Literatura académica sobre el tema "MODIFIED HISTOGRAM"
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Artículos de revistas sobre el tema "MODIFIED HISTOGRAM"
Tang, Jing Rui y Nor Ashidi Mat Isa. "Bi-histogram equalization using modified histogram bins". Applied Soft Computing 55 (junio de 2017): 31–43. http://dx.doi.org/10.1016/j.asoc.2017.01.053.
Texto completoPatil, Varsha y Tanuja Sarode. "Modified CSLBP". International Journal of Electrical and Computer Engineering (IJECE) 9, n.º 4 (1 de agosto de 2019): 2950. http://dx.doi.org/10.11591/ijece.v9i4.pp2950-2959.
Texto completoVorobel, R. A., O. R. Berehulyak, I. B. Ivasenko y T. S. Mandziy. "Modified method of image histogram hyperbolization". Information extraction and processing 2021, n.º 49 (17 de diciembre de 2021): 52–56. http://dx.doi.org/10.15407/vidbir2021.49.052.
Texto completoSkianis, G. Aim, Th Gournelos, D. Vaiopoulos y K. Nikolakopoulos. "A STUDY OF THE PERFORMANCE OF THE MODIFIED TRANSFORMED VEGETATION INDEX MTVI". Bulletin of the Geological Society of Greece 43, n.º 3 (24 de enero de 2017): 1647. http://dx.doi.org/10.12681/bgsg.11339.
Texto completoBerlinet, Alain, Gérard Biau y Laurent Rouvière. "Parameter selection in modified histogram estimates". Statistics 39, n.º 2 (abril de 2005): 91–105. http://dx.doi.org/10.1080/02331880500059713.
Texto completoKollem, Sreedhar, K. Ramalinga Reddy y D. Sreenivasa Rao. "Image Denoising by using Modified SGHP Algorithm". International Journal of Electrical and Computer Engineering (IJECE) 8, n.º 2 (1 de abril de 2018): 971. http://dx.doi.org/10.11591/ijece.v8i2.pp971-978.
Texto completoZhuang, Liyun y Yepeng Guan. "Image Enhancement Using Modified Histogram and Log-Exp Transformation". Symmetry 11, n.º 8 (20 de agosto de 2019): 1062. http://dx.doi.org/10.3390/sym11081062.
Texto completoSanthi, K. y R. S. D. Wahida Banu. "Adaptive contrast enhancement using modified histogram equalization". Optik - International Journal for Light and Electron Optics 126, n.º 19 (octubre de 2015): 1809–14. http://dx.doi.org/10.1016/j.ijleo.2015.05.023.
Texto completoSanthi, K. y R. S. D. Wahida Banu. "Contrast enhancement by modified octagon histogram equalization". Signal, Image and Video Processing 9, S1 (8 de mayo de 2014): 73–87. http://dx.doi.org/10.1007/s11760-014-0643-6.
Texto completoChen, Yung-Yao, Kai-Lung Hua, Yun-Chen Tsai y Jun-Hua Wu. "Photographic Reproduction and Enhancement Using HVS-Based Modified Histogram Equalization". Sensors 21, n.º 12 (16 de junio de 2021): 4136. http://dx.doi.org/10.3390/s21124136.
Texto completoTesis sobre el tema "MODIFIED HISTOGRAM"
Tannús, Marco Túlio Faissol. "Comparação de técnicas para a determinação de semelhança entre imagens digitais". Universidade Federal de Uberlândia, 2008. https://repositorio.ufu.br/handle/123456789/14388.
Texto completoA recuperação de imagens semelhantes em bancos de dados é um campo de pesquisa amplo, complexo e que apresenta grande demanda por aplicativos que apresentem bons resultados. O volume crescente de informações disponibilizadas ao público e o sucesso das ferramentas de busca textuais na Internet motivam a criação de utilitários que possibilitem a busca de imagens por semelhança de conteúdo. Podem-se utilizar várias características para a determinação da semelhança entre imagens digitais, tais como tamanho, cor, forma, variação de cores, textura, objetos e sua disposição espacial, entre outras. A textura e a cor são as duas características mais importantes que permitem uma análise preliminar da semelhança. Este trabalho apresenta várias técnicas constantes da literatura, que analisam textura e cor. Algumas dessas técnicas foram implementadas, seus desempenhos foram analisados e comparados e os resultados foram apresentados detalhadamente. Esse comparativo amplo permite determinar as melhores técnicas, possibilita a análise da aplicabilidade de cada uma delas e pode ser utilizada como referência em estudos futuros. As análises quantitativas de desempenho foram realizadas utilizando a métrica ANMRR, definida no padrão MPEG-7, e as matrizes de confusão, apresentadas para cada técnica testada. Dois grupos de testes quantitativos foram realizados: o primeiro utilizando um banco de imagens de texturas em tons de cinza e o segundo utilizando um banco de imagens coloridas. Os resultados dos testes com o banco de texturas em tons de cinza mostraram que as técnicas PBLIRU16, MCNC e sua combinação apresentaram os melhores desempenhos. Para o banco de imagens coloridas, os melhores desempenhos foram observados com a utilização das técnicas SCD, HDCIG e CSD.
Mestre em Ciências
Critchfield, Brian L. "Statistical Methods For Kinetic Modeling Of Fischer Tropsch Synthesis On A Supported Iron Catalyst". Diss., CLICK HERE for online access, 2006. http://contentdm.lib.byu.edu/ETD/image/etd1670.pdf.
Texto completoRouvière, Laurent. "Estimation de densité en dimension élevée et classification de courbes". Phd thesis, Université Montpellier II - Sciences et Techniques du Languedoc, 2005. http://tel.archives-ouvertes.fr/tel-00011624.
Texto completoLa première partie, intitulée compléments sur les histogrammes modifiés, est composée de deux chapitres consacrés l'étude d'une famille d'estimateurs non paramétriques de la densité, les histogrammes modifiés, connus pour posséder de bonnes propriétés de convergence au sens des critères de la théorie de l'information. Dans le premier chapitre, ces estimateurs sont envisagés comme des systèmes dynamiques espace d'états de dimension infinie. Le second chapitre est consacré l'étude de ces estimateurs pour des dimensions suprieures un.
La deuxième partie de la thèse, intituleé méthodes combinatoires en estimation de la densité, se divise en deux chapitres. Nous nous intéressons dans cette partie aux performances distance finie d'estimateurs de la densité sélectionnés à l'intérieur d'une famille d'estimateurs candidats, dont le cardinal n'est pas nécessairement fini. Dans le premier chapitre, nous étudions les performances de ces méthodes dans le cadre de la sélection des différents paramètres des histogrammes modifiés. Nous poursuivons, dans le deuxième chapitre, par la sélection d'estimateurs à noyau dont le paramètre de lissage s'adapte localement au point d'estimation et aux données.
Enfin, la troisième et dernière partie, plus appliquée et indépendante des précédentes, présente une nouvelle méthode permettant de classer des courbes partir d'une décomposition des observations dans des bases d'ondelettes.
KHANNA, CHINTAN. "SATELLITE IMAGE CONTRAST ENHANCEMENT USING MODIFIED HISTOGRAM". Thesis, 2016. http://dspace.dtu.ac.in:8080/jspui/handle/repository/15235.
Texto completoChou, Ching-Yao y 周敬堯. "Medical Image Enhancement Using Modified Color Histogram Equalization". Thesis, 2015. http://ndltd.ncl.edu.tw/handle/bdpgee.
Texto completo國立中正大學
醫療資訊管理研究所
103
Image enhancement represents a crucial application in medical imaging. Histogram equalization is one of the image enhancement techniques employed to enhance image contrast, which has become an vital part of general and medical image processing, and has been widely studied and applied. However, traditional histogram equalization achieves poor image enhancement results because it does not consider hue preservation. This study proposes a novel image enhancement method that incorporates hue preservation to address the problem of unpreserved hue in traditional approaches. In addition, this study use the Gabor filter to enhance image details. The results indicated that both methods achieved satisfactory results. Finally, this study proposed methods are applied to retinal and prostate cancer images. This can effectively assist physicians in making professional judgment.
Lo, Yi-Chung y 羅一中. "Low Cost FPGA Circuit Design of Modified Histogram Equalization". Thesis, 2005. http://ndltd.ncl.edu.tw/handle/h8k752.
Texto completo國立臺北科技大學
電機工程系所
93
For real-time moving picture contrast enhancement, the existing methods usually require one or more frame buffers to store the intermediate output, which is expensive to implement on a practical hardware system. Thus, this thesis proposes a modified histogram equalization (MHE) algorithm combined with a backward frame translation table to exclude any need of frame buffers. Conventional histogram equalization is widely adopted in contrast enhancement because of its automation in generating the transformation curve. It transforms the image based on the cumulative distribution function of histogram. However, there is a caveat to over-enhance the contrast when the auto-generated transform curve is too sharp due to some high peaks in the histogram. In this case, conventional histogram equalization may result in a harsh, noisy appearance of the output image. Our proposed MHE algorithm employs the mean and standard variation to pickup the high peaks in the histogram and modify histogram before making the translation table. Then the translation table was applied by a low pass filter to smooth on the contiguous values. This algorithm does not only avoid over-enhancement and increase dynamic range of the grey level, but also translates low spatial frequency area smoothly. The proposed algorithm was successfully implemented in an FPGA platform to demonstrate its effectiveness.
Su, Cing-De y 蘇慶德. "Vehicle Detection Algorithm Based on Modified Gradient Oriented Histogram Feature". Thesis, 2016. http://ndltd.ncl.edu.tw/handle/4329xy.
Texto completo國立雲林科技大學
電子工程系
104
In recent years, the public safety and home security are more and more important. The surveillance system will be becoming a hot industry. Therefore, this thesis proposed a modified gradient oriented histogram feature to identify vehicle for effective traffic control. This proposed method is divided into two parts. The first part is vehicle algorithm which use positive and negative samples to be input images in the training. The principal direction and the direction histogram are used for classification characteristics. Each pixel in the oriented image is represented by an angle bin, and 8*8 pixels for a cell histogram calculated is the presented by a 6*6 cell direction histogram. According the direction histogram, the maximal number of direction is the principal direction. The modified histogram orientation gradient (MHOG) feature is obtained by overlapping two cell in the cell direction histogram. The training parameters are obtained by inputting the MHOG features to SVM. When the principal direction of input image is same with the principal direction of training image, and the decision function of SVM is 1. Then, the window image will be a vehicle image. Experimental results show that the vehicle detect algorithm to achieve 98% which is better than SVM by HOG Feature detection. And average executing velocity of our method increase 40% in computer.
Chiang, Man-Shu y 江嫚書. "Nearly Reversible Data Hiding for Encrypted Audios by Modified Histogram Shifting". Thesis, 2016. http://ndltd.ncl.edu.tw/handle/63495366089534579842.
Texto completo國立臺灣大學
資訊網路與多媒體研究所
104
A nearly reversible and high capacity data hiding mechanism on encrypted audios is proposed in this thesis. In the first phase, instead of vacating room for embedding additional data from the encrypted audios, which may lead to inferior qualities of recovered signals, we exploit the continuity of original audio to estimate half of the samples and embed bits into their estimating errors. A content owner can encrypt the estimating errors with a specific encryption scheme. Note that any one of the existing encryption schemes, such as AES or bitwise exclusive OR, is allowed to be applied to the rest half of samples. With the data hiding key, a data hider can embed data in the encryption version of the audio. At the receiver side, those who have no knowledge about the encryption key are able to extract concealing bits without errors. Moreover, the order of signal decryption and data extraction can be exchanged depending on the requirements of applications. Furthermore, experimental results demonstrate that the proposed work achieved a superior imperceptibility of marked audios with large amount of embedding data and, more importantly, the recovered audios are also nearly totally reversible with segSNR higher than or equal to 60 dB.
LIN, BOR-HAUR y 林伯豪. "Gender Recognition from Gait-based Images with Modified Histogram of Template". Thesis, 2017. http://ndltd.ncl.edu.tw/handle/47368395541354315299.
Texto completo國立高雄大學
電機工程學系碩博士班
105
With the development of technology, gait recognition has become an important area of reseach in computer vision. Gender recognition from human gaits has drawn extensive attention in recent years. In this paper, we propose a new approach for gender recognition with human gait. We present a method which combines the gait energy image and a texture descriptor called MHOT to identify the gender from a human gait video, and the recognition is performaed by using a support vector machine. Experimental results on the CASIA dataset B are provided to illustrate the proposed approach is an effective method.
Chiang, Wen-Chin y 江文欽. "A Modified Histogram Shifting Based Reversible Data Hiding Scheme for High Quality Image". Thesis, 2011. http://ndltd.ncl.edu.tw/handle/07375330952899131765.
Texto completo育達商業技術學院
資訊管理所
99
Histogram-shifting is a well-known method among the reversible data hiding techniques. By counting the number of pixels for each pixel values in the image, it could construct a histogram, and the secret message could be hidden by shifting this histogram. Such modification will change the pixel value by 1 at most for each pixel in the stego image and the PSNR (Peak Signal-to-Noise Ratio) would be no less than 48dB. However, there are two problems for this method. First, the sizes of the peak (the most common pixel values appear in the image) determine the maximum payload. Second, as long as the payload within the maximum, the PSNR is hardly changed. To overcome these problems, this research proposed two ideas. First, use the pixel value difference between pixels to generate the histogram. Second, combine the histogram shifting process into embedding process to increase the stego image’s PSNR when the payload is low. According to the experimental result, the method we proposed allowing the stego image to have both better image quality and average 4 times the payload compare to Ni et al.’s method.
Capítulos de libros sobre el tema "MODIFIED HISTOGRAM"
Montazeri, Mitra. "Modified Histogram Segmentation Bi-Histogram Equalization". En Advances in Intelligent Systems and Computing, 443–53. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1081-6_38.
Texto completoHussain, Ayyaz, M. Arfan Jaffar, Abdul Basit Siddiqui, Muhammad Nazir y Anwar M. Mirza. "Modified Histogram Based Fuzzy Filter". En Computer Vision/Computer Graphics CollaborationTechniques, 277–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01811-4_25.
Texto completoNaushad Ali, M. M. y M. Abdullah-Al-Wadud. "Image Enhancement Using a Modified Histogram Equalization". En Communications in Computer and Information Science, 17–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35270-6_3.
Texto completoKim, Hyoung-Joon, Jong-Myung Lee, Jin-Aeon Lee, Sang-Geun Oh y Whoi-Yul Kim. "Contrast Enhancement Using Adaptively Modified Histogram Equalization". En Advances in Image and Video Technology, 1150–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11949534_116.
Texto completoKajiwara, Hiroshi, Hiroharu Kawanaka, Koji Yamamoto, Haruhiko Takase y Shinji Tsuruoka. "Modified Weighted Direction Index Histogram Method for Schema Recognition". En Graphics Recognition. Current Trends and Challenges, 63–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-44854-0_6.
Texto completoJain, Jitendra, Soyuj Kumar Sahoo, S. R. Mahadeva Prasanna y G. Siva Reddy. "Modified Chain Code Histogram Feature for Handwritten Character Recognition". En Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 611–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27299-8_64.
Texto completoTsai, Wen-Kai, Sheng-Kai Lo, Ching-De Su y Ming-Hwa Sheu. "Vehicle Detection Algorithm Based on Modified Gradient Oriented Histogram Feature". En Advances in Intelligent Information Hiding and Multimedia Signal Processing, 127–34. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-50212-0_16.
Texto completoZhao, Jie, Gang Xie y Wenjing Zhao. "A Modified Fuzzy Color Histogram Using Vision Perception Difference of Pixels Location". En Lecture Notes in Computer Science, 382–89. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08644-6_40.
Texto completoGupta, Vishal, Monish Gupta y Nikhil Marriwala. "A Modified Weighed Histogram Approach for Image Enhancement Using Optimized Alpha Parameter". En Mobile Radio Communications and 5G Networks, 521–31. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7018-3_39.
Texto completoMalik, Shaveta, Archana Mire, Amit Kumar Tyagi y Vasudha Arora. "A Novel Feature Extractor Based on the Modified Approach of Histogram of Oriented Gradient". En Computational Science and Its Applications – ICCSA 2020, 753–70. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58817-5_54.
Texto completoActas de conferencias sobre el tema "MODIFIED HISTOGRAM"
Qiu, Qin-Jun, Yong Liu, Da-Wei Cai y Jia-Zheng Tan. "Modified Color Texton Histogram for Image Retrieval". En 2013 International Conference on Computer Sciences and Applications (CSA). IEEE, 2013. http://dx.doi.org/10.1109/csa.2013.142.
Texto completoMurahira, Kota, Takashi Kawakami y Akira Taguchi. "Modified histogram equalization for image contrast enhancement". En 2010 4th International Symposium on Communications, Control and Signal Processing (ISCCSP). IEEE, 2010. http://dx.doi.org/10.1109/isccsp.2010.5463329.
Texto completoA. Kandeel, Amany, Alaa M. Abbas, Mohiy M. Hadhoud y Zeiad El-Saghir. "A Modified Histogram Based Fast Enhancement Algorithm". En International Conference on Foundations of Computer Science & Technology. Academy & Industry Research Collaboration Center (AIRCC), 2014. http://dx.doi.org/10.5121/csit.2014.4124.
Texto completoLi, Yuecheng y Hong Zhang. "Modified Clipped Histogram Equalization for Contrast Enhancement". En 2012 13th International Conference on Parallel and Distributed Computing Applications and Technologies (PDCAT). IEEE, 2012. http://dx.doi.org/10.1109/pdcat.2012.97.
Texto completoTanaka, Hideaki y Akira Taguchi. "Modified Histogram Equalization Emphasizing Only Region of Interest". En 2019 19th International Symposium on Communications and Information Technologies (ISCIT). IEEE, 2019. http://dx.doi.org/10.1109/iscit.2019.8905153.
Texto completoYelmanov, Sergei y Yuriy Romanyshyn. "Image Contrast Enhancement Using a Modified Histogram Equalization". En 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP). IEEE, 2018. http://dx.doi.org/10.1109/dsmp.2018.8478441.
Texto completoTai, Shen-Chuan, Chia-Ying Chang, Yi-Shan Lin y Yu-Yi Liao. "Modified histogram equalization based on the partitioned dynamic range". En 2014 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW). IEEE, 2014. http://dx.doi.org/10.1109/icce-tw.2014.6904007.
Texto completoSichao Li, Jinhai Sun, Jinhai Li y Yuepeng Yan. "A modified histogram bit synchronization algorithm for GNSS receivers". En 2010 2nd International Conference on Information Science and Engineering (ICISE). IEEE, 2010. http://dx.doi.org/10.1109/icise.2010.5691411.
Texto completoPark, Hye Jin, Min-Seok Ji, Jae-Taeg Ha, Jong-Hun Chun y Jong An Park. "Shape matching using the modified histogram-based chain code". En Asia-Pacific Optical and Wireless Communications, editado por Shumin Cao, Chih-Lin I y Jiann-An Tsai. SPIE, 2004. http://dx.doi.org/10.1117/12.520672.
Texto completoLi, Yang, Runmin Sun, Chen Luo y Yijun Zhou. "A modified Histogram Equalization approach for Image Contrast Enhancement". En 2022 2nd International Conference on Consumer Electronics and Computer Engineering (ICCECE). IEEE, 2022. http://dx.doi.org/10.1109/iccece54139.2022.9712841.
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