Готові списки джерел за темами / MODIFIED HISTOGRAM

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Добірка наукової літератури з теми "MODIFIED HISTOGRAM"

Автор: Grafiati

Опубліковано: 11 вересня 2023

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Статті в журналах з теми "MODIFIED HISTOGRAM"

Tang, Jing Rui, and Nor Ashidi Mat Isa. "Bi-histogram equalization using modified histogram bins." Applied Soft Computing 55 (June 2017): 31–43. http://dx.doi.org/10.1016/j.asoc.2017.01.053.

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Patil, Varsha, and Tanuja Sarode. "Modified CSLBP." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 4 (August 1, 2019): 2950. http://dx.doi.org/10.11591/ijece.v9i4.pp2950-2959.

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<p>Image hashing is an efficient way to handle digital data authentication problem. Image hashing represents quality summarization of image features in compact manner. In this paper, the modified center symmetric local binary pattern (CSLBP) image hashing algorithm is proposed. Unlike CSLBP 16 bin histogram, Modified CSLBP generates 8 bin histogram without compromise on quality to generate compact hash. It has been found that, uniform quantization on a histogram with more bin results in more precision loss. To overcome quantization loss, modified CSLBP generates the two histogram of a four bin. Uniform quantization on a 4 bin histogram results in less precision loss than a 16 bin histogram. The first generated histogram represents the nearest neighbours and second one is for the diagonal neighbours. To enhance quality in terms of discrimination power, different weight factor are used during histogram generation. For the nearest and the diagonal neighbours, two local weight factors are used. One is the Standard Deviation (SD) and other is the Laplacian of Gaussian (LoG). Standard deviation represents a spread of data which captures local variation from mean. LoG is a second order derivative edge detection operator which detects edges well in presence of noise. The proposed algorithm is resilient to the various kinds of attacks. The proposed method is tested on database having malicious and non-malicious images using benchmark like NHD and ROC which confirms theoretical analysis. The experimental results shows good performance of the proposed method for various attacks despite the short hash length.</p>

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Vorobel, R. A., O. R. Berehulyak, I. B. Ivasenko, and T. S. Mandziy. "Modified method of image histogram hyperbolization." Information extraction and processing 2021, no. 49 (December 17, 2021): 52–56. http://dx.doi.org/10.15407/vidbir2021.49.052.

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Анотація:

One of the methods to improve image quality, which consists in increasing the resolution of image details by contrast enhancement, is to hyperbolize the image histogram. Herewith this increase in local contrast is carried out indirectly. It is due to the nature of the change in the histogram of the transformed image. Usually the histogram of the input image is transformed so that it has a uniform distribution, which illustrates the same contribution of pixels gray level to the image structure. However, there is a method that is based on modeling the human visual system, which is characterized by the logarithmic dependence of the human reaction to light stimulation. It consists in the hyperbolic transformation of the histogram of the image. Then, due to its perception by the visual system, at its output, during the psychophysical perception of the image, an approximately uniform distribution of the histogram of the levels of gray pixels is formed. But the drawback is the lack of effectiveness of this approach for excessively light or dark images. The modified method of image histogram hyperbolization has been developed. It is based on the power transformation of the probability distribution function, which in the discrete version of the images is approximated by a normalized cumulative histogram. The power index is a control parameter of the transformation. to improve the darkened images we use the value of the control parameter less than one, and for light images more than one. The effectiveness of the proposed method is shown by examples.

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Skianis, G. Aim, Th Gournelos, D. Vaiopoulos, and K. Nikolakopoulos. "A STUDY OF THE PERFORMANCE OF THE MODIFIED TRANSFORMED VEGETATION INDEX MTVI." Bulletin of the Geological Society of Greece 43, no. 3 (January 24, 2017): 1647. http://dx.doi.org/10.12681/bgsg.11339.

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In the context of a recent research on the performance of vegetation indices we have shown, with the aid of probability theory, that the shape and width of the histogram of the Transformed Vegetation Index TVI is controlled by the ratio of the standard deviation of the Red band to that of the NIR band. Therefore a modification of the mathematical expression of the TVI vegetation index may produce images with a varying tonality contrast. In the present paper the modified transformed vegetation index MTVI is introduced, the value of which is controlled by a positive parameter c. A theoretical study of the effect of this parameter on the image histogram is first carried out and it is shown that changing c one can obtain MTVI images with different histograms and standard deviations. Experimentation with a satellite image over western Peloponnese verifies that the parameter c controls the shape of the MTVI histogram and, furthermore, the optical effect of the MTVI image as well as the spatial variation (semivariogram) of the pixel values. Therefore the proposed modified transformed vegetation index may help the potential user in broadening his/her choices to map the vegetation cover of the area under study.

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Berlinet, Alain, Gérard Biau, and Laurent Rouvière. "Parameter selection in modified histogram estimates." Statistics 39, no. 2 (April 2005): 91–105. http://dx.doi.org/10.1080/02331880500059713.

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Kollem, Sreedhar, K. Ramalinga Reddy, and D. Sreenivasa Rao. "Image Denoising by using Modified SGHP Algorithm." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 2 (April 1, 2018): 971. http://dx.doi.org/10.11591/ijece.v8i2.pp971-978.

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In real time applications, image denoising is a predominant task. This task makes adequate preparation for images looks prominent. But there are several denoising algorithms and every algorithm has its own distinctive attribute based upon different natural images. In this paper, we proposed a perspective that is modified parameter in S-Gradient Histogram Preservation denoising method. S-Gradient Histogram Preservation is a method to compute the structure gradient histogram from the noisy observation by taking different noise standard deviations of different images. The performance of this method is enumerated in terms of peak signal to noise ratio and structural similarity index of a particular image. In this paper, mainly focus on peak signal to noise ratio, structural similarity index, noise estimation and a measure of structure gradient histogram of a given image.

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Zhuang, Liyun, and Yepeng Guan. "Image Enhancement Using Modified Histogram and Log-Exp Transformation." Symmetry 11, no. 8 (August 20, 2019): 1062. http://dx.doi.org/10.3390/sym11081062.

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Анотація:

An effective method to enhance the contrast of digital images is proposed in this paper. A histogram function is developed to make the histogram curve smoother, which can be used to avoid the loss of information in the processed image. Besides the histogram function, an adaptive gamma correction for the histogram is proposed to stretch the brightness contrast. Moreover, the log-exp transformation strategy is presented to progressively increase the low intensity while suppressing the decrement of the high intensity. In order to further widen the dynamic range of the image, the nonlinear normalization transformation is put forward to make the output image more natural and clearer. In the experiment on non-uniform illumination images, the average contrast per pixel (CPP), root mean square (RMS), and discrete entropy (DE) metrics of the developed approach are shown to be superior to selected state-of-the-art methods.

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Santhi, K., and R. S. D. Wahida Banu. "Adaptive contrast enhancement using modified histogram equalization." Optik - International Journal for Light and Electron Optics 126, no. 19 (October 2015): 1809–14. http://dx.doi.org/10.1016/j.ijleo.2015.05.023.

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Santhi, K., and R. S. D. Wahida Banu. "Contrast enhancement by modified octagon histogram equalization." Signal, Image and Video Processing 9, S1 (May 8, 2014): 73–87. http://dx.doi.org/10.1007/s11760-014-0643-6.

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Chen, Yung-Yao, Kai-Lung Hua, Yun-Chen Tsai, and Jun-Hua Wu. "Photographic Reproduction and Enhancement Using HVS-Based Modified Histogram Equalization." Sensors 21, no. 12 (June 16, 2021): 4136. http://dx.doi.org/10.3390/s21124136.

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Анотація:

Photographic reproduction and enhancement is challenging because it requires the preservation of all the visual information during the compression of the dynamic range of the input image. This paper presents a cascaded-architecture-type reproduction method that can simultaneously enhance local details and retain the naturalness of original global contrast. In the pre-processing stage, in addition to using a multiscale detail injection scheme to enhance the local details, the Stevens effect is considered for adapting different luminance levels and normally compressing the global feature. We propose a modified histogram equalization method in the reproduction stage, where individual histogram bin widths are first adjusted according to the property of overall image content. In addition, the human visual system (HVS) is considered so that a luminance-aware threshold can be used to control the maximum permissible width of each bin. Then, the global tone is modified by performing histogram equalization on the output modified histogram. Experimental results indicate that the proposed method can outperform the five state-of-the-art methods in terms of visual comparisons and several objective image quality evaluations.

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Більше джерел

Дисертації з теми "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.

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Анотація:

The retrieval of similar images in databases is a wide and complex research field that shows a great demand for good performance applications. The increasing volume of information available in the Internet and the success of textual search engines motivate the development of tools that make possible image searches by content similarity. Many features can be applied in determining the similarity between images, such as size, color, shape, color variation, texture, objects and their spatial distribution, among others. Texture and color are the most important features which allow a preliminary analysis of image similarity. This dissertation presents many techniques introduced in the literature, which analyze texture and color. Some of them were implemented, their performances were compared and the results were presented. This comparison allows the determination of the best techniques, making possible the analysis of their applicability and can be used as a reference in future works. The quantitative performance analyses were done using the ANMRR metric, defined in the MPEG-7 standard, and the confusion matrices were presented for each of the tested techniques. Two groups of quantitative tests were realized: the first one was applied upon a gray scale texture database and the second one, upon a color image database. For the experiment with the gray scale texture images, the techniques PBLIRU16, MCNC and their combination presented the best performances. For the experiment with the color images, SCD, HDCIG and CSD techniques performed best.
A 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

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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.

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Rouvière, 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.

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L'objectif de cette thèse consiste étudier et approfondir des techniques d'estimation de la densité et de classification dans des espaces de dimension élevée. Nous avons choisi de structurer notre travail en trois parties.

La 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.

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KHANNA, CHINTAN. "SATELLITE IMAGE CONTRAST ENHANCEMENT USING MODIFIED HISTOGRAM." Thesis, 2016. http://dspace.dtu.ac.in:8080/jspui/handle/repository/15235.

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This project presents a study of various Histogram Equalisation based Contrast Enhancement (CE) techniques followed by the proposal of a novel CE algorithm for satellite and aerial images. The algorithm is referred as Contour Based Histogram Equalisation (CBHE). The algorithm presents a novel method to capture the structural property of an image using the contour of the image. The algorithm addresses the inherent drawbacks of HE viz. artefacts and saturation by decreasing the contribution of high probability pixels in the histogram and increasing that of low probability pixels. Finally the algorithm enhances the features of the image by adjusting the coefficients of DCT. The algorithm generates good contrast images with richer details over a varied set of images including satellite and aerial images. It is computationally comparable to HE, does not introduce noise and saturation, preserves characteristic shape of original image histogram and does not enhance an already high contrast image. It thus qualifies as an effective pre-processing step. CBHE is compared with the conventional and best of HE based techniques both quantitatively and visually. The quantitative analysis of the results is carried out using several standard measures like Discrete Entropy, Signal to Noise Ratio (PSNR), Measurement of Enhancement (EME), Average Mean brightness Error (AMBE), Gradient Magnitude Similarity Index (GMSD) and Structural Similarity Index (SSI) over varied datasets.

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Chou, Ching-Yao, and 周敬堯. "Medical Image Enhancement Using Modified Color Histogram Equalization." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/bdpgee.

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Анотація:

碩士
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.

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Lo, Yi-Chung, and 羅一中. "Low Cost FPGA Circuit Design of Modified Histogram Equalization." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/h8k752.

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碩士
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.

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Su, Cing-De, and 蘇慶德. "Vehicle Detection Algorithm Based on Modified Gradient Oriented Histogram Feature." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/4329xy.

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Анотація:

碩士
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.

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Chiang, Man-Shu, and 江嫚書. "Nearly Reversible Data Hiding for Encrypted Audios by Modified Histogram Shifting." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/63495366089534579842.

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Анотація:

碩士
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.

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LIN, BOR-HAUR, and 林伯豪. "Gender Recognition from Gait-based Images with Modified Histogram of Template." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/47368395541354315299.

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Анотація:

碩士
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.

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Chiang, Wen-Chin, and 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.

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Анотація:

碩士
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.

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Частини книг з теми "MODIFIED HISTOGRAM"

Montazeri, Mitra. "Modified Histogram Segmentation Bi-Histogram Equalization." In Advances in Intelligent Systems and Computing, 443–53. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1081-6_38.

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Hussain, Ayyaz, M. Arfan Jaffar, Abdul Basit Siddiqui, Muhammad Nazir, and Anwar M. Mirza. "Modified Histogram Based Fuzzy Filter." In 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.

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Naushad Ali, M. M., and M. Abdullah-Al-Wadud. "Image Enhancement Using a Modified Histogram Equalization." In 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.

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Kim, Hyoung-Joon, Jong-Myung Lee, Jin-Aeon Lee, Sang-Geun Oh, and Whoi-Yul Kim. "Contrast Enhancement Using Adaptively Modified Histogram Equalization." In Advances in Image and Video Technology, 1150–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11949534_116.

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Kajiwara, Hiroshi, Hiroharu Kawanaka, Koji Yamamoto, Haruhiko Takase, and Shinji Tsuruoka. "Modified Weighted Direction Index Histogram Method for Schema Recognition." In 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.

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Jain, Jitendra, Soyuj Kumar Sahoo, S. R. Mahadeva Prasanna, and G. Siva Reddy. "Modified Chain Code Histogram Feature for Handwritten Character Recognition." In 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.

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Tsai, Wen-Kai, Sheng-Kai Lo, Ching-De Su, and Ming-Hwa Sheu. "Vehicle Detection Algorithm Based on Modified Gradient Oriented Histogram Feature." In 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.

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Zhao, Jie, Gang Xie, and Wenjing Zhao. "A Modified Fuzzy Color Histogram Using Vision Perception Difference of Pixels Location." In Lecture Notes in Computer Science, 382–89. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08644-6_40.

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Gupta, Vishal, Monish Gupta, and Nikhil Marriwala. "A Modified Weighed Histogram Approach for Image Enhancement Using Optimized Alpha Parameter." In Mobile Radio Communications and 5G Networks, 521–31. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7018-3_39.

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Malik, Shaveta, Archana Mire, Amit Kumar Tyagi, and Vasudha Arora. "A Novel Feature Extractor Based on the Modified Approach of Histogram of Oriented Gradient." In 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.

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Тези доповідей конференцій з теми "MODIFIED HISTOGRAM"

Qiu, Qin-Jun, Yong Liu, Da-Wei Cai, and Jia-Zheng Tan. "Modified Color Texton Histogram for Image Retrieval." In 2013 International Conference on Computer Sciences and Applications (CSA). IEEE, 2013. http://dx.doi.org/10.1109/csa.2013.142.

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Murahira, Kota, Takashi Kawakami, and Akira Taguchi. "Modified histogram equalization for image contrast enhancement." In 2010 4th International Symposium on Communications, Control and Signal Processing (ISCCSP). IEEE, 2010. http://dx.doi.org/10.1109/isccsp.2010.5463329.

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A. Kandeel, Amany, Alaa M. Abbas, Mohiy M. Hadhoud, and Zeiad El-Saghir. "A Modified Histogram Based Fast Enhancement Algorithm." In International Conference on Foundations of Computer Science & Technology. Academy & Industry Research Collaboration Center (AIRCC), 2014. http://dx.doi.org/10.5121/csit.2014.4124.

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Li, Yuecheng, and Hong Zhang. "Modified Clipped Histogram Equalization for Contrast Enhancement." In 2012 13th International Conference on Parallel and Distributed Computing Applications and Technologies (PDCAT). IEEE, 2012. http://dx.doi.org/10.1109/pdcat.2012.97.

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Tanaka, Hideaki, and Akira Taguchi. "Modified Histogram Equalization Emphasizing Only Region of Interest." In 2019 19th International Symposium on Communications and Information Technologies (ISCIT). IEEE, 2019. http://dx.doi.org/10.1109/iscit.2019.8905153.

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Yelmanov, Sergei, and Yuriy Romanyshyn. "Image Contrast Enhancement Using a Modified Histogram Equalization." In 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP). IEEE, 2018. http://dx.doi.org/10.1109/dsmp.2018.8478441.

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Tai, Shen-Chuan, Chia-Ying Chang, Yi-Shan Lin, and Yu-Yi Liao. "Modified histogram equalization based on the partitioned dynamic range." In 2014 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW). IEEE, 2014. http://dx.doi.org/10.1109/icce-tw.2014.6904007.

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Sichao Li, Jinhai Sun, Jinhai Li, and Yuepeng Yan. "A modified histogram bit synchronization algorithm for GNSS receivers." In 2010 2nd International Conference on Information Science and Engineering (ICISE). IEEE, 2010. http://dx.doi.org/10.1109/icise.2010.5691411.

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Park, Hye Jin, Min-Seok Ji, Jae-Taeg Ha, Jong-Hun Chun, and Jong An Park. "Shape matching using the modified histogram-based chain code." In Asia-Pacific Optical and Wireless Communications, edited by Shumin Cao, Chih-Lin I, and Jiann-An Tsai. SPIE, 2004. http://dx.doi.org/10.1117/12.520672.

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Li, Yang, Runmin Sun, Chen Luo, and Yijun Zhou. "A modified Histogram Equalization approach for Image Contrast Enhancement." In 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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