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Letteratura scientifica selezionata sul tema "Histogram"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 9 giugno 2025

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Articoli di riviste sul tema "Histogram"

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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Tesi sul tema "Histogram"

Kvapil, Jiří. "Adaptivní ekvalizace histogramu digitálních obrazů." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2009. http://www.nusl.cz/ntk/nusl-228687.

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The diploma thesis is focused on histogram equalization method and his extension by the adaptive boundary. This thesis contains explanations of basic notions on that histogram equalization method was created. Next part is described the human vision and priciples of his imitation. In practical part of this thesis was created software that makes it possible to use methods of adaptive histogram equalization on real images. At the end is showed some results that was reached.

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Kurak, Charles W. Jr. "Adaptive Histogram Equalization, a Parallel Implementation." UNF Digital Commons, 1990. http://digitalcommons.unf.edu/etd/260.

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Adaptive Histogram Equalization (AHE) has been recognized as a valid method of contrast enhancement. The main advantage of AHE is that it can provide better contrast in local areas than that achievable utilizing traditional histogram equalization methods. Whereas traditional methods consider the entire image, AHE utilizes a local contextual region. However, AHE is computationally expensive, and therefore time-consuming. In this work two areas of computer science, image processing and parallel processing, are combined to produce an efficient algorithm. In particular, the AHE algorithm is implemented with a Multiple-Instruction-Multiple-Data (MIMD) parallel architecture. It is proposed that, as MIMD machines become more powerful and prevalent, this methodology can be applied to not only this particular algorithm, but also to many others in its class.

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Jirka, Roman. "Časosběrné video." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2011. http://www.nusl.cz/ntk/nusl-236934.

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This thesis deals with the introduction into the topic of time-lapse video creation. It focuses on cases where tripod is not used and therefore it is necessary to eliminate incurred shortcomings. The main shortcomings are different position of individual frames, different brightness and color adjustment. The next topic describes which principles should be followed during the creation process. Thesis describes and implements methods for elimination of main shortcomings during process long time-lapse videos, which are recorded by hand. Thesis also precisely describes image registration, correction of brightness and colors. Thesis is also considers histograms comparison. Result of this work is application, which eliminates problems described above.

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Müller, Patrice. "Scalable localized histogram aggregation for P2P MMOGs." Zürich : ETH, Eidgenössische Technische Hochschule Zürich, 2005. http://e-collection.ethbib.ethz.ch/show?type=dipl&nr=169.

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SKARPMAN, MUNTER JOHANNA. "Dose-Volume Histogram Prediction using KernelDensity Estimation." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-155893.

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Dose plans developed for stereotactic radiosurgery are assessed by studying so called Dose-Volume Histograms. Since it is hard to compare an individual dose plan with doseplans created for other patients, much experience and knowledge is lost. This thesis therefore investigates a machine learning approach to predicting such Dose-Volume Histograms for a new patient, by learning from previous dose plans.The training set is chosen based on similarity in terms of tumour size. The signed distances between voxels in the considered volume and the tumour boundary decide the probability of receiving a certain dose in the volume. By using a method based on Kernel Density Estimation, the intrinsic probabilistic properties of a Dose-Volume Histogramare exploited.Dose-Volume Histograms for the brainstem of 22 Acoustic Schwannoma patients, treated with the Gamma Knife,have been predicted, solely based on each patient’s individual anatomical disposition. The method has proved higher prediction accuracy than a “quick-and-dirty” approach implemented for comparison. Analysis of the bias and variance of the method also indicate that it captures the main underlying factors behind individual variations. However,the degree of variability in dose planning results for the Gamma Knife has turned out to be very limited. Therefore, the usefulness of a data driven dose planning tool for the Gamma Knife has to be further investigated.

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Yakoubian, Jeffrey Scott. "Adaptive histogram equalization for mammographic image processing." Thesis, Georgia Institute of Technology, 1993. http://hdl.handle.net/1853/16387.

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Potgieter, Andrew. "A Parallel Multidimensional Weighted Histogram Analysis Method." Thesis, University of Cape Town, 2014. http://pubs.cs.uct.ac.za/archive/00000986/.

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The Weighted Histogram Analysis Method (WHAM) is a technique used to calculate free energy from molecular simulation data. WHAM recombines biased distributions of samples from multiple Umbrella Sampling simulations to yield an estimate of the global unbiased distribution. The WHAM algorithm iterates two coupled, non-linear, equations, until convergence at an acceptable level of accuracy. The equations have quadratic time complexity for a single reaction coordinate. However, this increases exponentially with the number of reaction coordinates under investigation, which makes multidimensional WHAM a computationally expensive procedure. There is potential to use general purpose graphics processing units (GPGPU) to accelerate the execution of the algorithm. Here we develop and evaluate a multidimensional GPGPU WHAM implementation to investigate the potential speed-up attained over its CPU counterpart. In addition, to avoid the cost of multiple Molecular Dynamics simulations and for validation of the implementations we develop a test system to generate samples analogous to Umbrella Sampling simulations. We observe a maximum problem size dependent speed-up of approximately 19 for the GPGPU optimized WHAM implementation over our single threaded CPU optimized version. We find that the WHAM algorithm is amenable to GPU acceleration, which provides the means to study ever more complex molecular systems in reduced time periods.

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Thapa, Mandira. "Optimal Feature Selection for Spatial Histogram Classifiers." Wright State University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=wright1513710294627304.

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Li, Yang. "Face Recognition Based on Histogram And Spin Image." Thesis, University of York, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.485831.

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This thesis presents our research work on shape-based human face recognition exploiting curvature-based histogram and spin image. Instead of the popular 2D shape information represented by fiducial points, the novelty here is the use of 2.5D shape information obtained by shape-from-shading (SFS). Though surface normals generated by performing shape-from-shading on objects are not widely accepted as a precise shape representation for face recognition purposes, recent research in shape-from-shading [Ragheb and Hancock, 2003] [prados et aI., 2006] [Castelan, 2006] [Smith and Hancock, 2006] has made it possible to recover fairly accurate shape information under various conditions from face images in the real world. These contributions make our face recognition approaches based on 2.5D shape recovered from a single image possi.ble. Chapter 2 is a thorough review of the existing literature in the areas pf surface reconstruction using shape-from-shading, appearance-based and model-based face recognition on 2D and 3D data, and histogram-based image representation and recognition. The literature is pretty sparse on works on shape-from-shading in the face recognition area, however there are plenty of approaches based on 2D images ~ and 3D range data. With accurate height map being recovered from single face image [Prados et aI., 2006] [Castelan, 2006] and statistical model being proposed to recover surface normals [Smith and Hancock, 2006], we have enough 2.5D shape infonnation recovered' from 2D image based on which we can perfonn face recognition. In Chapter 3, we present our curvature-based histogram appro,ach as our first contribution, which employs principal curvature infonnation calculated from the Hessian matrix based on the recovered surface nonnals. Generalized entropies are introduced as similarity measurements, which give stable performance especially when the number of relative images varies. While curvature-based histogram proves to be a fairly good face recognition approach with concise representation and easy comparison, its performance is not always stable when applied to different databases. Therefore more advanced facerecognition approach is required to bring better and more stable identification results. In Chapter 4, we derive patch-based spin image as a local shape representation from the idea of global curvature-based histogram in Chapter 3. This representation is inspired by [Johnson and Hebert, 1999] and adapted in this thesis as a solution to face recognition problem. Instead of using 3D range data, the estimated height map reconstructed by shape-from-shading is employed in the spin image construction. Also the mean needle map model is used as the preprocessing to correct the errors and noise that exists in the surface nonnal estimates. Face surface is segmented into small patches and the spin image corresponding to the surface is composed of histograms constructed on each surface patch. In Appendix A, we propose dual spin image to address the difficulties of recognizing face in rotation, among which the two major problems of point correspondence and point occlusion are of particular importance. Therefore we propose the idea of neighbour area spin image to construct the pointwise neighbourhood surface feature collection and use the ratio of projected distances to relative angles to alleviate the errors introduced by surface rotation. We also present the linear model and the finite Gaussian mixture model to approximate novel dual spin image using the existing dual spin images. Face recognition is performed based on the model parameters. .The work in this thesis suggests that 2.5D shape feature recovered by shapefrom- shading can be used for the purpose offace recognition. Also the appearancebased approaches derived from histogram are effective for face recognition. The result suggests that shape information recovered from single image is sufficient for face recognition based on the condition that shape-from:-shading can successfully recover the surface normal fields and the height map from the image.

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Gomes, David Menotti. "Contrast enhancement in digital imaging using histogram equalization." Phd thesis, Université Paris-Est, 2008. http://tel.archives-ouvertes.fr/tel-00470545.

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Nowadays devices are able to capture and process images from complex surveillance monitoring systems or from simple mobile phones. In certain applications, the time necessary to process the image is not as important as the quality of the processed images (e.g., medical imaging), but in other cases the quality can be sacrificed in favour of time. This thesis focuses on the latter case, and proposes two methodologies for fast image contrast enhancement methods. The proposed methods are based on histogram equalization (HE), and some for handling gray-level images and others for handling color images As far as HE methods for gray-level images are concerned, current methods tend to change the mean brightness of the image to the middle level of the gray-level range. This is not desirable in the case of image contrast enhancement for consumer electronics products, where preserving the input brightness of the image is required to avoid the generation of non-existing artifacts in the output image. To overcome this drawback, Bi-histogram equalization methods for both preserving the brightness and contrast enhancement have been proposed. Although these methods preserve the input brightness on the output image with a significant contrast enhancement, they may produce images which do not look as natural as the ones which have been input. In order to overcome this drawback, we propose a technique called Multi-HE, which consists of decomposing the input image into several sub-images, and then applying the classical HE process to each one of them. This methodology performs a less intensive image contrast enhancement, in a way that the output image presented looks more natural. We propose two discrepancy functions for image decomposition which lead to two new Multi-HE methods. A cost function is also used for automatically deciding in how many sub-images the input image will be decomposed on. Experimental results show that our methods are better in preserving the brightness and producing more natural looking images than the other HE methods. In order to deal with contrast enhancement in color images, we introduce a generic fast hue-preserving histogram equalization method based on the RGB color space, and two instances of the proposed generic method. The first instance uses R-red, G-green, and Bblue 1D histograms to estimate a RGB 3D histogram to be equalized, whereas the second instance uses RG, RB, and GB 2D histograms. Histogram equalization is performed using 7 Abstract 8 shift hue-preserving transformations, avoiding the appearance of unrealistic colors. Our methods have linear time and space complexities with respect to the image dimension, and do not require conversions between color spaces in order to perform image contrast enhancement. Objective assessments comparing our methods and others are performed using a contrast measure and color image quality measures, where the quality is established as a weighed function of the naturalness and colorfulness indexes. This is the first work to evaluate histogram equalization methods with a well-known database of 300 images (one dataset from the University of Berkeley) by using measures such as naturalness and colorfulness. Experimental results show that the value of the image contrast produced by our methods is in average 50% greater than the original image value, and still keeping the quality of the output images close to the original

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Libri sul tema "Histogram"

Kirk, Andy. Histogram. 1 Oliver’s Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2016. http://dx.doi.org/10.4135/9781529775877.

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Hildén, Jonatan. Learn to Create a Histogram in Python With Data From Eurostat (2019). 1 Oliver’s Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2021. http://dx.doi.org/10.4135/9781529774153.

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Elmabrouk, Ahmed M. Edge extraction using local histogram analysis and its application to image compression. Leicester: De Montfort University, 1999.

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DeLombard, Richard. Comparison tools for assessing the microgravity environment of orbital missions, carriers and conditions. [Cleveland, Ohio]: National Aeronautics and Space Administration, Glenn Research Center, 1999.

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Boscán, Guillermo, and Nerea González-García. Learn to Create Histograms in RStudio With COVID-19 Data (2020). 1 Oliver’s Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2021. http://dx.doi.org/10.4135/9781529780550.

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Wiesen, Christopher. Learn About Histograms in Stata With the Cardiac Catheterization Diagnostic Dataset (2018). 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2019. http://dx.doi.org/10.4135/9781526498731.

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Welch, Ronald M. The effects of cloud inhomogeneities upon radiative fluxes, and the supply of a cloud truth validation dataset: Semi-annual progress report, period: January-June 1996. [Washington, DC: National Aeronautics and Space Administration, 1996.

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United States. National Aeronautics and Space Administration., ed. The effects of cloud inhomogeneities upon radiative fluxes, and the supply of a cloud truth validation dataset. [Washington, DC: National Aeronautics and Space Administration, 1994.

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United States. National Aeronautics and Space Administration., ed. The Effects of cloud inhomogeneities upon radiative fluxes, and the supply of a cloud truth validation dataset. [Washington, DC: National Aeronautics and Space Administration, 1992.

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King, Harley D. Analytical results, sample locality map, geochemical maps, and histograms for samples from the Stibnite mill tailings pond, Stibnite, Valley County, Idaho. [Reston, Va.]: U.S. Dept. of the Interior, U.S. Geological Survey, 1994.

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Capitoli di libri sul tema "Histogram"

Gooch, Jan W. "Histogram." In Encyclopedic Dictionary of Polymers, 983. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-6247-8_15250.

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Zhang, Qing. "Histogram." In Encyclopedia of Database Systems, 1–2. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4899-7993-3_544-2.

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Härdle, Wolfgang, Axel Werwatz, Marlene Müller, and Stefan Sperlich. "Histogram." In Springer Series in Statistics, 21–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-17146-8_2.

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Zhang, Qing. "Histogram." In Encyclopedia of Database Systems, 1314–15. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_544.

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Holcomb, Zealure C., and Keith S. Cox. "Histogram." In Interpreting Basic Statistics, 53–55. Eighth edition. | New York, NY : Routledge, 2018.: Routledge, 2017. http://dx.doi.org/10.4324/9781315225647-19.

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Schwabish, Jonathan. "Histogram." In Data Visualization in Excel, 257–74. New York: A K Peters/CRC Press, 2023. http://dx.doi.org/10.1201/9781003321552-26.

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Wu, Ying Nian. "Histogram." In Computer Vision, 361–62. Boston, MA: Springer US, 2014. http://dx.doi.org/10.1007/978-0-387-31439-6_742.

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Cox, Keith S., and Zealure C. Holcomb. "Histogram." In Interpreting Basic Statistics, 57–59. 9th ed. New York: Routledge, 2021. http://dx.doi.org/10.4324/9781003096764-19.

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Wu, Ying Nian. "Histogram." In Computer Vision, 572–73. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-63416-2_742.

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Zhang, Qing. "Histogram." In Encyclopedia of Database Systems, 1714–15. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_544.

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Atti di convegni sul tema "Histogram"

Schreiner, Henry, Hans Dembinski, Shuo Liu, and Jim Pivarski. "Boost-histogram: High-Performance Histograms as Objects." In Python in Science Conference. SciPy, 2020. http://dx.doi.org/10.25080/majora-342d178e-009.

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McLauchlan, Philip, and John Mayhew. "Needles: A Stereo Algorithm for Texture." In Image Understanding and Machine Vision. Washington, D.C.: Optica Publishing Group, 1989. http://dx.doi.org/10.1364/iumv.1989.tud1.

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This paper describes Needles, an edge based stereo algorithm designed to take advantage of the smoothness of many textured surfaces. The correspondence problem is not addressed explicitly. Rather, a simple two stage process extracts surface position and orientation directly. Firstly local disparity histograms over a large range are constructed. Maxima in the histograms correspond to the possible surface depths. A Hough transform is used to fit a plane to the ambiguous disparity points close to the histogram maxima. This confirms and makes more precise the estimates of disparity obtained from the histograms. Local surface disparity and orientation are calculated from the best planar fit after all the histogram maxima (above a threshold) have been tried. This is an extension of an algorithm described in (Pollard 1985) which uses a Hough transform to find local surface orientation without explicit matching. In his algorithm pairs of possible matches vote for the disparity gradient between them. When all pairs have voted the winning disparity gradient (and hence, surface orientation) has the highest Hough accumulator value.

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Zweig, David A., and C. J. Morgan. "Photon-limited Scene Matching Using Histogram Analysis." In Quantum-Limited Imaging and Image Processing. Washington, D.C.: Optica Publishing Group, 1986. http://dx.doi.org/10.1364/qlip.1986.tud3.

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Owing to advances in photon detection, there is a developing interest in identifying objects from a limited number of photons. Such applications include astronomy, night vision, long-range target recognition and medical and industrial radiology. In each of these applications it is desirable to extract the maximum amount of information from a limited number of photons. In this paper, we propose a method for object recognition based on the formation of irradiance level histograms for a group of known objects. The technique is used to construct a binary histogram which requires fewer photons than a correlation filter to identify a particular object.

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Kim, Soomin, JongHwan Oh, and Joonhwan Lee. "Histogram." In the 29th Annual Symposium. New York, New York, USA: ACM Press, 2016. http://dx.doi.org/10.1145/2984751.2984759.

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Grundland, Mark, and Neil A. Dodgson. "Color histogram specification by histogram warping." In Electronic Imaging 2005, edited by Reiner Eschbach and Gabriel G. Marcu. SPIE, 2005. http://dx.doi.org/10.1117/12.596953.

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Consortini, A., and F. Cochetti. "Noise deconvolution from probability density in laser atmospheric scintillation." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1991. http://dx.doi.org/10.1364/oam.1991.wk2.

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A deconvolution procedure to remove large uncorrelated noise is applied to probability density histograms of intensity fluctuations of a laser beam after propagating a 1200-m path through atmospheric turbulence. The data were collected by very small (0.04 mm2) apertures at NOAA/ERL/WPL during an experiment not requiring noise resolved measurements at small irradiance.1 Each signal histogram contains data from a 64-s total signal (laser, background and noise) measurement. Each so-called noise histogram contains data obtained in the subsequent 3.2-s with the laser screened off; it therefore includes noise and background. The noise was independent of the laser signal.1

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Yao, Jie, Harrison H. Barrett, and Jannick P. Rolland. "Effect of higher-order statistics of images on signal detection performance of human observers." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1991. http://dx.doi.org/10.1364/oam.1991.thaa3.

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Recent work has shown that a linear discriminant model derived from the work of Harold Hotelling can account for a significant body of psychophysical data.1-3 This model utilizes only the first- and second-order statistics of the image and is insensitive to the shape of the grey level histogram. A natural question is whether the human observer is also insensitive to the shape of the histogram or whether human observer performance is sensitive to higher-order statistics in a image. To answer this question, a psychophysical study was conducted. The images viewed by human observers were simulated ones with inhomogeneous, random backgrounds, and Poisson noise. The mean, variance, and autocorrelation function of the images were controlled to be the same for all images. The grey level histograms of half of the images were designed to be distinctly non-Gaussian, while the other half had Gaussian histograms. Thus the first- and second-order statistics were constant, and only higher-orders were variable. Our results indicate that human detectability is independent of the shape of the grey level histograms, and that the Hotelling observer remains a good predictor of human performance in spite of this variability.

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Liu, Hui-Dong, and Ming Yang. "Local histogram specification using learned histograms for face recognition." In 2012 19th IEEE International Conference on Image Processing (ICIP 2012). IEEE, 2012. http://dx.doi.org/10.1109/icip.2012.6466930.

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Gautam, Krishna Swaroop. "Parallel Histogram Calculation for FPGA: Histogram Calculation." In 2016 IEEE 6th International Conference on Advanced Computing (IACC). IEEE, 2016. http://dx.doi.org/10.1109/iacc.2016.148.

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Bingbing Ni, Shuicheng Yan, and A. Kassim. "Contextualizing histogram." In 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2009. http://dx.doi.org/10.1109/cvprw.2009.5206856.

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Rapporti di organizzazioni sul tema "Histogram"

Stricker, Markus A., and Michael J. Swain. The Capacity of Color Histogram Indexing. Fort Belvoir, VA: Defense Technical Information Center, January 1993. http://dx.doi.org/10.21236/ada279031.

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Maierhafer, Daniel, John Polack, Peter Marleau, Steven Hammon, Rachel Helguero, and Christian Geyer. Open Radiation Monitoring: Histogram Builder Module Design . Office of Scientific and Technical Information (OSTI), May 2021. http://dx.doi.org/10.2172/1808743.

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Cihlar, J., G. Okouneva, J. Beaubien, and R. Latifovic. A new histogram quantization algorithm for land cover classification. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2001. http://dx.doi.org/10.4095/219323.

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Ainsleigh, Phillip L. A Tutorial on EM-Based Density Estimation with Histogram Intensity Data. Fort Belvoir, VA: Defense Technical Information Center, June 2009. http://dx.doi.org/10.21236/ada505302.

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Zhao, L. C., P. R. Krishnaiah, and X. R. Chen. Almost Sure L(Gamma)-Norm Convergence for Data-Based Histogram Density Estimates. Fort Belvoir, VA: Defense Technical Information Center, August 1987. http://dx.doi.org/10.21236/ada189944.

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Chen, X. R., and L. C. Zhao. Almost Sure L(1)-Norm Convergence for Data-Based Histogram Density Estimates. Fort Belvoir, VA: Defense Technical Information Center, March 1986. http://dx.doi.org/10.21236/ada170059.

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Boyarski, A. HANDYPAK: a histogram and display package (release 6. 5). [In Fortran for IBM 370 and VAX/VMS]. Office of Scientific and Technical Information (OSTI), September 1988. http://dx.doi.org/10.2172/6561664.

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Chen, X. R., and L. C. Zhoa. Necessary and Sufficient Conditions for the Convergence of Integrated and Mean-Integrated r-th Order Error of Histogram Density Estimates. Fort Belvoir, VA: Defense Technical Information Center, April 1987. http://dx.doi.org/10.21236/ada186037.

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Paulter Jr., Nicholas G. Comparison of the measurement uncertainties and errors for the waveform state levels estimated using the histogram mode and shorth methods. Gaithersburg, MD: National Institute of Standards and Technology, February 2019. http://dx.doi.org/10.6028/nist.tn.2036.

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Emma, Olsson. Kolinlagring med biokol : Att nyttja biokol och hydrokol som kolsänka i östra Mellansverige. Linköping: Linköping University Electronic Press, March 2025. https://doi.org/10.3384/9789180759496.

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Pest inventory of a field is a way of knowing when the thresholds for pest control is reached. It is of increasing interest to use machine learning to automate this process, however, many challenges arise with detection of small insects both in traps and on plants. This thesis investigates the prospects of developing an automatic warning system for notifying a user of when certain pests are detected in a trap. For this, sliding window with histogram of oriented gradients based support vector machine were implemented. Trap detection with neural network models and a check size function were tested for narrowing the detections down to pests of a certain size. The results indicates that with further refinement and more training images this approach might hold potential for fungus gnat and rape beetles. Further, this thesis also investigates detection performance of Mask R-CNN and YOLOv5 on different insects in fields for the purpose of automating the data gathering process. The models showed promise for detection of rape beetles. YOLOv5 also showed promise as a multi-class detector of different insects, where sizes ranged from small rape beetles to larger bumblebees.

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