Relevant bibliographies by topics / Histogram

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Histogram'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Histogram"

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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 (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 p
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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 (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,
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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 (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
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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 (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 exis
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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 fi
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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.
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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 (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
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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 (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 histo
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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 (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 eval
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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 pro
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Dissertations / Theses on the topic "Histogram"

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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 implem
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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, correct
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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
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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 WH
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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 ma
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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 ima
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Books on the topic "Histogram"

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Kirk, Andy. Histogram. 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). 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. De Montfort University, 1999.

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DeLombard, Richard. Comparison tools for assessing the microgravity environment of orbital missions, carriers and conditions. 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). 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). 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. 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. 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. 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. U.S. Dept. of the Interior, U.S. Geological Survey, 1994.

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Book chapters on the topic "Histogram"

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Gooch, Jan W. "Histogram." In Encyclopedic Dictionary of Polymers. 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. 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. 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. 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. Routledge, 2017. http://dx.doi.org/10.4324/9781315225647-19.

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Schwabish, Jonathan. "Histogram." In Data Visualization in Excel. 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. 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, 9th ed. Routledge, 2021. http://dx.doi.org/10.4324/9781003096764-19.

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Wu, Ying Nian. "Histogram." In Computer Vision. 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. Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_544.

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Conference papers on the topic "Histogram"

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Attarakih, Menwer, Mark W. Hlawitschka, Linda Al-Hmoud, and and Hans-J�rg Bart. "Unveiling Probability Histograms from Random Signals using a Variable-Order Quadrature Method of Moments." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.148742.

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Random signals are crucial in chemical and process engineering, where industrial plants generate big data that can be used for process understanding and decision-making. This makes it necessary to unveil the underlying probability histograms from these signals with a finite number of bins. However, the search for the optimal number of bins is still based on empirical optimisation and general rules of thumb. In this work, we introduce an alternative and general method to unveil probability histograms. Our method employs a novel variable-order QMOM, which adapts automatically based on the releva
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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. 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 t
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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. 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
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Kim, Soomin, JongHwan Oh, and Joonhwan Lee. "Histogram." In the 29th Annual Symposium. 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. 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 incl
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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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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. 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
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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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Reports on the topic "Histogram"

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Stricker, Markus A., and Michael J. Swain. The Capacity of Color Histogram Indexing. Defense Technical Information Center, 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), 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. Defense Technical Information Center, 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. Defense Technical Information Center, 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. Defense Technical Information Center, 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), 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. Defense Technical Information Center, 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. National Institute of Standards and Technology, 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 University Electronic Press, 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 test
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