Статті в журналах: "MODIFIED HISTOGRAM"

1

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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2

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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3

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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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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4

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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5

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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6

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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7

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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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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8

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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9

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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10

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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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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11

Chan, Kelvin, Raymond Chan, and Mila Nikolova. "A Convex Model for Edge-Histogram Specification with Applications to Edge-Preserving Smoothing." Axioms 7, no. 3 (August 2, 2018): 53. http://dx.doi.org/10.3390/axioms7030053.

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The goal of edge-histogram specification is to find an image whose edge image has a histogram that matches a given edge-histogram as much as possible. Mignotte has proposed a non-convex model for the problem in 2012. In his work, edge magnitudes of an input image are first modified by histogram specification to match the given edge-histogram. Then, a non-convex model is minimized to find an output image whose edge-histogram matches the modified edge-histogram. The non-convexity of the model hinders the computations and the inclusion of useful constraints such as the dynamic range constraint. In this paper, instead of considering edge magnitudes, we directly consider the image gradients and propose a convex model based on them. Furthermore, we include additional constraints in our model based on different applications. The convexity of our model allows us to compute the output image efficiently using either Alternating Direction Method of Multipliers or Fast Iterative Shrinkage-Thresholding Algorithm. We consider several applications in edge-preserving smoothing including image abstraction, edge extraction, details exaggeration, and documents scan-through removal. Numerical results are given to illustrate that our method successfully produces decent results efficiently.

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12

K, Ribana. "Image Enhancement using Modified Histogram Equalization with DSIHE." IJARCCE 6, no. 1 (January 30, 2017): 325–27. http://dx.doi.org/10.17148/ijarcce.2017.6162.

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13

Sundaram, M., K. Ramar, N. Arumugam, and G. Prabin. "Histogram-modified local contrast enhancement for mammogram images." International Journal of Biomedical Engineering and Technology 9, no. 1 (2012): 60. http://dx.doi.org/10.1504/ijbet.2012.047371.

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14

Skosan, Marshalleno, and Daniel Mashao. "Modified Segmental Histogram Equalization for robust speaker verification." Pattern Recognition Letters 27, no. 5 (April 2006): 479–86. http://dx.doi.org/10.1016/j.patrec.2005.09.009.

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15

Sundaram, M., K. Ramar, N. Arumugam, and G. Prabin. "Histogram Modified Local Contrast Enhancement for mammogram images." Applied Soft Computing 11, no. 8 (December 2011): 5809–16. http://dx.doi.org/10.1016/j.asoc.2011.05.003.

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16

Poddar, Shashi, Deewakar Sharma, Ashish Ghosh, Suman Tewary, Vinod Karar, and Sankar K. Pal. "Non-parametric modified histogram equalisation for contrast enhancement." IET Image Processing 7, no. 7 (October 1, 2013): 641–52. http://dx.doi.org/10.1049/iet-ipr.2012.0507.

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17

Hsu, Wei-Yen, and Ching-Yao Chou. "Medical Image Enhancement Using Modified Color Histogram Equalization." Journal of Medical and Biological Engineering 35, no. 5 (September 21, 2015): 580–84. http://dx.doi.org/10.1007/s40846-015-0078-8.

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18

Sandulyak, A. A., V. A. Ershova, A. V. Sandulyak, and M. N. Pugacheva. "The Characteristic Sizes of Ferromagnetic Impurity Working Environments (for raw data histograms)." Izvestiya MGTU MAMI 2, no. 1 (January 10, 2008): 198–200. http://dx.doi.org/10.17816/2074-0530-69726.

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The dispersed composition of ferro-admixtures in foodstuffs is studied. An ordinary histogram is transformed into a modified histogram showing the portion of particles in total volume (mass, concentration). It enables identifying of particle predominant sizes.

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19

Rudikov, S. I., V. Yu Tsviatkou, and A. P. Shkadarevich. "Dynamic range reduction of infrared images based on adaptive equalization, stretch and compression of histogram." Proceedings of the National Academy of Sciences of Belarus, Physical-Technical Series 66, no. 4 (December 26, 2021): 470–82. http://dx.doi.org/10.29235/1561-8358-2021-66-4-470-482.

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The problem of reducing the dynamic range and improving the quality of infrared (IR) images with a wide dynamic range for their display on a liquid crystal matrix with 8-bit pixels is considered. To solve this problem in optoelectronic devices in real time, block algorithms based on local equalization of the histogram are widely used, taking into account their relatively low computational complexity and the possibility of taking into account local features of the brightness distribution. The basic adaptive histogram equalization algorithm provides reasonably high image quality after conversion, but may result in excessive contrast for some types of images. In a modified algorithm of adaptive histogram equalization, the contrast is limited by a threshold by truncating local maxima at the edges of the histogram. This leads, however, to a deterioration in other indicators of image quality. This disadvantage is inherent in many algorithms of local histogram equalization, along with limited control over the characteristics of image reproduction quality. To improve the quality and expand the control interval for the characteristics of the reproduction of infrared images, the article proposes an algorithm for double reduction of the dynamic range of the image with intermediate control of the shape of its histogram. This algorithm performs: preliminary reduction of the dynamic range of the image based on adaptive equalization of the histogram, control of the shape of the histogram based on its linear or nonlinear compression, linear stretching of its central part and linear stretching (compression) of its lateral parts, final reduction of the dynamic range based on linear compression of the entire histograms. The characteristics of the proposed algorithm are compared with the characteristics of known algorithms for reducing the dynamic range and improving the image quality. The dependences of the characteristics of the quality of image reproduction after a decrease in their dynamic range on the control parameters of the proposed algorithm and recommendations for their choice taking into account the computational complexity are given.

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20

Zhao, Yu Qian, and Zhi Gang Li. "FPGA Implementation of Real-Time Adaptive Bidirectional Equalization for Histogram." Advanced Materials Research 461 (February 2012): 215–19. http://dx.doi.org/10.4028/www.scientific.net/amr.461.215.

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According to the characteristics of infrared images, a contrast enhancement algorithm was presented. The principium of FPGA-based adaptive bidirectional plateau histogram equalization was given in this paper. The plateau value was obtained by finding local maximum and whole maximum in statistical histogram based on dimensional histogram statistic. Statistical histogram was modified by the plateau value and balanced in gray scale and gray spacing. Test data generated by single frame image, which was simulated by FPGA-based real-time adaptive bidirectional plateau histogram equalization. The simulation results indicates that the precept meet the requests well in both the image processing effects and processing speed

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21

Lee, Chang-Young, and Nam-Ho Kim. "A Study on Edge Detection using Modified Histogram Equalization." Journal of the Korea Institute of Information and Communication Engineering 19, no. 5 (May 31, 2015): 1221–27. http://dx.doi.org/10.6109/jkiice.2015.19.5.1221.

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22

M., Rajeshkumar. "Quad Histogram based Color Feature Extraction and Modified Convolutional Neural Network for Weed Classification." Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (March 31, 2020): 85–96. http://dx.doi.org/10.5373/jardcs/v12sp4/20201469.

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23

Murali, V., and T. Venkateswarlu. "A Novel Technique for Automatic Image Enhancement using HTHET Approach." Asian Journal of Computer Science and Technology 8, no. 1 (February 5, 2019): 26–31. http://dx.doi.org/10.51983/ajcst-2019.8.1.2123.

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Image enhancement techniques are methods used for producing images with better quality than the original image. None of the existing methods increase the information content of the image, and are usually of little interest for subsequent automatic analysis of images. In this paper, automated Image Enhancement is achieved by carrying out Histogram techniques. Histogram equalization (HE) is a spatial domain image enhancement technique, which effectively enhances the contrast of an image. We make use of Transformation and Hyperbolization techniques for automatic image enhancement. However, while it takes care of contrast enhancement, a modified histogram equalization technique, Histogram Transformation and Hyperbolization Equalization Technique (HTHET) using optimization method is proposed using EQHIST and LINHIST.

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24

Jagatheeswari, P., S. Suresh Kumar, and M. Mary Linda. "Quadrant Dynamic with Automatic Plateau Limit Histogram Equalization for Image Enhancement." Mathematical Problems in Engineering 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/302732.

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The fundamental and important preprocessing stage in image processing is the image contrast enhancement technique. Histogram equalization is an effective contrast enhancement technique. In this paper, a histogram equalization based technique called quadrant dynamic with automatic plateau limit histogram equalization (QDAPLHE) is introduced. In this method, a hybrid of dynamic and clipped histogram equalization methods are used to increase the brightness preservation and to reduce the overenhancement. Initially, the proposed QDAPLHE algorithm passes the input image through a median filter to remove the noises present in the image. Then the histogram of the filtered image is divided into four subhistograms while maintaining second separated point as the mean brightness. Then the clipping process is implemented by calculating automatically the plateau limit as the clipped level. The clipped portion of the histogram is modified to reduce the loss of image intensity value. Finally the clipped portion is redistributed uniformly to the entire dynamic range and the conventional histogram equalization is executed in each subhistogram independently. Based on the qualitative and the quantitative analysis, the QDAPLHE method outperforms some existing methods in literature.

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25

Overton, W. Roy. "Modified histogram subtraction technique for analysis of flow cytometry data." Cytometry 9, no. 6 (November 1988): 619–26. http://dx.doi.org/10.1002/cyto.990090617.

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26

Chan, Wai Ti. "Conditional Noise Filter for MRI Images with Revised Theory on Second-order Histograms." International Journal on Robotics, Automation and Sciences 3 (November 8, 2021): 25–32. http://dx.doi.org/10.33093/ijoras.2021.3.5.

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Previous research by the author has the theory that histograms of second-order derivatives are capable of determining differences between pixels in MRI images for the purpose of noise reduction without having to refer to ground truth. However, the methodology of the previous research resulted in significant false negatives in determining which pixel is affected by noise. The theory has been revised in this article through the introduction of an additional Laplace curve, leading to comparisons between the histogram profile and two curves instead of just one. The revised theory is that differences between the first curve and the histogram profile and the differences between the second curve and the profile can determine which pixels are to be selected for filtering in order to improve image clarity while minimizing blurring. The revised theory is tested with a modified average filter versus a generic average filter, with PSNR and SSIM for scoring. The results show that for most of the sample MRI images, the theory of pixel selection is more reliable at higher levels of noise but not as reliable at preventing blurring at low levels of noise.

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27

Babu, P., V. Rajamani, and K. Balasubramanian. "Multipeak Mean Based Optimized Histogram Modification Framework Using Swarm Intelligence for Image Contrast Enhancement." Mathematical Problems in Engineering 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/265723.

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A novel approach, Multipeak mean based optimized histogram modification framework (MMOHM) is introduced for the purpose of enhancing the contrast as well as preserving essential details for any given gray scale and colour images. The basic idea of this technique is the calculation of multiple peaks (local maxima) from the original histogram. The mean value of multiple peaks is computed and the input image’s histogram is segmented into two subhistograms based on this multipeak mean (mmean) value. Then, a bicriteria optimization problem is formulated and the subhistograms are modified by selecting optimal contrast enhancement parameters. While formulating the enhancement parameters, particle swarm optimization is employed to find optimal values of them. Finally, the union of the modified subhistograms produces a contrast enhanced and details preserved output image. This mechanism enhances the contrast of the input image better than the existing contemporary HE methods. The performance of the proposed method is well supported by the contrast enhancement quantitative metrics such as discrete entropy, natural image quality evaluator, and absolute mean brightness error.

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28

Wang, Gui Zhou, and Guo Jin He. "A Modified Multi Scale Retinex with Color Restoration Algorithm for Automatic Enhancement of Landsat-5 Remote Sensing Image." Advanced Materials Research 341-342 (September 2011): 893–97. http://dx.doi.org/10.4028/www.scientific.net/amr.341-342.893.

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The retinex is a human perception based image processing algorithm which provides color constancy and dynamic range compression. The multi scale retinex with color restoration (MSRCR) has shown itself to be a very versatile automatic image enhancement algorithm that simultaneously provides dynamic range compression, color constancy, and color rendition. But the MSRCR results suffer from lower global brightness and partial color distortion. In order to improve the MSRCR method, this paper presents a modified MSRCR algorithm to Landsat-5 image enhancement considering percent liner stretch and histogram adjustment. Finally, the effect of modified MSRCR method on Landsat-5 image enhancement is analyzed and the comparison with other color adjustment methods such as gamma correction and histogram equalization is reported in the experimental results.

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29

Clifford, M. J., K. A. Simmons, J. Roberts, and T. D. Truscot. "Computational fluid dynamics modelling of a static mixer." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 220, no. 3 (March 1, 2006): 325–32. http://dx.doi.org/10.1243/09544062c06405.

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In this paper, the data from physical experiments are used to quantify the mixing efficiency of a static mixer injected with two coloured streams of fluid. Two image analysis techniques are investigated - counting striations and a modified mixing index approach based on creating a histogram of greyscale values from the digitized image. The histogram approach is identified as the more promising and establishes the method for a future, more detailed study Data from a computational fluid dynamics (CFD) model of the mixer were analysed using the modified mixing index approach. Comparison to experimental results suggests that the bulk behaviour of the static mixer can be adequately captured by the deterministic CFD approach, despite the chaotic nature of the original mixing process.

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30

Liu, Dong Mei, Tao Zhang, Chuan Li Yin, and Xiao Qiang Ji. "An Embedded Infrared Image Enhancement System Based on DSP and FPGA." Advanced Materials Research 505 (April 2012): 263–66. http://dx.doi.org/10.4028/www.scientific.net/amr.505.263.

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According to the disadvantage of the large noises of histogram equalization algorithm, a new adaptive image enhancement algorithm is presented. First, the statistical histogram of the infrared image is done. Then the threshold of plateaus Equalization is calculated and the statistical histogram is modified. Finally the bright values of the pixels of the image are changed. An embedded high speed image enhancement processing system on high performance DSP TMS320DM642 and FPGA was designed. Experimental results with real images shown that the system can improve the contrast of the infrared image, limit the noises of the enhancement image, and effectively enhance the infrared image, the running time of the program is shorter, so it can meet the requirements of real-time in the project.

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31

Zhuang, Liyun, and Yepeng Guan. "Image Enhancement via Subimage Histogram Equalization Based on Mean and Variance." Computational Intelligence and Neuroscience 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/6029892.

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This paper puts forward a novel image enhancement method via Mean and Variance based Subimage Histogram Equalization (MVSIHE), which effectively increases the contrast of the input image with brightness and details well preserved compared with some other methods based on histogram equalization (HE). Firstly, the histogram of input image is divided into four segments based on the mean and variance of luminance component, and the histogram bins of each segment are modified and equalized, respectively. Secondly, the result is obtained via the concatenation of the processed subhistograms. Lastly, the normalization method is deployed on intensity levels, and the integration of the processed image with the input image is performed. 100 benchmark images from a public image database named CVG-UGR-Database are used for comparison with other state-of-the-art methods. The experiment results show that the algorithm can not only enhance image information effectively but also well preserve brightness and details of the original image.

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32

Holmes, Philip M., Kun-Hui Chen, Hyungkyi Lee, James Fitzsimmons, Shawn O'Driscoll, and Matthew W. Urban. "Evaluating a modified delay-multiply-and-sum reconstruction algorithm to improve detection of osteochondritis dissecans." Journal of the Acoustical Society of America 152, no. 4 (October 2022): A184. http://dx.doi.org/10.1121/10.0015971.

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Osteochondritis dissecans (OCD) is a focal joint defect that is prevalent among youth athletes. With the application of medical ultrasound, OCD of the humeral capitellum could be detected earlier and prevent surgery. In this work, we tested a modified Delay-Multiply-and-Sum (DMAS) reconstruction algorithm to evaluate how it affects medical ultrasound’s ability to detect OCD. Starting with the DMAS reconstruction algorithm described by Matrone et al. (2015), we modified the implementation of filtering and envelope detection steps. Delay-and-Sum (DAS), DMAS, and modified DMAS algorithms were tested on phantom and cadaveric models of capitellar OCD. The DMAS and modified DMAS images were histogram matched to the DAS image for quantitative comparison. By taking several profiles across the images of the artificial OCD lesions, we compared the lesion contrast and bone surface clarity produced by each algorithm. We found that the unmodified DMAS algorithm showed little improvement over the DAS algorithm, particularly after histogram matching. The modified DMAS algorithm showed a much greater improvement in the detection of OCD lesions compared to the DMAS algorithm. This modified DMAS algorithm could be used for other bone surface imaging applications. Future work includes evaluating these algorithms in vivo with patients diagnosed with OCD.

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33

Thulasimani, K., and K. Selvamani. "Improve Detection Rate using Modified Histogram of Gradient for Pedestrian Classification." Asian Journal of Research in Social Sciences and Humanities 6, no. 9 (2016): 500. http://dx.doi.org/10.5958/2249-7315.2016.00814.5.

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34

Kermani, Ali, Ahmad Ayatollahi, Ahmad Mirzaei, and Mohammad Barekatain. "Medical ultrasound image segmentation by modified local histogram range image method." Journal of Biomedical Science and Engineering 03, no. 11 (2010): 1078–84. http://dx.doi.org/10.4236/jbise.2010.311140.

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35

Astola, Laura, and Jaap Molenaar. "A New Modified Histogram Matching Normalization for Time Series Microarray Analysis." Microarrays 3, no. 3 (July 1, 2014): 203–11. http://dx.doi.org/10.3390/microarrays3030203.

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36

Shanmugavadivu, P. "Modified Histogram Equalization for Image Contrast Enhancement Using Particle Swarm Optimization." International Journal of Computer Science, Engineering and Information Technology 1, no. 5 (December 31, 2011): 13–27. http://dx.doi.org/10.5121/ijcseit.2011.1502.

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37

Kareem, Hana H. "Color image with Dim regions Enhancement Using Modified Histogram Equalization Algorithm." Journal of Al-Nahrain University Science 15, no. 3 (September 1, 2012): 101–11. http://dx.doi.org/10.22401/jnus.15.3.15.

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38

A. Kandeel, Amany, Alaa M. Abbas, Mohiy M. Hadhoud, and Zeiad El Saghir. "A Study of a Modified Histogram Based Fast Enhancement Algorithm (MHBFE)." Signal & Image Processing : An International Journal 5, no. 1 (February 28, 2014): 55–67. http://dx.doi.org/10.5121/sipij.2014.5105.

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39

Kandhway, Pankaj, and Ashish Kumar Bhandari. "Modified clipping based image enhancement scheme using difference of histogram bins." IET Image Processing 13, no. 10 (August 22, 2019): 1658–70. http://dx.doi.org/10.1049/iet-ipr.2019.0111.

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40

Deng, Tingting, Tong Xing, Madison K. Brod, Ye Sheng, Pengfei Qiu, Igor Veremchuk, Qingfeng Song, et al. "Discovery of high-performance thermoelectric copper chalcogenide using modified diffusion-couple high-throughput synthesis and automated histogram analysis technique." Energy & Environmental Science 13, no. 9 (2020): 3041–53. http://dx.doi.org/10.1039/d0ee02209h.

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41

DeMartinis, F. D. "Very small fat cell populations determined by a modified osmium tetroxide-urea method." American Journal of Physiology-Cell Physiology 249, no. 1 (July 1, 1985): C89—C96. http://dx.doi.org/10.1152/ajpcell.1985.249.1.c89.

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To facilitate investigations on very small fat cell (VSFC) populations in adipose tissue, an alternate method of preparing fat tissue samples was explored. The osmium tetroxide-8M urea method, modified by addition of a 95% ethanol step in tissue processing, centrifugation between steps, and final resuspension in 55% glycerol in 0.01% Triton-saline, was compared with the collagenase method for determination of VSFC populations in Fischer 344 epididymal and Sprague-Dawley retroperitoneal adipose depots. For each method and in both depots, the average histogram of 300 adipocyte diameters, measured by microscopy, was bimodal with the nadir between 30 and 40 micron diameter. The average histogram of fat cells less than 35 micron in diameter showed a separate population of VSFC existed in each depot. The modified osmium-urea method gave better results and was easier to perform than the collagenase method. It has confirmed our earlier results and raises anew questions concerning a role for the natural existence of a VSFC population in the adipose depot.

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42

Younis, Dr Basma MohammedKamal, and Dua’a Basman Younis. "Fuzzy Image Processing Based Architecture for Contrast Enhancement in Diabetic Retinopathy Images." International Journal of Computer Engineering and Information Technology 12, no. 4 (April 30, 2020): 26–30. http://dx.doi.org/10.47277/ijceit/12(4)1.

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Diabetic retinopathy” is damage to retina denotes one of the problems of diabetes which is a significant reason for visual infirmity and blindness. A comprehensive and routine eye check is important to early detection and rapid treatment. This study proposes a hardware system that can enhance the contrast in the diabetic retinopathy eye fundus images as a first step in different eye disease diagnoses. The fuzzy histogram equalization technique is proposed to increases the local contrast of Diabetic Retinopathy Images. First, a histogram construction hardware architecture for different image processing purposes has been built then modified with fuzzy techniques to create fuzzy histogram equalization architecture, which is used to enhance the original images. Both architectures are designed using a finite-state machine (FSM) technique and programmed with VHDL programming language. The first one is implemented using two (Spartan 3E-XC3S500 and Xilinx Artix-7 XC7A100T) kits, while the second architecture is implemented using (Spartan 3E-XC3S500) kit. The system consists also of a modified video graphics array (VGA) port to display the input and resulted images with a proper resolution. All the hardware outputs are compared to that results produce from MatLab for verification and the resulted images are tested by PSNR, MSE, ENTROPY ,and AMBE

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43

Chinegeram, Kalyani, Ramudu Kama, and Ganta Raghotham Reddy. "Enhancement and Segmentation of Medical Images Using AGCWD and ORACM." International Journal of Online and Biomedical Engineering (iJOE) 16, no. 13 (November 17, 2020): 45. http://dx.doi.org/10.3991/ijoe.v16i13.18501.

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<p>Images that are obtained in the real world in low contrast are inappropriate for human eyes to read the medical images. Enhancement and segmentation have an important role to play in digital image processing, pattern recognition, and the computer vision. Here, this paper presents an effective way of changing histograms and improving contrast in digital images. Segmentation is done on AGCWD enhanced images. Histogram equalization is an important technique for contrast enhancement. Nevertheless, modern Histogram Equalization commonly results in unnecessary contrast enhancement, which in turn offers an un-natural presence to the processed image and produces visual artifacts. We present an automated transformation technique that helps boost dimmed image brightness by gamma correction and weighted distribution, commonly known as Adaptive Gamma Correction Weighted Distribution (AGCWD). The contrast enhancement level can be modified using this technique; noise robustness, white or black stretching, and the protection of medium brightness can be easily integrated into the optimization process. Finally, a contrast enhancement algorithm with low complexity is introduced. All the process of enhancement will be done during the process of pre-processing the image. Later, in post-processing, we introduce a specific level set method known as ORACM for better segmentation of an enhanced AGCWD image, and it is compared with the traditional level set method.</p>

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44

S N, Lohith Raj. "Using Median-LBPH Algorithm for Real-Time Face Recognition System." International Journal for Research in Applied Science and Engineering Technology 9, no. 11 (November 30, 2021): 1497–503. http://dx.doi.org/10.22214/ijraset.2021.39038.

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Abstract: The LBPH algorithm is used ubiquitously for Face Recognition applications in modern times because of its simplicity of implementation, despite providing high accuracy and less computation time. However, in conditions of varied illumination, face expression and angles at which face images are captured, its confidence is decreased. We propose a slightly modified algorithm that considers the median of the neighbourhood pixels rather than the pixel itself to overcome this issue. This algorithm is called Median-LBPH. The grey value of every pixel is replaced by the median of all the neighbourhood pixel values. Then the features are extracted, and a histogram representing the original image is saved in the model. This model, in turn, can be used to compare with histograms obtained from the faces in real-time footage to find a potential match. This algorithm is used in an end-to-end face recognition system, a web application prototype for Law Enforcement Agencies to maintain a central criminal database shared and accessed across various departments. A live surveillance system is added as part of this novel application so that whenever an already registered criminal appears live on surveillance cameras, a notification will be received, and personnel appropriate Law Enforcement authorities will receive e-mail and text messages through a secured channel. Keywords: Face Recognition, Median-Local Binary Pattern Histogram (MLBPH), Haar Cascade, Adaboost, Neighbourhood Median

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45

Munarko, Yuda, and Agus Eko Minarno. "HII: Histogram Inverted Index For Fast Images Retrieval." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 5 (October 1, 2018): 3140. http://dx.doi.org/10.11591/ijece.v8i5.pp3140-3148.

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<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p><span>This work aims to improve the speed of search by creating an indexing structure in CBIR system. We utilised an inverted index structure that usually used in text retrieval with a modification. The modified inverted index is built based on histogram data that generated using Multi Texton Histogram (MTH) and Multi Texton Co-Occurrence Descriptor (MTCD) from 10,000 images of Corel dataset. When building the inverted index, we normalised value of each feature into a real number and considered pairs of feature and value that owned by a particular number of images. Based on our investigation, on MTCD histogram of 5,000 data test, we found that by considering histogram variable values which owned by maximum 12% of images, the number of comparison for each query can be reduced by 67.47% in a rate, the precision is 82.2%, and the rate of access to disk is 32.83%. Furthermore, we named our approach as Histogram Inverted Index (HII). </span></p></div></div></div></div></div></div>

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46

Sivasubramanian, Nandhini, Gunaseelan Konganathan, and Yeragudipati Venkata Ramana Rao. "High capacity multi-bit data hiding based on modified histogram shifting technique." ETRI Journal 40, no. 5 (September 3, 2018): 677–86. http://dx.doi.org/10.4218/etrij.2018-0089.

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47

Thamaraichelvi, Balan. "Modified fuzzy clustering-based segmentation through histogram combined with K-NN classification." International Journal of Medical Engineering and Informatics 13, no. 5 (2021): 410. http://dx.doi.org/10.1504/ijmei.2021.117730.

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48

Thamaraichelvi, Balan. "Modified fuzzy clustering-based segmentation through histogram combined with K-NN classification." International Journal of Medical Engineering and Informatics 13, no. 5 (2021): 410. http://dx.doi.org/10.1504/ijmei.2021.10041113.

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49

Dhal, Krishna Gopal, and Sanjoy Das. "Colour retinal images enhancement using modified histogram equalisation methods and firefly algorithm." International Journal of Biomedical Engineering and Technology 28, no. 2 (2018): 160. http://dx.doi.org/10.1504/ijbet.2018.094725.

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50

Dhal, Krishna Gopal, and Sanjoy Das. "Colour retinal images enhancement using modified histogram equalisation methods and firefly algorithm." International Journal of Biomedical Engineering and Technology 28, no. 2 (2018): 160. http://dx.doi.org/10.1504/ijbet.2018.10015722.

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