUI." JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT 14, no. 1 (March 31, 2008): 67–72. http://dx.doi.org/10.3846/1392-3730.2008.14.67-72.
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The paper is aimed at the fuzzy probabilistic analysis of fatigue resistance due to uncertainty of input parameters. The fatigue resistance of the steel member is evaluated by linear fracture mechanics as the number of cycles leading to the propagation of initial cracks into a critical crack resulting in brittle fracture. When the histogram of stress range is known, the fatigue resistance is a random variable. In the event that the histogram is unknown or was acquired from a small number of experiments, another source of uncertainty is of an epistemic origin. Two basic approaches, which make provision for uncertainty of input histograms of stress range, are illustrated in the paper. Uncertainty of histograms of stress range is taken into account by the variability of equivalent stress range in the first stochastic approach. Input histograms as considered as members of a fuzzy set in the second approach. Santrauka Straipsnyje nagrinėjamas atspario nuovargio esant neapibrėžtiems pradiniams duomenims vertinimas naudojant fuzi tikimybinę analizę. Plieninių elementų atsparis nuovargiui pagal tiesinę irimo mechaniką apibūdinamas ciklų skaičiumi, kai pradiniai plyšiai perauga į kritinį plyšį, sukeliantį trapų suirimą. Kai įtempimų kitimo histograma yra žinoma, atsparis nuovargiui yra atsitiktinis dydis. Kai histograma yra nežinoma arba ji atitinka mažą eksperimentų skaičių, atsiranda kitas episteminės kilmės neapibrėžtumas. Pateikiami du pagrindiniai būdai, rodantys histogramos neapibrėžtumą. Pirmuoju, stochastiniu būdu, įtempių kitimo diapazono histograma yra modeliuojama ekvivalentinio įtempio kitimu. Antruoju būdu pradinės histogramos nagrinėjamos kaip fuzi aibės elementai.
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Maksymiv, Mykola, and Taras Rak. "Methods to Increase the Contrast of the Image with Preserving the Visual Quality." Advances in Cyber-Physical Systems 6, no. 2 (December 17, 2021): 140–45. http://dx.doi.org/10.23939/acps2021.02.140.
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Contrast enhancement is a technique for increasing the contrast of an image to obtain better image quality. As many existing contrast enhancement algorithms typically add too much contrast to an image, maintaining visual quality should be considered as a part of enhancing image contrast. This paper focuses on a contrast enhancement method that is based on histogram transformations to improve contrast and uses image quality assessment to automatically select the optimal target histogram. Improvements in contrast and preservation of visual quality are taken into account in the target histogram, so this method avoids the problem of excessive increase in contrast. In the proposed method, the optimal target histogram is the weighted sum of the original histogram, homogeneous histogram and Gaussian histogram. Structural and statistical metrics of “naturalness of the image” are used to determine the weights of the corresponding histograms. Contrast images are obtained by matching the optimal target histogram. Experiments show that the proposed method gives better results compared to other existing algorithms for increasing contrast based on the transformation of histograms.
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Goldstein, Alexandra, and Kam Y. J. Zhang. "The Two-Dimensional Histogram as a Constraint for Protein Phase Improvement." Acta Crystallographica Section D Biological Crystallography 54, no. 6 (November 1, 1998): 1230–44. http://dx.doi.org/10.1107/s0907444998001863.
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The joint distribution of electron density and its gradient in a protein electron-density map was examined. This joint distribution was represented by a two-dimensional histogram (2D histogram) of electron-density values and the modulus of the gradient. 16 structures representing distinct protein-fold families were selected to study the dependence of the 2D histogram on resolution, overall temperature factor, structural conformation and phase error. The similarity between the histograms for a pair of structures was measured by correlation coefficient, and the residual provided a measure of the difference. The 2D histogram was found to vary with resolution and overall temperature factor, but was found to be insensitive to structure conformation. The average correlation coefficient between pairs of 2D histograms at three different resolutions examined was 0.90 with a standard deviation of 0.04. The average residual for the same condition was 0.13 with a standard deviation of 0.03. The 2D histogram was also found to be sensitive to phase error. The average correlation coefficient and residual between 2D histograms with 10° phase difference are 0.71 and 0.18, respectively. The variation of the 2D histogram resulting from structure-conformation changes was estimated to be equivalent to that of a 4° phase error. This establishes the minimal phase error that a 2D histogram-matching method could achieve. The conservation of the 2D histogram with respect to structure conformation enables the prediction of the ideal 2D histogram for unknown structures. The sensitivity of the 2D histogram to phase error suggests that it could be used as a target for the density-modification method and also could be used as a figure of merit for phase selection in ab initio phasing.
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Dhal, Krishna Gopal, Sankhadip Sen, Kaustav Sarkar, and Sanjoy Das. "Entropy based Range Optimized Brightness Preserved Histogram-Equalization for Image Contrast Enhancement." International Journal of Computer Vision and Image Processing 6, no. 1 (January 2016): 59–72. http://dx.doi.org/10.4018/ijcvip.2016010105.
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In this study the over-enhancement problem of traditional Histogram-Equalization (HE) has been removed to some extent by a variant of HE called Range Optimized Entropy based Bi-Histogram Equalization (ROEBHE). In ROEBHE image histogram has been thresholded into two sub-histograms i.e. histograms corresponding to background and foreground. The threshold is calculated by maximizing the sum of the entropy of these two sub-histograms. The range for equalization has been optimized by maximizing the Peak-Signal to Noise ratio (PSNR). The experimental results prove that ROEBHE has prevailed over existing methods and PSNR is a better range optimizer than Absolute Mean Brightness Error (AMBE).
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Jing, Hui, Mei Fa Huang, and Cong Li. "3D Mechanical Models Retrieval Based on Combined Histograms for Rapid Product Design." Applied Mechanics and Materials 16-19 (October 2009): 65–69. http://dx.doi.org/10.4028/www.scientific.net/amm.16-19.65.
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Shape Distribution (3DSD) and Radius Angle Histogram (RAH) are useful methods for retrieving 3D model in mechanical engineering. Through these methods have advantages such as fast speeds and simple operations, the retrieval precision are not very high enough. To improve the retrieval precision, a new method named combined histograms which integrates the advantages of 3DSD and RAH is proposed. This method makes use of the information both of shape and surface of the models to be retrieved. In the retrieval process, the shape histogram and the radius angle histogram of the retrieved model are first extracted. Then, the combined histograms of the model are established by integrating the shape histogram and the radius angle histogram. To validate the proposed method, an experiment is given. The experiment results show that the proposed method has higher retrieval precision than that of 3DSD and RAH and is suitable for mechanical model design.
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Wu, Jun Feng, Xian Qiang Lv, Wen Lian Yang, Ye Tao, Jing Zhang, and Song Yang. "Image Retrieval Based on Color Histogram of Saliency Map." Advanced Materials Research 989-994 (July 2014): 3552–55. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.3552.
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With the development of the internet, more and more images appear in the internet. How to effectively retrieve the desired image is still an important problem. In the past, traditional color histogram is used image retrieval system, but color histograms lack spatial information and are sensitive to intensity variation, color distortion and cropping. As a result, images with similar histograms may have totally different semantics. So the spatial information should be included in color histogram. The color histogram based on saliency map approach is introduced to overcome the above limitations. In this paper, we present a robust image retrieval based on color histogram of saliency map. Firstly, in order to extract useful spatial information of each pixel, the steady saliency map of the images is extracted. Then, color histogram based on saliency map is introduced, and the similarity between color images is computed by using the color histogram of saliency map. Experimental results show that the proposed color image retrieval is more accurate and efficient in retrieving the user-interested images.
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Porebski, Alice, Vinh Truong Hoang, Nicolas Vandenbroucke, and Denis Hamad. "Combination of LBP Bin and Histogram Selections for Color Texture Classification." Journal of Imaging 6, no. 6 (June 23, 2020): 53. http://dx.doi.org/10.3390/jimaging6060053.
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LBP (Local Binary Pattern) is a very popular texture descriptor largely used in computer vision. In most applications, LBP histograms are exploited as texture features leading to a high dimensional feature space, especially for color texture classification problems. In the past few years, different solutions were proposed to reduce the dimension of the feature space based on the LBP histogram. Most of these approaches apply feature selection methods in order to find the most discriminative bins. Recently another strategy proposed selecting the most discriminant LBP histograms in their entirety. This paper tends to improve on these previous approaches, and presents a combination of LBP bin and histogram selections, where a histogram ranking method is applied before processing a bin selection procedure. The proposed approach is evaluated on five benchmark image databases and the obtained results show the effectiveness of the combination of LBP bin and histogram selections which outperforms the simple LBP bin and LBP histogram selection approaches when they are applied independently.
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Ognjenovic, Visnja, Vladimir Brtka, Jelena Stojanov, Eleonora Brtka, and Ivana Berkovic. "The Cuts Selection Method Based on Histogram Segmentation and Impact on Discretization Algorithms." Entropy 24, no. 5 (May 11, 2022): 675. http://dx.doi.org/10.3390/e24050675.
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The preprocessing of data is an important task in rough set theory as well as in Entropy. The discretization of data as part of the preprocessing of data is a very influential process. Is there a connection between the segmentation of the data histogram and data discretization? The authors propose a novel data segmentation technique based on a histogram with regard to the quality of a data discretization. The significance of a cut’s position has been researched on several groups of histograms. A data set reduct was observed with respect to the histogram type. Connections between the data histograms and cuts, reduct and the classification rules have been researched. The result is that the reduct attributes have a more irregular histogram than attributes out of the reduct. The following discretization algorithms were used: the entropy algorithm and the Maximal Discernibility algorithm developed in rough set theory. This article presents the Cuts Selection Method based on histogram segmentation, reduct of data and MD algorithm of discretization. An application on the selected database shows that the benefits of a selection of cuts relies on histogram segmentation. The results of the classification were compared with the results of the Naïve Bayes algorithm.
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Elmore, Kimberly L. "Alternatives to the Chi-Square Test for Evaluating Rank Histograms from Ensemble Forecasts." Weather and Forecasting 20, no. 5 (October 1, 2005): 789–95. http://dx.doi.org/10.1175/waf884.1.
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Abstract Rank histograms are a commonly used tool for evaluating an ensemble forecasting system’s performance. Because the sample size is finite, the rank histogram is subject to statistical fluctuations, so a goodness-of-fit (GOF) test is employed to determine if the rank histogram is uniform to within some statistical certainty. Most often, the χ2 test is used to test whether the rank histogram is indistinguishable from a discrete uniform distribution. However, the χ2 test is insensitive to order and so suffers from troubling deficiencies that may render it unsuitable for rank histogram evaluation. As shown by examples in this paper, more powerful tests, suitable for small sample sizes, and very sensitive to the particular deficiencies that appear in rank histograms are available from the order-dependent Cramér–von Mises family of statistics, in particular, the Watson and Anderson–Darling statistics.
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Ongkittikul, Surachai, Wachirapong Kesjindatanawaj, and Sanun Srisuk. "Multi-Window and Line Scan Histogram Features for Bilateral Filtering." Applied Mechanics and Materials 781 (August 2015): 547–50. http://dx.doi.org/10.4028/www.scientific.net/amm.781.547.
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Bilateral filtering is the crucial process to enhance the image. This paper aims to improve the bilateral filtering base on the multi-window and line scan histogram scheme. The multi-windows histogram has been introduced to solve the problem when apply to large image by using a number of the window histogram with different weight to estimate the domain filtering function. Anyway, the complexity of this algorithm is increase by multiply of the number ofmwindows histogram that use for estimating the domain filtering. To improve this, our algorithm that based on the multi-windows histogram is proposed which can reduce the complexity of the filtering from O(mB) to O(m+B). Also, our algorithm uses multi-line scan histogram extraction which adapted from dual line scan histogram extraction to reduce the complexity. The experiments show the new algorithm has slightly increase for filtering when increase the number of the window histogramm.
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Klose, Uwe, Marion Batra, and Thomas Nägele. "Parameterization of the Age-Dependent Whole Brain Apparent Diffusion Coefficient Histogram." BioMed Research International 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/373716.
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Purpose. The distribution of apparent diffusion coefficient (ADC) values in the brain can be used to characterize age effects and pathological changes of the brain tissue. The aim of this study was the parameterization of the whole brain ADC histogram by an advanced model with influence of age considered.Methods. Whole brain ADC histograms were calculated for all data and for seven age groups between 10 and 80 years. Modeling of the histograms was performed for two parts of the histogram separately: the brain tissue part was modeled by two Gaussian curves, while the remaining part was fitted by the sum of a Gaussian curve, a biexponential decay, and a straight line.Results. A consistent fitting of the histograms of all age groups was possible with the proposed model.Conclusions. This study confirms the strong dependence of the whole brain ADC histograms on the age of the examined subjects. The proposed model can be used to characterize changes of the whole brain ADC histogram in certain diseases under consideration of age effects.
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Dai, Shang Ping, Tao Jin, and Qing Liang. "Segmentation of Adhering Cell Images Based on Two-Dimensional Histogram." Advanced Materials Research 588-589 (November 2012): 2108–12. http://dx.doi.org/10.4028/www.scientific.net/amr.588-589.2108.
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For duplication and adhering of cells images in medical micro-images, it presents a variety of segmentation method based on two-dimensional histogram of the distance transformed images.Two-dimensional histogram is constructed by the concerned center pixel’s gray scale in a window .We can select gray mask by observing the characteristics distribution of the histogram of the region through the adhering cell image,getting the inverse transformed images.Experimental results show that different type of histograam segmention method are applicable to different adhering cell images’ segmentation.
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Chen, Ling Xia, and Liang Xu. "The Method Research of Auto-Generating Engineering Geology Histogram Based on MapGIS." Applied Mechanics and Materials 246-247 (December 2012): 1003–6. http://dx.doi.org/10.4028/www.scientific.net/amm.246-247.1003.
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This thesis study on algorithms of auto-generating engineering geology histogram, For example: the hierarchy model of drilling histogram, rendering algorithm of lithologic columnar and the rendering algorithm column separator. MapGIS was secondary exploitation platformand , and Borland C++ Builder 6.0 was programming language. Through called MapGIS’ secondary exploitation components directly and used algorithms mentioned in this paper, engineering geology histograms was auto-generated. Being validated by practical engineering geology data, the software system not only runs steadily and generates plot rapidly, but also the result is exact, generating histograms is slinky and concordant.
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Inoue, Kohei, Naoki Ono, and Kenji Hara. "Local Contrast-Based Pixel Ordering for Exact Histogram Specification." Journal of Imaging 8, no. 9 (September 10, 2022): 247. http://dx.doi.org/10.3390/jimaging8090247.
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Histogram equalization is one of the basic image processing tasks for contrast enhancement, and its generalized version is histogram specification, which accepts arbitrary shapes of target histograms including uniform distributions for histogram equalization. It is well known that strictly ordered pixels in an image can be voted to any target histogram to achieve exact histogram specification. This paper proposes a method for ordering pixels in an image on the basis of the local contrast of each pixel, where a Gaussian filter without approximation is used to avoid the duplication of pixel values that disturbs the strict pixel ordering. The main idea of the proposed method is that the problem of pixel ordering is divided into small subproblems which can be solved separately, and then the results are merged into one sequence of all ordered pixels. Moreover, the proposed method is extended from grayscale images to color ones in a consistent manner. Experimental results show that the state-of-the-art histogram specification method occasionally produces false patterns, which are alleviated by the proposed method. Those results demonstrate the effectiveness of the proposed method for exact histogram specification.
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Dembinski, Hans Peter, Jim Pivarski, and Henry Schreiner. "Recent developments in histogram libraries." EPJ Web of Conferences 245 (2020): 05014. http://dx.doi.org/10.1051/epjconf/202024505014.
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Boost.Histogram, a header-only C++14 library that provides multidimensional histograms and profiles, became available in Boost 1.70. It is extensible, fast, and uses modern C++ features. Using template metaprogramming, the most efficient code path for any given configuration is automatically selected. The library includes key features designed for the particle physics community, such as optional under- and overflow bins, weighted increments, reductions, growing axes, thread-safe filling, and memory-efficient counters with high-dynamic range. Python bindings for Boost.Histogram are being developed in the Scikit-HEP project to provide a fast, easy-to-install package as a backend for other Python libraries and for advanced users to manipulate histograms. Versatile and efficient histogram filling, effective manipulation, multithreading support, and other features make this a powerful tool. This library has also driven package distribution efforts in Scikit-HEP, allowing binary packages hosted on PyPI to be available for a very wide variety of platforms. Two other libraries fill out the remainder of the Scikit-HEP Python histogramming effort. Aghast is a library designed to provide conversions between different forms of histograms, enabling interaction between histogram libraries, often without an extra copy in memory. This enables a user to make a histogram in one library and then save it in another form, such as saving a Boost.Histogram in ROOT. And Hist is a library providing friendly, analyst-targeted syntax and shortcuts for quick manipulations and fast plotting using these two libraries.
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GONZÁLEZ, ORLANDO. "TEACHERS’ CONCEPTIONS AND PROFESSIONAL KNOWLEDGE OF VARIABILITY FROM THEIR INTERPRETATION OF HISTOGRAMS: THE CASE OF VENEZUELAN IN-SERVICE SECONDARY MATHEMATICS TEACHERS." STATISTICS EDUCATION RESEARCH JOURNAL 20, no. 2 (December 25, 2021): 15. http://dx.doi.org/10.52041/serj.v20i2.412.
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Many studies have reported on the influence of teachers’ conceptions of variability on different aspects of their professional knowledge for teaching statistics and their classroom practices. However, research on these kind of conceptions is still scarce, particularly in Latin American countries like Venezuela. In an effort to help fill this gap, a qualitative study was conducted that aimed to characterize the different ways in which Venezuelan in-service secondary school mathematics teachers conceptualize variability. For that purpose, a survey instrument was developed and administered to 27 teachers working at the metropolitan area of Caracas. This paper focuses on the participants’ answers to two items in which interpretation of histograms was necessary. It was found that about a third of the participants exhibited a sophisticated recognition of variability (e.g., gave answers connecting both middles and extremes), whereas about half of them exhibited misconceptions of variability, such as acknowledging variability from the viewpoint of idiosyncratic ideas, or the degree of symmetry (or lack thereof) of a histogram. Moreover, it was also found that about two-thirds of the participants were unable to correctly match real-life contexts to their corresponding histograms, while about two-fifths were unable to correctly determine the accuracy or inaccuracy of descriptions of the variability in a histogram. The author discusses possible reasons for the obtained results, in order to identify relevant implictions for teacher education in the area of statistics. Abstract: Spanish Diversos estudios han reportado que las concepciones de los docentes sobre variabilidad influencian tanto su conocimiento profesional para la enseñanza de la estadística, como sus prácticas en el aula. Sin embargo, investigaciones sobre este tipo de concepciones son aún escasas, particularmente en países latinoamericanos como Venezuela. Intentando satisfacer esta necesidad, se condujo un estudio cualitativo para identificar y caracterizar las diferentes maneras en que maestros venezolanos de matemáticas a nivel de secundaria conceptualizan la variabilidad. Con tal propósito, un cuestionario fue desarrollado y administrado a 27 docentes en el área metropolitana de Caracas. Este artículo se centra en las respuestas dadas por los participantes a dos ítems del cuestionario, en los que era necesaria la interpretación de histogramas. Se descubrió que aproximadamente un quinto de los participantes demostró un reconocimiento sofisticado de la variabilidad (e.g., considerar simultáneamente valores centrales y extremos de un histograma), mientras que alreadedor de la mitad exhibió concepciones erróneas, tales como el reconocimiento de la variabilidad a partir de ideas idiosincrásicas, o del grado de simetría de un histograma. Además, unos cuatro quintos de los participantes fueron incapaces de establecer una correspondencia entre contextos de la vida real y sus respectivos histogramas, mientras que unos dos quintos fueron incapaces de determinar si descripciones de la variabilidad en un histograma eran o no correctas. El autor discute las posibles razones de los resultados obtenidos, con el fin de identificar implicaciones relevantes para la formación docente en el área de la estadística.
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Griffin, C. M., J. Dehmeshki, D. T. Chard, G. JM Parker, G. J. Barker, A. J. Thompson, and D. H. Miller. "T1 histograms of normal-appearing brain tissue are abnormal in early relapsing-remitting multiple sclerosis." Multiple Sclerosis Journal 8, no. 3 (June 2002): 211–16. http://dx.doi.org/10.1191/1352458502ms807oa.
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Objective: To use both whole-brain and normal-appearing brain tissue (NABT) T1 relaxation time histograms to investigate abnormalities in early relapsing-remitting (RR) multiple sclerosis (MS). Background: In patients with established MS, both lesions and NABT exhibit an increase in T1 relaxation time. By using T1 histogram analysis, it is hoped that such changes in early disease can be detected. Method: Twenty-seven patients and 14 age- and sex-matched controls underwent magnetic resonance imaging (MRI) of the brain, which included the following sequences: 1) proton density (PD)- and T2-weighted fast spin echo (FSE) to measure T2 lesion load, 2) PD- and T1-weighted gradient echos from which T1 relaxation was calculated, and 3) T1-weighted SE imaging pre- and post-triple dose (0.3 mmol/kg) gadolinium (Gd-DTPA) to measure T1 hypointense and gadolinium-enhancing lesion loads, respectively. All patients had RR MS with disease duration <3 years (median 1.7 years). Statistical parametric mapping (SPM) 99 was used to segment brain from cerebrospinal fluid (CSF), and lesions were segmented using a local thresholding technique. Results: Both whole-brain and NABT histograms were abnormal for all six T1 histogram parameters that were measured. For NABT, the mean T1 was 1027 (-74) ms in patients and 969 (-41) ms in controls (p=0.003). There was little difference between the global and NABT histograms, which indicates that most of the whole-brain histogram abnormality derives from normal-appearing tissues. There was a correlation between the Nine-Hole Peg Test and NABT T1 measures. Conclusion: There are widespread abnormalities of NABT in early RR MS, which were sensitively detected by T1 relaxation time histogram analysis. As such, T1 histogram analysis appears promising for studying the natural history of early RR MS, and in the monitoring of response to treatment.
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Mirzargar, Mahsa, and Jeffrey L. Anderson. "On Evaluation of Ensemble Forecast Calibration Using the Concept of Data Depth." Monthly Weather Review 145, no. 5 (April 10, 2017): 1679–90. http://dx.doi.org/10.1175/mwr-d-16-0351.1.
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Abstract Various generalizations of the univariate rank histogram have been proposed to inspect the reliability of an ensemble forecast or analysis in multidimensional spaces. Multivariate rank histograms provide insightful information about the misspecification of genuinely multivariate features such as the correlation between various variables in a multivariate ensemble. However, the interpretation of patterns in a multivariate rank histogram should be handled with care. The purpose of this paper is to focus on multivariate rank histograms designed based on the concept of data depth and outline some important considerations that should be accounted for when using such multivariate rank histograms. To generate correct multivariate rank histograms using the concept of data depth, the datatype of the ensemble should be taken into account to define a proper preranking function. This paper demonstrates how and why some preranking functions might not be suitable for multivariate or vector-valued ensembles and proposes preranking functions based on the concept of simplicial depth that are applicable to both multivariate points and vector-valued ensembles. In addition, there exists an inherent identifiability issue associated with center-outward preranking functions used to generate multivariate rank histograms. This problem can be alleviated by complementing the multivariate rank histogram with other well-known multivariate statistical inference tools based on rank statistics such as the depth-versus-depth (DD) plot. Using a synthetic example, it is shown that the DD plot is less sensitive to sample size compared to multivariate rank histograms.
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Zhang, Shun, Tie-gang Gao, and Fu-sheng Yang. "A High Capacity Reversible Data Hiding Scheme Based on Multi-Level Integer DWT and Histogram Modification." International Journal of Digital Crime and Forensics 6, no. 1 (January 2014): 51–64. http://dx.doi.org/10.4018/ijdcf.2014010104.
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A reversible data hiding scheme based on integer DWT and histogram modification is proposed. In the scheme, the cover media is firstly transformed by Integer DWT (Discrete Wavelet Transformation); then information is embedded through the modification of histograms of the middle and high frequency sub-bands of the DWT coefficients. In order to increase the embedding capacity, a multi-level scheme is proposed, which achieved both high embedding capacity and reversibility. Extensive experimental results have shown that the proposed scheme achieves both higher embedding capacity and lower distortion than spatial domain histogram modification based schemes; and it achieved better performance than integer DCT (Discrete Cosine Transformation) based histogram modification scheme.
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Steffen-Smith, Emilie A., Joelle E. Sarlls, Carlo Pierpaoli, Joanna H. Shih, Robyn S. Bent, Lindsay Walker, and Katherine E. Warren. "Diffusion Tensor Histogram Analysis of Pediatric Diffuse Intrinsic Pontine Glioma." BioMed Research International 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/647356.
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Purpose. To evaluate tumor structure in children with diffuse intrinsic pontine glioma (DIPG) using histogram analyses of mean diffusivity (MD), determine potential treatment and corticosteroid-related effects on MD, and monitor changes in MD distributions over time.Materials and Methods. DTI was performed on a 1.5T GE scanner. Regions of interest included the entire FLAIR-defined tumor. MD data were used to calculate histograms. Patterns in MD distributions were evaluated and fitted using a two-normal mixture model. Treatment-related effects were evaluated using theR2statistic for linear mixed models and Cox proportional hazards models.Results. 12 patients with DIPG underwent one or more DTI exams. MD histogram distributions varied among patients. Over time, histogram peaks became shorter and broader (P=0.0443). Two-normal mixture fitting revealed large lower curve proportions that were not associated with treatment response or outcome. Corticosteroid use affected MD histograms and was strongly associated with larger, sharper peaks (R2=0.51,P=0.0028).Conclusions. MD histograms of pediatric DIPG show significant interpatient and intratumoral differences and quantifiable changes in tumor structure over time. Corticosteroids greatly affected MD and must be considered a confounding factor when interpreting MD results in the context of treatment response.
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Husain, Nursuci Putri, and Nurseno Bayu Aji. "Pemanfaatan Histogram Equalization pada Local Tri Directional Pattern untuk Sistem Temu Kembali Citra." SPECTA Journal of Technology 4, no. 1 (April 1, 2020): 49–58. http://dx.doi.org/10.35718/specta.v4i1.164.
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Abstract
Local tri-directional pattern (LtriDP) is a method of extracting local intensity features from each pixel based on direction. However, this method has not been able to provide good performance in extracting features for image retrieval. One reason that makes image retrieval performance worse is the effect of lighting. Lighting can cause large variations between images. This study proposed utilization of Histogram Equalization (HE). Histogram equalization is a functional method of stretching gray degrees and expanding image contrast. This will make variations in the gray level of the original image can be controlled. There are several main stages in this study, firstly query image and image dataset will be preprocessed with histogram equalization. After that, the image is extracted by a tri-directional pattern and magnitude pattern are searched. A tri-directional pattern will produce two histograms, while a magnitude pattern produces one histogram. The three histograms are combined or joint histogram is performed. Histogram that has been joint is a feature vector. The feature vector will be calculated using a similarity measurement Canberra. After that, an image similar to the query image will be obtained. The experiment was conducted using 3 face datasets namely ORL, BERN, and YALE. The average recall value was 0.422 for the ORL dataset, 0.50 for the BERN dataset, and 0.63 for the YALE dataset. The evaluation show, the proposed method can be used as a process of improving the quality of image datasets in the image retrieval system.
Keywords: Image retrieval system, Local tri-directional pattern, Streching Image, Histogram Equalization, Similarity Measurement Canberra.
Abstrak
Local tri-directional pattern (LtriDP) merupakan salah satu metode ekstraksi fitur intensitas lokal dari setiap piksel berdasarkan arah. Namun, metode ini belum mampu memberikan performa yang baik dalam mengekstrak fitur untuk temu kembali citra. Salah satu alasan yang membuat performa temu kembali citra tidak baik adalah pengaruh pencahayaan. Pencahayaan dapat menyebabkan variasi besar antar citra. Penelitian ini mengusulkan pemanfaatan Histogram Equalization (HE). HE merupakan metode fungsional dalam peregangan derajat keabuan dan memperluas kontras citra. Hal ini akan membuat variasi level keabuan dari citra asli dapat terkendali. Ada beberapa tahapan utama dalam penelitian ini, yang pertama citra query dan citra dataset akan terlebih dahulu di preprocessing dengan histogram equalization. Setelah itu, citra tersebut diekstrak fiturnya, dicari pola tri-directional dan pola magnitude. Pola tri-directional akan menghasilkan dua histogram, sedangkan pola magnitude menghasilkan satu histogram. Ketiga histogram tersebut kemudian disatukan atau dilakukan joint histogram. Histogram yang telah dijoint merupakan vektor fitur. Vektor fitur tersebut akan dihitung rankingnya menggunakan pengukuran jarak canberra. Setelah itu, akan didapatkan citra yang mirip dengan citra query. Uji coba dilakukan dengan menggunakan 3 dataset wajah yaitu ORL, BERN, dan YALE. Nilai rata-rata recall yang di dapatkan 0,422 untuk dataset ORL, 0,50 untuk dataset BERN, dan 0,63 untuk dataset YALE. Dari hasil evaluasi tersebut, dapat disimpulkan metode yang diusulkan dapat digunakan sebagai proses peningkatan kualitas dataset citra pada system temu kembali citra.
Keywords: Sistem Temu Kembali Citra, Local tri-directional pattern, Peregangan Kontras, Histogram Equalization, Perhitungan Jarak Canberra.
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Gao, Hui-ting, Wei Liu, Hong-yan He, Bing-xian Zhang, and Cheng Jiang. "DE-STRIPING FOR TDICCD REMOTE SENSING IMAGE BASED ON STATISTICAL FEATURES OF HISTOGRAM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 3, 2016): 311–16. http://dx.doi.org/10.5194/isprsarchives-xli-b1-311-2016.
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Aim to striping noise brought by non-uniform response of remote sensing TDI CCD, a novel de-striping method based on statistical features of image histogram is put forward. By analysing the distribution of histograms,the centroid of histogram is selected to be an eigenvalue representing uniformity of ground objects,histogrammic centroid of whole image and each pixels are calculated first,the differences between them are regard as rough correction coefficients, then in order to avoid the sensitivity caused by single parameter and considering the strong continuity and pertinence of ground objects between two adjacent pixels,correlation coefficient of the histograms is introduces to reflect the similarities between them,fine correction coefficient is obtained by searching around the rough correction coefficient,additionally,in view of the influence of bright cloud on histogram,an automatic cloud detection based on multi-feature including grey level,texture,fractal dimension and edge is used to pre-process image.Two 0-level panchromatic images of SJ-9A satellite with obvious strip noise are processed by proposed method to evaluate the performance, results show that the visual quality of images are improved because the strip noise is entirely removed,we quantitatively analyse the result by calculating the non-uniformity ,which has reached about 1% and is better than histogram matching method.
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Gao, Hui-ting, Wei Liu, Hong-yan He, Bing-xian Zhang, and Cheng Jiang. "DE-STRIPING FOR TDICCD REMOTE SENSING IMAGE BASED ON STATISTICAL FEATURES OF HISTOGRAM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 3, 2016): 311–16. http://dx.doi.org/10.5194/isprs-archives-xli-b1-311-2016.
Full textAbstract:
Aim to striping noise brought by non-uniform response of remote sensing TDI CCD, a novel de-striping method based on statistical features of image histogram is put forward. By analysing the distribution of histograms,the centroid of histogram is selected to be an eigenvalue representing uniformity of ground objects,histogrammic centroid of whole image and each pixels are calculated first,the differences between them are regard as rough correction coefficients, then in order to avoid the sensitivity caused by single parameter and considering the strong continuity and pertinence of ground objects between two adjacent pixels,correlation coefficient of the histograms is introduces to reflect the similarities between them,fine correction coefficient is obtained by searching around the rough correction coefficient,additionally,in view of the influence of bright cloud on histogram,an automatic cloud detection based on multi-feature including grey level,texture,fractal dimension and edge is used to pre-process image.Two 0-level panchromatic images of SJ-9A satellite with obvious strip noise are processed by proposed method to evaluate the performance, results show that the visual quality of images are improved because the strip noise is entirely removed,we quantitatively analyse the result by calculating the non-uniformity ,which has reached about 1% and is better than histogram matching method.
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Yousuf, M. A., and M. R. H. Rakib. "An Effective Image Contrast Enhancement Method Using Global Histogram Equalization." Journal of Scientific Research 3, no. 1 (December 19, 2010): 43. http://dx.doi.org/10.3329/jsr.v3i1.5299.
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Image enhancement is one of the most important issues in low-level image processing. Histograms are the basis for numerous spatial domain processing techniques. In this paper, we present a simple and effective method for image contrast enhancement based on global histogram equalization. In this method, at first input image is normalized by making the minimum gray level value to 0. Then the probability of each grey level is calculated from the available ROI grey levels. Finally, histogram equalization is performed on the input image based on the calculated probability density (or distribution) function. As a result, the mean brightness of the input image does not change significantly by the histogram equalization. Additionally, noise is prevented from being greatly amplified. Experimental results on medical images demonstrate that the proposed method can enhance the images effectively. The result is also compared with the result of image enhancement technique using local statistics.Keywords: Histogram equalization; Global histogram equalization; Image enhancement; Local statistics.© 2011 JSR Publications. ISSN: 2070-0237 (Print); 2070-0245 (Online). All rights reserved.doi:10.3329/jsr.v3i1.5299 J. Sci. Res. 3 (1), 43-50 (2011)
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Fushing, Hsieh, and Tania Roy. "Complexity of possibly gapped histogram and analysis of histogram." Royal Society Open Science 5, no. 2 (February 2018): 171026. http://dx.doi.org/10.1098/rsos.171026.
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We demonstrate that gaps and distributional patterns embedded within real-valued measurements are inseparable biological and mechanistic information contents of the system. Such patterns are discovered through data-driven possibly gapped histogram, which further leads to the geometry-based analysis of histogram (ANOHT). Constructing a possibly gapped histogram is a complex problem of statistical mechanics due to the ensemble of candidate histograms being captured by a two-layer Ising model. This construction is also a distinctive problem of Information Theory from the perspective of data compression via uniformity. By defining a Hamiltonian (or energy) as a sum of total coding lengths of boundaries and total decoding errors within bins, this issue of computing the minimum energy macroscopic states is surprisingly resolved by applying the hierarchical clustering algorithm. Thus, a possibly gapped histogram corresponds to a macro-state. And then the first phase of ANOHT is developed for simultaneous comparison of multiple treatments, while the second phase of ANOHT is developed based on classical empirical process theory for a tree-geometry that can check the authenticity of branches of the treatment tree. The well-known Iris data are used to illustrate our technical developments. Also, a large baseball pitching dataset and a heavily right-censored divorce data are analysed to showcase the existential gaps and utilities of ANOHT.
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Gadetska, Svitlana, Valeriy Dubnitskiy, Alexander Khodyrev, and Yuri Kushneruk. "Excel-oriented calculator for calculating results of entropy analysis of data distributed by categories." Advanced Information Systems 7, no. 2 (June 12, 2023): 28–40. http://dx.doi.org/10.20998/2522-9052.2023.2.05.
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The goal of the work. Development of EXCEL-oriented calculator for calculating the results of entropy analysis of data, which are distributed by categories. The subject of research is histograms of arbitrary distribution laws and conjugation tables 2×2. Research methods: Entropy and information analysis of histograms of arbitrary distribution laws and conjugation tables. The obtained results. It is proposed to use methods of entropy analysis for the analysis of data distributed by categories; information on the structure of the EXCEL-oriented calculator designed for this purpose is given. The calculator makes it possible to calculate entropy characteristics of histograms, namely: histogram entropy, histogram dispersion, histogram confidence intervals, diversity information index. The calculator performs a pairwise comparison of entropies of histograms using the Hutcheson method, determines Hellinger and Kullback-Leibler distances between histograms of arbitrary distribution laws and thus complements the chi-square criterion, determines the informational correlation coefficient. The correspondence between the Pearson correlation coefficient and the information correlation coefficient is established by the method of statistical modeling. For 2×2 conjugation tables, the calculator makes it possible to estimate the significance of the interaction between the row factor and the column factor. The calculator determines the values of conditional entropies for 2×2 conjugation tables. The proposed calculator fills the gaps in existing software products and can be used to process data distributed by categories using entropy analysis methods. It is shown that entropy methods of analysis are appropriate to use in cases where histograms determine arbitrary distribution laws.
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Wei, Guodong, Ying Tian, Shun’ichi Kaneko, and Zhengang Jiang. "Robust Template Matching Using Multiple-Layered Absent Color Indexing." Sensors 22, no. 17 (September 3, 2022): 6661. http://dx.doi.org/10.3390/s22176661.
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Color is an essential feature in histogram-based matching. This can be extracted as statistical data during the comparison process. Although the applicability of color features in histogram-based techniques has been proven, position information is lacking during the matching process. We present a conceptually simple and effective method called multiple-layered absent color indexing (ABC-ML) for template matching. Apparent and absent color histograms are obtained from the original color histogram, where the absent colors belong to low-frequency or vacant bins. To determine the color range of compared images, we propose a total color space (TCS) that can determine the operating range of the histogram bins. Furthermore, we invert the absent colors to obtain the properties of these colors using threshold hT. Then, we compute the similarity using the intersection. A multiple-layered structure is proposed against the shift issue in histogram-based approaches. Each layer is constructed using the isotonic principle. Thus, absent color indexing and multiple-layered structure are combined to solve the precision problem. Our experiments on real-world images and open data demonstrated that they have produced state-of-the-art results. Moreover, they retained the histogram merits of robustness in cases of deformation and scaling.
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Scott, David W. "Histogram." WIREs Computational Statistics 2, no. 1 (December 31, 2009): 44–48. http://dx.doi.org/10.1002/wics.59.
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Inamura, Minoru. "Non-Parametric Classification of Remotely Sensed Multispectral Image Data by Means of Matrix Representation of Multidimensional Histograms." Journal of Robotics and Mechatronics 6, no. 1 (February 20, 1994): 42–50. http://dx.doi.org/10.20965/jrm.1994.p0042.
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The computer framing of land use maps using remotely sensed multispectral image data is identical with pattern classification for spectral reflectance of objects on earth's surface. In particular, the classification by the maximum likelihood method is the most popular method because it theoretically gives the highest correct classification rate on the condition that the statistical distribution of the image data be normal. However, the histogram of real image data is not a normal distribution. Actual histograms show the proper distributions to classes. This fact means that a histogram gives a spatial property of the class statistically. This paper described a newly developed non-parametric method by means of the matrix representations of multidimensional histograms and subimages.
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Chen, Sheng, Xianyi Chen, and Huijuan Fu. "General Framework of Reversible Watermarking Based on Asymmetric Histogram Shifting of Prediction Error." Advances in Multimedia 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/6359248.
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This paper presents a general framework for the reversible watermarking based on asymmetric histogram shifting of prediction error, which is inspired by reversible watermarking of prediction error. Different from the conventional algorithms using single-prediction scheme to create symmetric histogram, the proposed method employs a multi-prediction scheme, which calculates multiple prediction values for the pixels. Then, the suitable value would be selected by two dual asymmetric selection functions to construct two asymmetric error histograms. Finally, the watermark is embedded in the two error histograms separately utilizing a complementary embedding strategy. The proposed framework provides a new perspective for the research of reversible watermarking, which brings about many benefits for the information security.
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Jensen, Peter Klint, and Kirsten Hansen. "Deconvolution of fission-track length distributions and its application to dating and separating pre- and post-depositional components." Geochronology 3, no. 2 (December 21, 2021): 561–75. http://dx.doi.org/10.5194/gchron-3-561-2021.
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Abstract. To enable the separation of pre- and post-depositional components of the length distribution of (partially annealed) horizontal confined fission tracks, the length distribution is corrected by deconvolution. Probabilistic least-squares inversion corrects natural track length histograms for observational biases, considering the variance in data, modelization, and prior information. The corrected histogram is validated by its variance–covariance matrix. It is considered that horizontal track data can exist with or without measurements of angles to the c axis. In the latter case, 3D histograms are introduced as an alternative to histograms of c-axis-projected track lengths. Thermal history modelling of samples is not necessary for the calculation of track age distributions of corrected tracks. In an example, the age equations are applied to apatites with pre-depositional (inherited) tracks in order to extract the post-depositional track length histogram. Fission tracks generated before deposition in detrital apatite crystals are mixed with post-depositional tracks. This complicates the calculation of the post-sedimentary thermal history, as the grains have experienced different thermal histories prior to deposition. Thereafter, the grains share a common thermal history. Thus, the extracted post-depositional histogram without inherited tracks may be used for thermal history calculation.
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Papageorgiou, Ismini, Nektarios A. Valous, Stathis Hadjidemetriou, Ulf Teichgräber, and Ansgar Malich. "Quantitative Assessment of Breast-Tumor Stiffness Using Shear-Wave Elastography Histograms." Diagnostics 12, no. 12 (December 13, 2022): 3140. http://dx.doi.org/10.3390/diagnostics12123140.
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Purpose: Shear-wave elastography (SWE) measures tissue elasticity using ultrasound waves. This study proposes a histogram-based SWE analysis to improve breast malignancy detection. Methods: N = 22/32 (patients/tumors) benign and n = 51/64 malignant breast tumors with histological ground truth. Colored SWE heatmaps were adjusted to a 0–180 kPa scale. Normalized, 250-binned RGB histograms were used as image descriptors based on skewness and area under curve (AUC). The histogram method was compared to conventional SWE metrics, such as (1) the qualitative 5-point scale classification and (2) average stiffness (SWEavg)/maximal tumor stiffness (SWEmax) within the tumor B-mode boundaries. Results: The SWEavg and SWEmax did not discriminate malignant lesions in this database, p > 0.05, rank sum test. RGB histograms, however, differed between malignant and benign tumors, p < 0.001, Kolmogorov–Smirnoff test. The AUC analysis of histograms revealed the reduction of soft-tissue components as a significant SWE biomarker (p = 0.03, rank sum). The diagnostic accuracy of the suggested method is still low (Se = 0.30 for Se = 0.90) and a subject for improvement in future studies. Conclusions: Histogram-based SWE quantitation improved the diagnostic accuracy for malignancy compared to conventional average SWE metrics. The sensitivity is a subject for improvement in future studies.
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Guo, Lie, Guang Xi Zhang, Ping Shu Ge, and Lin Hui Li. "Pedestrian Tracking with HOG and Color Histogram Features." Applied Mechanics and Materials 241-244 (December 2012): 498–501. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.498.
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To improve the effectiveness of pedestrian tracking, the histograms of oriented gradients (HOG) and color histogram characteristics are adopted to track pedestrian based on particle filter. Firstly, the pedestrian is detected using the HOG features to determine the initial target position. Then the target is tracked based on particle filter utilizing color histogram, during which the HOG is used to modify particle heavy weights and particle sampling. Experimental results verify the accurateness and efficiency of the proposed method.
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Yu, Yuan Chih, Shing Chern D. You, and Dwen Ren Tsai. "Hill Climbing Algorithm for License Plate Recognition." Advanced Materials Research 267 (June 2011): 995–1000. http://dx.doi.org/10.4028/www.scientific.net/amr.267.995.
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Histogram thresholding has been widely used for image processing—it is simple, fast, and computationally inexpensive. In this paper, we develop a creative approach based on histogram’s distributions to segment interest regions from background. Unlike the existing threshold detection methods which measure the statistics of histogram in the multi-modal images, our approach analyses the shape representation of multi-modal which has several hill-climbing curves. The behavior of algorithm works like human vision which focuses on the high contrast areas and scans the shape variation first. Moreover, such an algorithm presents a new type of histogram analysis that depends on the particular shape of certain distribution in histogram. Experimental results reveal that the proposed algorithm performs distinct effects especially on the recognition of artificial signs such as road sign, vehicle plate, and signboard.
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Elaraby, Ahmed, and Ayman Taha. "An approach for cross-modality guided quality enhancement of liver image." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 2 (April 1, 2022): 1449. http://dx.doi.org/10.11591/ijece.v12i2.pp1449-1455.
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<p><span>A novel approach for multimodal liver image contrast enhancement is put forward in this paper. The proposed approach utilizes magnetic resonance imaging (MRI) scan of liver as a guide to enhance the structures of computed tomography (CT) liver. The enhancement process consists of two phases: The first phase is the transformation of MRI and CT modalities to be in the same range. Then the histogram of CT liver is adjusted to match the histogram of MRI. In the second phase, an adaptive histogram equalization technique is presented by splitting the CT histogram into two sub-histograms and replacing their cumulative distribution functions with two smooths sigmoid. The subjective and objective assessments of experimental results indicated that the proposed approach yields better results. In addition, the image contrast is effectively enhanced as well as the mean brightness and details are well preserved.</span></p>
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Sun, Miao, Shenglong Zhuo, and Patrick Yin Chiang. "Multi-Scale Histogram-Based Probabilistic Deep Neural Network for Super-Resolution 3D LiDAR Imaging." Sensors 23, no. 1 (December 30, 2022): 420. http://dx.doi.org/10.3390/s23010420.
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LiDAR (Light Detection and Ranging) imaging based on SPAD (Single-Photon Avalanche Diode) technology suffers from severe area penalty for large on-chip histogram peak detection circuits required by the high precision of measured depth values. In this work, a probabilistic estimation-based super-resolution neural network for SPAD imaging that firstly uses temporal multi-scale histograms as inputs is proposed. To reduce the area and cost of on-chip histogram computation, only part of the histogram hardware for calculating the reflected photons is implemented on a chip. On account of the distribution rule of returned photons, a probabilistic encoder as a part of the network is first proposed to solve the depth estimation problem of SPADs. By jointly using this neural network with a super-resolution network, 16× up-sampling depth estimation is realized using 32 × 32 multi-scale histogram outputs. Finally, the effectiveness of this neural network was verified in the laboratory with a 32 × 32 SPAD sensor system.
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Revelly, JP, F. Feihl, T. Liebling, and C. Perret. "Time constant histograms from the forced expired volume signal: a clinical evaluation." European Respiratory Journal 2, no. 6 (June 1, 1989): 536–42. http://dx.doi.org/10.1183/09031936.93.02060536.
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We evaluated a multicompartment analysis of forced expiration, based on modelling the lung as a set of twenty parallel compartments emptying exponentially with time constants ranging from 0.1-10 s; the forced expired volume signal was represented by a histogram showing the fraction of forced vital capacity as a function of compartmental time constants. We applied this technique to 80 healthy and 12 asthmatic subjects. The histograms computed from three consecutive forced expirations were poorly reproducible in 18 of the 80 healthy and 2 of the 12 asthmatic subjects. In the asthmatics, the time constant histograms conveyed no additional information on bronchial obstruction, beyond that already present in standard spirometric indices. A simulation study showed a high sensitivity of the histograms to the truncation of the terminal part of forced expiration. We conclude that the usefulness of the time constant histogram technique appears doubtful.
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Potter, Gerald L., George J. Huffman, David T. Bolvin, Michael G. Bosilovich, Judy Hertz, and Laura E. Carriere. "Histogram Anomaly Time Series: A Compact Graphical Representation of Spatial Time Series Data Sets." Bulletin of the American Meteorological Society 101, no. 12 (December 2020): E2133—E2137. http://dx.doi.org/10.1175/bams-d-20-0130.1.
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ABSTRACTWe introduce a simple method for detecting changes, both transient and persistent, in reanalysis and merged satellite products due to both natural climate variability and changes to the data sources/analyses used as input. This note demonstrates this Histogram Anomaly Time Series (HATS) method using tropical ocean daily precipitation from MERRA-2 and from GPCP One-Degree Daily (1DD) precipitation estimates. Rather than averaging over space or time, we create a time series display of histograms for each increment of data (such as a day or month). Regional masks such as land–ocean can be used to isolate particular domains. While the histograms reveal subtle structures in the time series, we can amplify the signal by computing the histogram’s anomalies from its climatological seasonal cycle. The qualitative analysis provided by this scheme can then form the basis for more quantitative analyses of specific features, both real and analysis induced. As an example, in the tropical oceans the analysis clearly identifies changes in the time series of both reanalysis and observations that may be related to changing inputs.
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Zheng, Yan, Baolong Guo, Cheng Li, and Yunyi Yan. "A Weighted Fourier and Wavelet-Like Shape Descriptor Based on IDSC for Object Recognition." Symmetry 11, no. 5 (May 20, 2019): 693. http://dx.doi.org/10.3390/sym11050693.
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This article presents an effective shape descriptor with a property of fast matching. This descriptor, called IDSC-wFW (a weighted Fourier and wavelet-like descriptor based on inner distance shape context), first rewrites shape histograms of IDSC descriptors, changing the histogram belonging to a point to the histogram belonging to a field, and sets the histogram of a field as a one-dimensional signal, then transforms this one-dimensional signal by using a Fourier transform and a transform similar to Haar wavelet. Finally, the two transform results are linearly combined to form a new descriptor. This new descriptor requires only a distance-based measure method during the matching stage. Experimental results on three well-known databases show that this new descriptor not only obtains accurate retrieval results but also runs fast.
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Alreshidi, Eissa, Rabie A. Ramadan, Md Haidar Sharif, Omer Faruk Ince, and Ibrahim Furkan Ince. "A Comparative Study of Image Descriptors in Recognizing Human Faces Supported by Distributed Platforms." Electronics 10, no. 8 (April 12, 2021): 915. http://dx.doi.org/10.3390/electronics10080915.
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Face recognition is one of the emergent technologies that has been used in many applications. It is a process of labeling pictures, especially those with human faces. One of the critical applications of face recognition is security monitoring, where captured images are compared to thousands, or even millions, of stored images. The problem occurs when different types of noise manipulate the captured images. This paper contributes to the body of knowledge by proposing an innovative framework for face recognition based on various descriptors, including the following: Color and Edge Directivity Descriptor (CEDD), Fuzzy Color and Texture Histogram Descriptor (FCTH), Color Histogram, Color Layout, Edge Histogram, Gabor, Hashing CEDD, Joint Composite Descriptor (JCD), Joint Histogram, Luminance Layout, Opponent Histogram, Pyramid of Gradient Histograms Descriptor (PHOG), Tamura. The proposed framework considers image set indexing and retrieval phases with multi-feature descriptors. The examined dataset contains 23,707 images of different genders and ages, ranging from 1 to 116 years old. The framework is extensively examined with different image filters such as random noise, rotation, cropping, glow, inversion, and grayscale. The indexer’s performance is measured based on a distributed environment based on sample size and multiprocessors as well as multithreads. Moreover, image retrieval performance is measured using three criteria: rank, score, and accuracy. The implemented framework was able to recognize the manipulated images using different descriptors with a high accuracy rate. The proposed innovative framework proves that image descriptors could be efficient in face recognition even with noise added to the images based on the outcomes. The concluded results are as follows: (a) the Edge Histogram could be best used with glow, gray, and inverted images; (b) the FCTH, Color Histogram, Color Layout, and Joint Histogram could be best used with cropped images; and (c) the CEDD could be best used with random noise and rotated images.
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Essa, Almabrok, and Vijayan K. Asari. "Histogram of Oriented Directional Features for Robust Face Recognition." International Journal of Monitoring and Surveillance Technologies Research 4, no. 3 (July 2016): 35–51. http://dx.doi.org/10.4018/ijmstr.2016070103.
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This paper presents an illumination invariant face recognition system that uses directional features and modular histogram. The proposed Histogram of Oriented Directional Features (HODF) produces multi-region histograms for each face image, then concatenates these histograms to form the final feature vector. This feature vector is used to recognize the face image by the help of k nearest neighbors classifier (KNN). The edge responses and the relationship among pixels are very important and play the main role for improving the face recognition accuracy. Therefore, this work presents the effectiveness of using different directional masks for detecting the edge responses on face recognition accuracy, such as Prewitt kernels, Kirsch masks, Sobel kernels, and Gaussian derivative masks. The performance evaluation of the proposed HODF algorithm is conducted on several publicly available databases and observed promising recognition rates.
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Park, Namuk, and Songkuk Kim. "FlexSketch: Estimation of Probability Density for Stationary and Non-Stationary Data Streams." Sensors 21, no. 4 (February 4, 2021): 1080. http://dx.doi.org/10.3390/s21041080.
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Efficient and accurate estimation of the probability distribution of a data stream is an important problem in many sensor systems. It is especially challenging when the data stream is non-stationary, i.e., its probability distribution changes over time. Statistical models for non-stationary data streams demand agile adaptation for concept drift while tolerating temporal fluctuations. To this end, a statistical model needs to forget old data samples and to detect concept drift swiftly. In this paper, we propose FlexSketch, an online probability density estimation algorithm for data streams. Our algorithm uses an ensemble of histograms, each of which represents a different length of data history. FlexSketch updates each histogram for a new data sample and generates probability distribution by combining the ensemble of histograms while monitoring discrepancy between recent data and existing models periodically. When it detects concept drift, a new histogram is added to the ensemble and the oldest histogram is removed. This allows us to estimate the probability density function with high update speed and high accuracy using only limited memory. Experimental results demonstrate that our algorithm shows improved speed and accuracy compared to existing methods for both stationary and non-stationary data streams.
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Kiadtikornthaweeyot, Warinthorn, and Adrian R. L. Tatnall. "REGION OF INTEREST DETECTION BASED ON HISTOGRAM SEGMENTATION FOR SATELLITE IMAGE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 21, 2016): 249–55. http://dx.doi.org/10.5194/isprs-archives-xli-b7-249-2016.
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High resolution satellite imaging is considered as the outstanding applicant to extract the Earth’s surface information. Extraction of a feature of an image is very difficult due to having to find the appropriate image segmentation techniques and combine different methods to detect the Region of Interest (ROI) most effectively. This paper proposes techniques to classify objects in the satellite image by using image processing methods on high-resolution satellite images. The systems to identify the ROI focus on forests, urban and agriculture areas. The proposed system is based on histograms of the image to classify objects using thresholding. The thresholding is performed by considering the behaviour of the histogram mapping to a particular region in the satellite image. The proposed model is based on histogram segmentation and morphology techniques. There are five main steps supporting each other; Histogram classification, Histogram segmentation, Morphological dilation, Morphological fill image area and holes and ROI management. The methods to detect the ROI of the satellite images based on histogram classification have been studied, implemented and tested. The algorithm is be able to detect the area of forests, urban and agriculture separately. The image segmentation methods can detect the ROI and reduce the size of the original image by discarding the unnecessary parts.
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Kiadtikornthaweeyot, Warinthorn, and Adrian R. L. Tatnall. "REGION OF INTEREST DETECTION BASED ON HISTOGRAM SEGMENTATION FOR SATELLITE IMAGE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 21, 2016): 249–55. http://dx.doi.org/10.5194/isprsarchives-xli-b7-249-2016.
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High resolution satellite imaging is considered as the outstanding applicant to extract the Earth’s surface information. Extraction of a feature of an image is very difficult due to having to find the appropriate image segmentation techniques and combine different methods to detect the Region of Interest (ROI) most effectively. This paper proposes techniques to classify objects in the satellite image by using image processing methods on high-resolution satellite images. The systems to identify the ROI focus on forests, urban and agriculture areas. The proposed system is based on histograms of the image to classify objects using thresholding. The thresholding is performed by considering the behaviour of the histogram mapping to a particular region in the satellite image. The proposed model is based on histogram segmentation and morphology techniques. There are five main steps supporting each other; Histogram classification, Histogram segmentation, Morphological dilation, Morphological fill image area and holes and ROI management. The methods to detect the ROI of the satellite images based on histogram classification have been studied, implemented and tested. The algorithm is be able to detect the area of forests, urban and agriculture separately. The image segmentation methods can detect the ROI and reduce the size of the original image by discarding the unnecessary parts.
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Buhmeida, A., A. Ålgars, R. Ristamäki, Y. Collan, K. Syrjänen, and S. Pyrhönen. "DNA Image cytometry as a prognostic tool in stage II and stage III colorectal cancer." Journal of Clinical Oncology 24, no. 18_suppl (June 20, 2006): 13565. http://dx.doi.org/10.1200/jco.2006.24.18_suppl.13565.
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13565 Background: We assessed the prognostic value of nuclear DNA content measured in the primary tumors of 123 patients with stage II or stage III colorectal cancer (CRC). Methods: Isolated nuclei from paraffin sections were stained with Feulgen and DNA was measured using a computer-assisted image analysis cytometry system (Ahrens ACAS). We applied 4 different approaches in analysis of DNA histograms: ABCDE approach, histogram range, peak evaluation, and DNA cut-off values. Results: Using the histogram range, narrow range was rare (3.7%) in patients who died of disease as compared with 16.4% among those alive (p=0.017). Modal peak evaluation was a significant predictor of disease free survival (DFS) (Kaplan-Meier log-rank p=0.0235). In the range evaluation, the first set (low-start gates) was a significant predictor of DFS (log-rank p=0.0121), where disease recurrence was closely associated the widest range (1.8c->10c) gates. Recurrence-free survival was markedly better among patients with narrow gate histograms than wide range histograms than among patients with wide range histograms (p<0.03). The first set also proved to be significant predictor of disease specific survival (DSS) (log-rank p=0.0045), being markedly better (78–90.0%) among the patients with the narrow-gate histograms. Grading of the histogram range into two categories (with 6.0c as cut-off for low and wide range), was a powerful predictor of both DSS (log-rank p= 0.0092) and 5-year DFS (p=0.0106) in the whole series, and separately in Stage III (but not Stage II) disease; p=0.0131 and p=0.0201, respectively. Conclusions: The DNA image cytometry with careful analysis of the histograms may provide valuable prognostic information in CRC, with potential clinical implications in patient management, particularly in predicting the patients at high risk for recurrence who should be considered as candidates for adjuvant therapy. No significant financial relationships to disclose.
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Mehmood, Zahid, Syed Muhammad Anwar, Nouman Ali, Hafiz Adnan Habib, and Muhammad Rashid. "A Novel Image Retrieval Based on a Combination of Local and Global Histograms of Visual Words." Mathematical Problems in Engineering 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/8217250.
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Content-based image retrieval (CBIR) provides a sustainable solution to retrieve similar images from an image archive. In the last few years, the Bag-of-Visual-Words (BoVW) model gained attention and significantly improved the performance of image retrieval. In the standard BoVW model, an image is represented as an orderless global histogram of visual words by ignoring the spatial layout. The spatial layout of an image carries significant information that can enhance the performance of CBIR. In this paper, we are presenting a novel image representation that is based on a combination of local and global histograms of visual words. The global histogram of visual words is constructed over the whole image, while the local histogram of visual words is constructed over the local rectangular region of the image. The local histogram contains the spatial information about the salient objects. Extensive experiments and comparisons conducted on Corel-A, Caltech-256, and Ground Truth image datasets demonstrate that the proposed image representation increases the performance of image retrieval.
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Robitaille, Nicolas, Abderazzak Mouiha, Burt Crépeault, Fernando Valdivia, and Simon Duchesne. "Tissue-Based MRI Intensity Standardization: Application to Multicentric Datasets." International Journal of Biomedical Imaging 2012 (2012): 1–11. http://dx.doi.org/10.1155/2012/347120.
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Intensity standardization in MRI aims at correcting scanner-dependent intensity variations. Existing simple and robust techniques aim at matching the input image histogram onto a standard, while we think that standardization should aim at matching spatially corresponding tissue intensities. In this study, we present a novel automatic technique, called STI forSTandardization of Intensities, which not only shares the simplicity and robustness of histogram-matching techniques, but also incorporates tissue spatial intensity information. STI uses joint intensity histograms to determine intensity correspondence in each tissue between the input and standard images. We compared STI to an existing histogram-matching technique on two multicentric datasets, Pilot E-ADNI and ADNI, by measuring the intensity error with respect to the standard image after performing nonlinear registration. The Pilot E-ADNI dataset consisted in 3 subjects each scanned in 7 different sites. The ADNI dataset consisted in 795 subjects scanned in more than 50 different sites. STI was superior to the histogram-matching technique, showing significantly better intensity matching for the brain white matter with respect to the standard image.
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Zhang, Guodong, Peilin Jiang, Kazuyuki Matsumoto, Minoru Yoshida, and Kenji Kita. "Reidentification of Persons Using Clothing Features in Real-Life Video." Applied Computational Intelligence and Soft Computing 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/5834846.
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Person reidentification, which aims to track people across nonoverlapping cameras, is a fundamental task in automated video processing. Moving people often appear differently when viewed from different nonoverlapping cameras because of differences in illumination, pose, and camera properties. The color histogram is a global feature of an object that can be used for identification. This histogram describes the distribution of all colors on the object. However, the use of color histograms has two disadvantages. First, colors change differently under different lighting and at different angles. Second, traditional color histograms lack spatial information. We used a perception-based color space to solve the illumination problem of traditional histograms. We also used the spatial pyramid matching (SPM) model to improve the image spatial information in color histograms. Finally, we used the Gaussian mixture model (GMM) to show features for person reidentification, because the main color feature of GMM is more adaptable for scene changes, and improve the stability of the retrieved results for different color spaces in various scenes. Through a series of experiments, we found the relationships of different features that impact person reidentification.
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Stolk, Ad, Torbjörn E. Törnqvist, Kilian P. V. Hekhuis, Henk J. A. Berendsen, and Johannes van der Plicht. "Calibration of 14C Histograms: A Comparison of Methods." Radiocarbon 36, no. 1 (1994): 1–10. http://dx.doi.org/10.1017/s0033822200014272.
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The interpretation of 14C histograms is complicated by the non-linearity of the 14C time scale in terms of calendar years, which may result in clustering of 14C ages in certain time intervals unrelated to the (geologic or archaeologic) phenomenon of interest. One can calibrate 14C histograms for such distortions using two basic approaches. The KORHIS method constructs a 14C histogram before calibration is performed by means of a correction factor. We present the CALHIS method based on the Groningen calibration program for individual 14C ages. CALHIS first calibrates single 14C ages and then sums the resulting calibration distributions, thus yielding a calibrated 14C histogram. The individual calibration distributions are normalized to a standard Gaussian distribution before superposition, thus allowing direct comparison among various 14C histograms. Several experiments with test data sets demonstrate that CALHIS produces significantly better results than KORHIS. Although some problems remain (part of the distortions due to 14C variations cannot be eliminated), we show that CALHIS offers good prospects for using 14C histograms, particularly with highly precise and accurate 14C ages.
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Tjahyanto, Aris, Ano Rangga Rahardika, and Ary Mazharuddin Shiddiqi. "An Enhanced Dynamic Signature Verification using the X and Y Histogram Features." Infotekmesin 12, no. 2 (August 24, 2021): 155–59. http://dx.doi.org/10.35970/infotekmesin.v12i2.668.
Full textAbstract:
Dynamic signature verification by using histogram features is a well-known signature forgery detection technique due to its high performance. However, this technique is often limited to angular histograms derived from vectors containing two adjacent points. We propose additional new features from the X and Y histograms to overcome the limitation. Our experiments indicate that our technique produced Under Curve Area AUC values 0.80 to detect skilled forgery and 0.91 for random forgery. Our method performed best when the verification system uses 12 of the most dominant features. This setup produced AUC values of 0.80 to detect skilled forgery and 0.93 for random forgery. These results outperformed the original technique when the X and Y histogram features are not used that produced AUC values of 0.78 to detect skilled forgery and 0.90 for random forgery.