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Journal articles on the topic 'Laplacian of Gaussian'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 February 2022

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1

Gazor, S., and Wei Zhang. "Speech enhancement employing Laplacian-Gaussian mixture." IEEE Transactions on Speech and Audio Processing 13, no. 5 (September 2005): 896–904. http://dx.doi.org/10.1109/tsa.2005.851943.

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2

Resdiana Hutagalung. "Mendeteksi Tepi Citra Penyakit Hemokromatosis Dengan Menggunakan Metode Log (Laplacian Of Gaussian)." JUKI : Jurnal Komputer dan Informatika 2, no. 1 (May 27, 2020): 49–58. http://dx.doi.org/10.53842/juki.v2i1.28.

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Hemochromatosis is a genetic or hereditary disease. Abnormalities of iron metabolism characterized by excessive deposition of iron in the tissues. Derivative conditions that cause the body to absorb too much iron from the food eaten. Excess iron is stored in organs such as the liver, heart and pancreas. Excess iron can cause toxicity to these organs, and is life threatening because it can cause diseases such as cancer, cardiac arrhythmias, and cirrhosis. LOG method (Laplacian of Gaussian) is a second-order edge detection operator or has a derivative filter whose function can detect areas that have rapid changes (rapit change) such as edges (edges) in the image. But this laplacian is very sensitive or low to the presence of noise. For that, the image will be smoothed first by using Gaussian. Thus a new derivative function is known, namely LoG or Laplacian of Gaussian. Many methods are used in solving edge detection problems, including Prewitt Operators, Sobel Operators, Canny Operators, but among all these methods, the Laplacian of Gaussian method is the method most often used in detecting edges. For this reason, it is hoped that the LoG (Laplacian of Gaussian) method can help to detect hemochromatosis. Can help how severe the disease has developed, and what symptoms can later be caused, so that it can help in the healing process.

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3

Gibson, Jerry, and Hoontaek Oh. "Mutual Information Loss in Pyramidal Image Processing." Information 11, no. 6 (June 15, 2020): 322. http://dx.doi.org/10.3390/info11060322.

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Gaussian and Laplacian pyramids have long been important for image analysis and compression. More recently, multiresolution pyramids have become an important component of machine learning and deep learning for image analysis and image recognition. Constructing Gaussian and Laplacian pyramids consists of a series of filtering, decimation, and differencing operations, and the quality indicator is usually mean squared reconstruction error in comparison to the original image. We present a new characterization of the information loss in a Gaussian pyramid in terms of the change in mutual information. More specifically, we show that one half the log ratio of entropy powers between two stages in a Gaussian pyramid is equal to the difference in mutual information between these two stages. We show that this relationship holds for a wide variety of probability distributions and present several examples of analyzing Gaussian and Laplacian pyramids for different images.

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4

Chen, J. S., A. Huertas, and G. Medioni. "Fast Convolution with Laplacian-of-Gaussian Masks." IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-9, no. 4 (July 1987): 584–90. http://dx.doi.org/10.1109/tpami.1987.4767946.

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5

Tabbone, S. A., L. Alonso, and D. Ziou. "Behavior of the Laplacian of Gaussian Extrema." Journal of Mathematical Imaging and Vision 23, no. 1 (July 2005): 107–28. http://dx.doi.org/10.1007/s10851-005-4970-7.

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6

Singh, Meghna, Mrinal K. Mandal, and Anup Basu. "Gaussian and Laplacian of Gaussian weighting functions for robust feature based tracking." Pattern Recognition Letters 26, no. 13 (October 2005): 1995–2005. http://dx.doi.org/10.1016/j.patrec.2005.03.015.

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7

Sumaiya, M. N., and R. Shantha Selva Kumari. "Satellite Image Change Detection Using Laplacian–Gaussian Distributions." Wireless Personal Communications 97, no. 3 (August 4, 2017): 4621–30. http://dx.doi.org/10.1007/s11277-017-4741-y.

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8

Pei, Soo-Chang, and Ji-Hwei Horng. "Design of FIR bilevel Laplacian-of-Gaussian filter." Signal Processing 82, no. 4 (April 2002): 677–91. http://dx.doi.org/10.1016/s0165-1684(02)00136-6.

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9

Cho, Yongju, Dojin Kim, Saleh Saeed, Muhammad Umer Kakli, Soon-Heung Jung, Jeongil Seo, and Unsang Park. "Keypoint Detection Using Higher Order Laplacian of Gaussian." IEEE Access 8 (2020): 10416–25. http://dx.doi.org/10.1109/access.2020.2965169.

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10

He, Xiaofei, Deng Cai, Yuanlong Shao, Hujun Bao, and Jiawei Han. "Laplacian Regularized Gaussian Mixture Model for Data Clustering." IEEE Transactions on Knowledge and Data Engineering 23, no. 9 (September 2011): 1406–18. http://dx.doi.org/10.1109/tkde.2010.259.

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11

Patton, Steven W. "Robust and least‐squares estimation of acoustic attenuation from well‐log data." GEOPHYSICS 53, no. 9 (September 1988): 1225–32. http://dx.doi.org/10.1190/1.1442563.

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Several least‐squares attenuation (Q) estimation algorithms are tested on various types of models. These algorithms include the spectral ratio method and methods based upon eigenvector decomposition and Wiener filtering. The eigenvector decomposition and Wiener filter methods prove to be unsatisfactory even on model data, while the spectral ratio method yields fairly poor results. Tests indicate that the underlying Q error distribution is non‐Gaussian; hence more robust methods are needed. The errors in Q estimation have an asymptotically Cauchy distribution, with a reasonable noise model and Gaussian input noise. On noise models with Gaussian errors slightly contaminated by Cauchy or Laplacian noise, a maximum‐likelihood (ML) estimator based on Gaussian noise performed best. On heavily contaminated models, the ML estimator based on Laplacian noise performed best; but simple, robust estimators such as the median also did well. On more realistic models with noise, the median and alpha‐trimmed mean (ATM) appear to be the best.

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12

Shi, Luyao, Yang Chen, Wenlong Yuan, Libo Zhang, BenQiang Yang, Huazhong Shu, Limin Luo, and Jean-Louis Coatrieux. "Comparative Analysis of Median and Average Filters in Impulse Noise Suppression." Fluctuation and Noise Letters 14, no. 01 (December 25, 2014): 1550002. http://dx.doi.org/10.1142/s0219477515500029.

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Median type filters coupled with the Laplacian distribution assumption have shown a high efficiency in suppressing impulse noise. We however demonstrate in this paper that the Gaussian distribution assumption is more preferable than Laplacian distribution assumption in suppressing impulse noise, especially for high noise densities. This conclusion is supported by numerical experiments with different noise densities and filter models.

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13

WANG, HAIJING, PEIHUA LI, and TIANWEN ZHANG. "BOOSTED GAUSSIAN CLASSIFIER WITH INTEGRAL HISTOGRAM FOR FACE DETECTION." International Journal of Pattern Recognition and Artificial Intelligence 21, no. 07 (November 2007): 1127–39. http://dx.doi.org/10.1142/s0218001407005880.

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Novel features and weak classifiers are proposed for face detection within the AdaBoost learning framework. Features are histograms computed from a set of spatial templates in filtered images. The filter banks consist of Intensity, Laplacian of Gaussian (Difference of Gaussians), and Gabor filters, aiming to capture spatial and frequency properties of faces at different scales and orientations. Features selected by AdaBoost learning, each of which corresponds to a histogram with a pair of filter and template, can thus be interpreted as boosted marginal distributions of faces. We fit the Gaussian distribution of each histogram feature only for positives (faces) in the sample set as the weak classifier. The results of the experiment demonstrate that classifiers with corresponding features are more powerful in describing the face pattern than haar-like rectangle features introduced by Viola and Jones.

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14

Dorafshan, Thomas, and Maguire. "Benchmarking Image Processing Algorithms for Unmanned Aerial System-Assisted Crack Detection in Concrete Structures." Infrastructures 4, no. 2 (April 30, 2019): 19. http://dx.doi.org/10.3390/infrastructures4020019.

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This paper summarizes the results of traditional image processing algorithms for detection of defects in concrete using images taken by Unmanned Aerial Systems (UASs). Such algorithms are useful for improving the accuracy of crack detection during autonomous inspection of bridges and other structures, and they have yet to be compared and evaluated on a dataset of concrete images taken by UAS. The authors created a generic image processing algorithm for crack detection, which included the major steps of filter design, edge detection, image enhancement, and segmentation, designed to uniformly compare different edge detectors. Edge detection was carried out by six filters in the spatial (Roberts, Prewitt, Sobel, and Laplacian of Gaussian) and frequency (Butterworth and Gaussian) domains. These algorithms were applied to fifty images each of defected and sound concrete. Performances of the six filters were compared in terms of accuracy, precision, minimum detectable crack width, computational time, and noise-to-signal ratio. In general, frequency domain techniques were slower than spatial domain methods because of the computational intensity of the Fourier and inverse Fourier transformations used to move between spatial and frequency domains. Frequency domain methods also produced noisier images than spatial domain methods. Crack detection in the spatial domain using the Laplacian of Gaussian filter proved to be the fastest, most accurate, and most precise method, and it resulted in the finest detectable crack width. The Laplacian of Gaussian filter in spatial domain is recommended for future applications of real-time crack detection using UAS.

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15

SMOLYANOV, O. G., and H. v. WEIZSÄCKER. "SMOOTH PROBABILITY MEASURES AND ASSOCIATED DIFFERENTIAL OPERATORS." Infinite Dimensional Analysis, Quantum Probability and Related Topics 02, no. 01 (March 1999): 51–78. http://dx.doi.org/10.1142/s0219025799000047.

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We compare different notions of differentiability of a measure along a vector field on a locally convex space. We consider in the L2-space of a differentiable measure the analog of the classical concepts of gradient, divergence and Laplacian (which coincides with the Ornstein–Uhlenbeck operator in the Gaussian case). We use these operators for the extension of the basic results of Malliavin and Stroock on the smoothness of finite dimensional image measures under certain nonsmooth mappings to the case of non-Gaussian measures. The proof of this extension is quite straight forward and does not use any Chaos-decomposition. Finally, the role of this Laplacian in the procedure of quantization of anharmonic oscillators is discussed.

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16

Wang, Feng, Gui Tang Wang, Rui Huang Wang, and Xiao Wu Huang. "FPGA Implementation of Laplacian of Gaussian Edge Detection Algorithm." Advanced Materials Research 282-283 (July 2011): 157–60. http://dx.doi.org/10.4028/www.scientific.net/amr.282-283.157.

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This paper introduces a design of gaussian Laplace edge detection algorithm model based on system generator which can be realized in FPGA.The data of a two- dimensional image was changed into a one-dimensional array，before line buffering in two Dual port RAM,the convolution of the image pixel data and the LOG template was carried out in the modules constituted of the component elements such as AddSub, Shift and Delay . After getting the absolute value with the modules of Slice，Negate and Mux ，the output was the image after edge-detection .The module function and the selecting principle was analyzed from the point of view of saving FPGA resources.The WaveScope and resource estimator showed that ：not only the detection result and the running speed was guaranteed but also the FPGA resources can be saved .

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17

Gunn, Steve R. "On the discrete representation of the Laplacian of Gaussian." Pattern Recognition 32, no. 8 (August 1999): 1463–72. http://dx.doi.org/10.1016/s0031-3203(98)00163-0.

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18

Wang, Zhong-Yuan, Zhen Han, Rui-Min Hu, and Jun-Jun Jiang. "Noise robust face hallucination employing Gaussian–Laplacian mixture model." Neurocomputing 133 (June 2014): 153–60. http://dx.doi.org/10.1016/j.neucom.2013.11.021.

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19

Kong, Hui, Sanjay E. Sarma, and Feng Tang. "Generalizing Laplacian of Gaussian Filters for Vanishing-Point Detection." IEEE Transactions on Intelligent Transportation Systems 14, no. 1 (March 2013): 408–18. http://dx.doi.org/10.1109/tits.2012.2216878.

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20

Neycenssac, F. "Contrast Enhancement Using the Laplacian-of-a-Gaussian Filter." CVGIP: Graphical Models and Image Processing 55, no. 6 (November 1993): 447–63. http://dx.doi.org/10.1006/cgip.1993.1034.

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21

Kamejima, Kohji. "Laplacian-Gaussian Sub-Correlation Analysis for Scale Space Imaging." Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications 2005 (May 5, 2005): 277–82. http://dx.doi.org/10.5687/sss.2005.277.

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22

Bogdanskii, Yu V., and Ya Yu Sanzharevskii. "Laplacian Generated by the Gaussian Measure and Ergodic Theorem." Ukrainian Mathematical Journal 67, no. 9 (February 2016): 1316–26. http://dx.doi.org/10.1007/s11253-016-1155-z.

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23

Mukherjee, D. "Parameter Selection for Wyner–Ziv Coding of Laplacian Sources with Additive Laplacian or Gaussian Innovation." IEEE Transactions on Signal Processing 57, no. 8 (August 2009): 3208–25. http://dx.doi.org/10.1109/tsp.2009.2018617.

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24

Huang, Mingming, Zhichun Mu, Hui Zeng, and Hongbo Huang. "A Novel Approach for Interest Point Detection via Laplacian-of-Bilateral Filter." Journal of Sensors 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/685154.

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Scale-invariant feature transform (SIFT) algorithm, one of the most famous and popular interest point detectors, detects extrema by using difference-of-Gaussian (DoG) filter which is an approximation to the Laplacian-of-Gaussian (LoG) for improving speed. However, DoG filter has a strong response along edge, even if the location along the edge is poorly determined and therefore is unstable to small amounts of noise. In this paper, we propose a novel interest point detection algorithm, which detects scale space extrema by using a Laplacian-of-Bilateral (LoB) filter. The LoB filter, which is produced by Bilateral and Laplacian filter, can preserve edge characteristic by fully utilizing the information of intensity variety. Compared with the SIFT algorithm, our algorithm substantially improves the repeatability of detected interest points on a very challenging benchmark dataset, in which images were generated under different imaging conditions. Extensive experimental results show that the proposed approach is more robust to challenging problems such as illumination and viewpoint changes, especially when encountering large illumination change.

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25

Bhadoria, Ajeeta Singh, and Vandana Vikas Thakre. "Improved Single Haze Removal using Weighted Filter and Gaussian-Laplacian." International Journal of Electrical and Electronics Research 8, no. 2 (June 30, 2020): 26–31. http://dx.doi.org/10.37391/ijeer.080201.

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Generally computer applications use digital images. Digital image plays a vital role in the analysis and explanation of data, which is in the digital form. Images and videos of outside scenes are generally affected by the bad weather environment such as haze, fog, mist etc. It will result in bad visibility of the scene caused by the lack of quality. This paper exhibits a study about various image defogging techniques to eject the haze from the fog images caught in true world to recuperate a fast and enhanced nature of fog free images. In this paper, we propose a simple but effective the weighted median (WM) filter was first presented as an overview of the standard median filter, where a nonnegative integer weight is assigned to each position in the filter window image .Gaussian and laplacian pyramids are applying Gaussian and laplacian filter in an image in cascade order with different kernel sizes of gaussian and laplacian filter .The dark channel prior is a type of statistics of the haze-free outdoor images. It is based on a key observation - most local patches in haze-free outdoor images contain some pixels which have very low intensities in at least one-color channel. Using this prior with the haze imaging model, we can directly estimate the thickness of the haze and recover a high-quality haze-free image. Results on a variety of outdoor haze images demonstrate the power of the proposed prior. Moreover, a high-quality depth map can also be obtained as a by-product of haze removal and Calculate the PSNR and MSE of three sample images.

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26

Krupiński, Robert. "Reconstructed Quantized Coefficients Modeled with Generalized Gaussian Distribution with Exponent 1/3." Image Processing & Communications 21, no. 4 (December 1, 2016): 5–12. http://dx.doi.org/10.1515/ipc-2016-0019.

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Abstract Generalized Gaussian distribution (GGD) includes specials cases when the shape parameter equals p = 1 and p = 2. It corresponds to Laplacian and Gaussian distributions respectively. For p → ∞, f(x) becomes a uniform distribution, and for p → 0, f(x) approaches an impulse function. Chapeau-Blondeau et al. [4] considered another special case p = 0.5. The article discusses more peaky case in which GGD p = 1/3.

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Ji, Chang Peng, Mo Gao, and Jie Yang. "Voice Activity Detection Based on Multiple Statistical Models." Advanced Materials Research 181-182 (January 2011): 765–69. http://dx.doi.org/10.4028/www.scientific.net/amr.181-182.765.

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One of the key issues in practical speech processing is to achieve robust voice activity detection (VAD) against the background noise. Most of the statistical model-based approaches have tried to employ the Gaussian assumption in the discrete Fourier transform (DFT) domain, which, however, deviates from the real observation. For a class of VAD algorithms based on Gaussian model and Laplacian model, we incorporate complex Laplacian probability density function to our analysis of statistical properties. Since the statistical characteristics of the speech signal are differently affected by the noise types and levels, to cope with the time-varying environments, our approach is aimed at finding adaptively an appropriate statistical model in an online fashion. The performance of the proposed VAD approaches in stationary noise environment is evaluated with the aid of an objective measure.

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28

Wang, Gang, Carlos Lopez-Molina, and Bernard De Baets. "Automated blob detection using iterative Laplacian of Gaussian filtering and unilateral second-order Gaussian kernels." Digital Signal Processing 96 (January 2020): 102592. http://dx.doi.org/10.1016/j.dsp.2019.102592.

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29

de Castro, J., F. Ballesteros, A. Méndez, and A. M. Tarquis. "Fractal Analysis of Laplacian Pyramidal Filters Applied to Segmentation of Soil Images." Scientific World Journal 2014 (2014): 1–13. http://dx.doi.org/10.1155/2014/212897.

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The laplacian pyramid is a well-known technique for image processing in which local operators of many scales, but identical shape, serve as the basis functions. The required properties to the pyramidal filter produce a family of filters, which is unipara metrical in the case of the classical problem, when the length of the filter is 5. We pay attention to gaussian and fractal behaviour of these basis functions (or filters), and we determine the gaussian and fractal ranges in the case of single parametera. These fractal filters loose less energy in every step of the laplacian pyramid, and we apply this property to get threshold values for segmenting soil images, and then evaluate their porosity. Also, we evaluate our results by comparing them with the Otsu algorithm threshold values, and conclude that our algorithm produce reliable test results.

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30

Akhaee, Mohammad Ali, Nima Khademi Kalantari, and Farokh Marvasti. "Robust audio and speech watermarking using Gaussian and Laplacian modeling." Signal Processing 90, no. 8 (August 2010): 2487–97. http://dx.doi.org/10.1016/j.sigpro.2010.02.013.

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31

Xu, Hongming, Cheng Lu, Richard Berendt, Naresh Jha, and Mrinal Mandal. "Automatic Nuclei Detection Based on Generalized Laplacian of Gaussian Filters." IEEE Journal of Biomedical and Health Informatics 21, no. 3 (May 2017): 826–37. http://dx.doi.org/10.1109/jbhi.2016.2544245.

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32

Zhang, Ge, Xiaoqiang Yan, Yulong Xu, and Yangdong Ye. "Neural guided visual slam system with Laplacian of Gaussian operator." IET Computer Vision 15, no. 3 (March 10, 2021): 181–96. http://dx.doi.org/10.1049/cvi2.12022.

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33

Alhevaz, Abdollah, Maryam Baghipur, and Yilun Shang. "On Generalized Distance Gaussian Estrada Index of Graphs." Symmetry 11, no. 10 (October 11, 2019): 1276. http://dx.doi.org/10.3390/sym11101276.

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For a simple undirected connected graph G of order n, let D ( G ) , D L ( G ) , D Q ( G ) and T r ( G ) be, respectively, the distance matrix, the distance Laplacian matrix, the distance signless Laplacian matrix and the diagonal matrix of the vertex transmissions of G. The generalized distance matrix D α ( G ) is signified by D α ( G ) = α T r ( G ) + ( 1 - α ) D ( G ) , where α ∈ [ 0 , 1 ] . Here, we propose a new kind of Estrada index based on the Gaussianization of the generalized distance matrix of a graph. Let ∂ 1 , ∂ 2 , … , ∂ n be the generalized distance eigenvalues of a graph G. We define the generalized distance Gaussian Estrada index P α ( G ) , as P α ( G ) = ∑ i = 1 n e - ∂ i 2 . Since characterization of P α ( G ) is very appealing in quantum information theory, it is interesting to study the quantity P α ( G ) and explore some properties like the bounds, the dependence on the graph topology G and the dependence on the parameter α . In this paper, we establish some bounds for the generalized distance Gaussian Estrada index P α ( G ) of a connected graph G, involving the different graph parameters, including the order n, the Wiener index W ( G ) , the transmission degrees and the parameter α ∈ [ 0 , 1 ] , and characterize the extremal graphs attaining these bounds.

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34

Lee, Seohyung, and Daeho Lee. "Fusion of IR and Visual Images Based on Gaussian and Laplacian Decomposition Using Histogram Distributions and Edge Selection." Mathematical Problems in Engineering 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/3130681.

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We propose a novel fusion method of IR (infrared) and visual images to combine distinct information from two sources. To decompose an image into its low and high frequency components, we use Gaussian and Laplacian decomposition. The strong high frequency information in the two sources can be easily fused by selecting the large magnitude of Laplacian images. The distinct low frequency information, however, is not as easily determined. As such, we use histogram distributions of the two sources. Therefore, experimental results show that the fused images can contain the dominant characteristics of both sources.

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35

Botelho, Luiz C. L. "Some Comments on Rigorous Finite-Volume Euclidean Quantum Field Path Integrals in the Analytical Regularization Scheme." Advances in Mathematical Physics 2011 (2011): 1–14. http://dx.doi.org/10.1155/2011/257916.

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Through the systematic use of the Minlos theorem on the support of cylindrical measures on , we produce several mathematically rigorous finite-volume euclidean path integrals in interacting euclidean quantum fields with Gaussian free measures defined by generalized powers of finite-volume Laplacian operator.

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36

Kittisuwan, P. "Textural Image Denoising Using Gumbel Random Vectors in Gaussian Noise." International Journal of Image and Graphics 17, no. 01 (January 2017): 1750003. http://dx.doi.org/10.1142/s0219467817500036.

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Gaussian noise is an important problem in computer vision. The novel methods that become popular in recent years for Gaussian noise reduction are Bayesian techniques in wavelet domain. In wavelet domain, the Bayesian techniques require a prior distribution of wavelet coefficients. In general case, the wavelet coefficients might be better modeled by non-Gaussian density such as Laplacian, two-sided gamma, and Pearson type VII densities. However, statistical analysis of textural image is Gaussian model. So, we require flexible model between non-Gaussian and Gaussian models. Indeed, Gumbel density is a suitable model. So, we present new Bayesian estimator for Gumbel random vectors in AWGN (additive white Gaussian noise). The proposed method is applied to dual-tree complex wavelet transform (DT-CWT) as well as orthogonal discrete wavelet transform (DWT). The simulation results show that our proposed methods outperform the state-of-the-art methods qualitatively and quantitatively.

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37

AGISHTEIN, M. E., R. BENAV, A. A. MIGDAL, and S. SOLOMON. "NUMERICAL STUDY OF A TWO-POINT CORRELATION FUNCTION AND LIOUVILLE FIELD PROPERTIES IN TWO-DIMENSIONAL QUANTUM GRAVITY." Modern Physics Letters A 06, no. 12 (April 20, 1991): 1115–31. http://dx.doi.org/10.1142/s0217732391001172.

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Two-point Green’s function is measured on the manifolds of a 2-dimensional quantum gravity. The recursive sampling technique is used to generate the triangulations, lattice sizes being up to hundred thousand triangles. The grid Laplacian was inverted by means of the algebraic multi-grid solver. The free field model of the Quantum Gravity assumes the Gaussian behavior of Liouville field and curvature. We measured histograms as well as six momenta of these fields. Our results support the Gaussian assumption.

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38

Wu, Shibin, Shaode Yu, Yuhan Yang, and Yaoqin Xie. "Feature and Contrast Enhancement of Mammographic Image Based on Multiscale Analysis and Morphology." Computational and Mathematical Methods in Medicine 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/716948.

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A new algorithm for feature and contrast enhancement of mammographic images is proposed in this paper. The approach bases on multiscale transform and mathematical morphology. First of all, the Laplacian Gaussian pyramid operator is applied to transform the mammography into different scale subband images. In addition, the detail or high frequency subimages are equalized by contrast limited adaptive histogram equalization (CLAHE) and low-pass subimages are processed by mathematical morphology. Finally, the enhanced image of feature and contrast is reconstructed from the Laplacian Gaussian pyramid coefficients modified at one or more levels by contrast limited adaptive histogram equalization and mathematical morphology, respectively. The enhanced image is processed by global nonlinear operator. The experimental results show that the presented algorithm is effective for feature and contrast enhancement of mammogram. The performance evaluation of the proposed algorithm is measured by contrast evaluation criterion for image, signal-noise-ratio (SNR), and contrast improvement index (CII).

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39

Guo, Shiping, Hongqiang Lv, Yongyi Liu, Rongzhi Zhang, and Jisheng Li. "Multichannel Parallel Deblurring and Collaborative Registration Using Gaussian Total Variation Regularization for Image Fusion." Mathematical Problems in Engineering 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/9491326.

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We focus on the multichannel image fusion problem for the purpose of reaching the diffraction-limited resolution of turbulence-degraded images observed by multiple acquisition channels. A hybrid strategy consisting of multichannel parallel deblurring followed by collaborative registration is developed for the final fusion. In particular, a Gaussian total variation regularization scheme taking advantage of low-order Gaussian derivative operators is proposed, which integrates the deblurring and registration problems into a unified mathematical formalization. Specifically, the gradient magnitude of Gaussian operator is proposed to define the total variation norm, and the Laplacian of Gaussian operator is used to adjust the regularization parameter when searching the extremum in each iterative step. In addition, the coordination technique involving the regularization parameter among different channels is also considered.

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40

Xu, Pengfei, Tianhao Cui, and Lei Chen. "ANLoC: An Anomaly-Aware Node Localization Algorithm for WSNs in Complex Environments." Sensors 19, no. 8 (April 23, 2019): 1912. http://dx.doi.org/10.3390/s19081912.

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Accurate and sufficient node location information is crucial for Wireless Sensor Networks (WSNs) applications. However, the existing range-based localization methods often suffer from incomplete and detorted range measurements. To address this issue, some methods based on low-rank matrix recovery have been proposed, which usually assume noises follow single Gaussian distribution or/and single Laplacian distribution, and thus cannot handle the case with wider noise distributions beyond Gaussian and Laplacian ones. In this paper, a novel Anomaly-aware Node Localization (ANLoC) method is proposed to simultaneously impute missing range measurements and detect node anomaly in complex environments. Specifically, by utilizing inherent low-rank property of Euclidean Distance Matrix (EDM), we formulate range measurements imputation problem as a Robust ℓ 2 , 1 -norm Regularized Matrix Decomposition (RRMD) model, where complex noise is fitted by Mixture of Gaussian (MoG) distribution, and node anomaly is sifted by ℓ 2 , 1 -norm regularization. Meanwhile, an efficient optimization algorithm is designed to solve proposed RRMD model based on Expectation Maximization (EM) method. Furthermore, with the imputed EDM, all unknown nodes can be easily positioned by using Multi-Dimensional Scaling (MDS) method. Finally, some experiments are designed to evaluate performance of the proposed method, and experimental results demonstrate that our method outperforms three state-of-the-art node localization methods.

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41

Yadav, Sushma. "Loss of Calculation in Nature Disaster by using Laplacian of Gaussian." International Journal of Computer Applications 114, no. 13 (March 18, 2015): 33–35. http://dx.doi.org/10.5120/20042-1894.

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Huertas, Andres, and Gerard Medioni. "Detection of Intensity Changes with Subpixel Accuracy Using Laplacian-Gaussian Masks." IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-8, no. 5 (September 1986): 651–64. http://dx.doi.org/10.1109/tpami.1986.4767838.

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Argenti, Fabrizio, Tiziano Bianchi, Alessandro Lapini, and Luciano Alparone. "Fast MAP Despeckling Based on Laplacian–Gaussian Modeling of Wavelet Coefficients." IEEE Geoscience and Remote Sensing Letters 9, no. 1 (January 2012): 13–17. http://dx.doi.org/10.1109/lgrs.2011.2158798.

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Gazor, S., and Wei Zhang. "A soft voice activity detector based on a laplacian-gaussian model." IEEE Transactions on Speech and Audio Processing 11, no. 5 (September 2003): 498–505. http://dx.doi.org/10.1109/tsa.2003.815518.

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SUHAMA, Tomoyuki, and Naoka OGAYA. "Study for the topographic representation method using Laplacian of Gaussian filtering." Journal of the Japan society of photogrammetry and remote sensing 51, no. 6 (2013): 370–74. http://dx.doi.org/10.4287/jsprs.51.370.

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Muranaka, Noriaki, Shinya Kudoh, Takanori Ashida, Masataka Tokumaru, and Shigeru Imanishi. "Multiple-valued image-contour extraction method using a Laplacian– Gaussian filter." Systems and Computers in Japan 38, no. 8 (2007): 61–71. http://dx.doi.org/10.1002/scj.10467.

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Cai, Qingpo, Jian Kang, and Tianwei Yu. "Bayesian Network Marker Selection via the Thresholded Graph Laplacian Gaussian Prior." Bayesian Analysis 15, no. 1 (March 2020): 79–102. http://dx.doi.org/10.1214/18-ba1142.

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Joshi, R. L., and T. R. Fischer. "Comparison of generalized Gaussian and Laplacian modeling in DCT image coding." IEEE Signal Processing Letters 2, no. 5 (May 1995): 81–82. http://dx.doi.org/10.1109/97.386283.

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Ou, Shifeng, Peng Song, and Ying Gao. "Soft Decision Based Gaussian-Laplacian Combination Model for Noisy Speech Enhancement." Chinese Journal of Electronics 27, no. 4 (July 1, 2018): 827–34. http://dx.doi.org/10.1049/cje.2018.05.015.

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Zhan, Qingming, Yubin Liang, Ying Cai, and Yinghui Xiao. "Pattern detection in airborne LiDAR data using Laplacian of Gaussian filter." Geo-spatial Information Science 14, no. 3 (January 2011): 184–89. http://dx.doi.org/10.1007/s11806-011-0540-x.

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