Готові списки джерел за темами / Histogram correction

Добірка наукової літератури з теми "Histogram correction"

Автор: Grafiati

Опубліковано: 30 травня 2022

Оновлено: 31 травня 2022

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Статті в журналах з теми "Histogram correction"

Gao, Hui-ting, Wei Liu, Hong-yan He, Bing-xian Zhang, and Cheng Jiang. "DE-STRIPING FOR TDICCD REMOTE SENSING IMAGE BASED ON STATISTICAL FEATURES OF HISTOGRAM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 3, 2016): 311–16. http://dx.doi.org/10.5194/isprsarchives-xli-b1-311-2016.

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Aim to striping noise brought by non-uniform response of remote sensing TDI CCD, a novel de-striping method based on statistical features of image histogram is put forward. By analysing the distribution of histograms,the centroid of histogram is selected to be an eigenvalue representing uniformity of ground objects,histogrammic centroid of whole image and each pixels are calculated first,the differences between them are regard as rough correction coefficients, then in order to avoid the sensitivity caused by single parameter and considering the strong continuity and pertinence of ground objects between two adjacent pixels,correlation coefficient of the histograms is introduces to reflect the similarities between them,fine correction coefficient is obtained by searching around the rough correction coefficient,additionally,in view of the influence of bright cloud on histogram,an automatic cloud detection based on multi-feature including grey level,texture,fractal dimension and edge is used to pre-process image.Two 0-level panchromatic images of SJ-9A satellite with obvious strip noise are processed by proposed method to evaluate the performance, results show that the visual quality of images are improved because the strip noise is entirely removed,we quantitatively analyse the result by calculating the non-uniformity ,which has reached about 1% and is better than histogram matching method.

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Hildebolt, C. F., R. K. Walkup, G. L. Conover, N. Yokoyama-Crothers, T. Q. Bartlett, M. W. Vannier, M. K. Shrout, and J. J. Camp. "Histogram-matching and histogram-flattening contrast correction methods: a comparison." Dentomaxillofacial Radiology 25, no. 1 (January 1996): 42–47. http://dx.doi.org/10.1259/dmfr.25.1.9084285.

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Zhang, Jing, and Guang Xue Chen. "Research on the Color Correction Algorithm of Images Based on Histogram Matching." Applied Mechanics and Materials 469 (November 2013): 256–59. http://dx.doi.org/10.4028/www.scientific.net/amm.469.256.

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Different rendering conditions (e.g., changes in lighting conditions or atmospheric conditions, changes of the imaging system) often cause significant color differences between two images. In the prepress process, the brightness and hue between two images should be adjusted to be as similar as possible. Currently, we generally use image processing software such as PhotoShop to adjust the image manually, it’s complex and time consuming. In this paper, the color correction algorithm based on histogram matching was put forward and implemented. Only one image needed to be adjusted well previously as the reference image, and the mapping relationship was established on pixels between the histogram of the source images and the reference image, then the source images would have the histograms similar to that of the reference image, so that the images would have similar color characteristic and achieve image color correction finally. The experimental result showed that the realized color correction algorithm was effective, it could not only maintain the visual effect of images, but also eliminate the color differences between the reference image and the source images.

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Stolk, Ad, Torbjörn E. Törnqvist, Kilian P. V. Hekhuis, Henk J. A. Berendsen, and Johannes van der Plicht. "Calibration of 14C Histograms: A Comparison of Methods." Radiocarbon 36, no. 1 (1994): 1–10. http://dx.doi.org/10.1017/s0033822200014272.

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The interpretation of 14C histograms is complicated by the non-linearity of the 14C time scale in terms of calendar years, which may result in clustering of 14C ages in certain time intervals unrelated to the (geologic or archaeologic) phenomenon of interest. One can calibrate 14C histograms for such distortions using two basic approaches. The KORHIS method constructs a 14C histogram before calibration is performed by means of a correction factor. We present the CALHIS method based on the Groningen calibration program for individual 14C ages. CALHIS first calibrates single 14C ages and then sums the resulting calibration distributions, thus yielding a calibrated 14C histogram. The individual calibration distributions are normalized to a standard Gaussian distribution before superposition, thus allowing direct comparison among various 14C histograms. Several experiments with test data sets demonstrate that CALHIS produces significantly better results than KORHIS. Although some problems remain (part of the distortions due to 14C variations cannot be eliminated), we show that CALHIS offers good prospects for using 14C histograms, particularly with highly precise and accurate 14C ages.

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Wand, M. P. "Correction: Data-Based Choice of Histogram Bin Width." American Statistician 53, no. 2 (May 1999): 174. http://dx.doi.org/10.2307/2685743.

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Kholmovski, Eugene G., Andrew L. Alexander, and Dennis L. Parker. "Correction of slab boundary artifact using histogram matching." Journal of Magnetic Resonance Imaging 15, no. 5 (April 26, 2002): 610–17. http://dx.doi.org/10.1002/jmri.10094.

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Yi, Zeguang, Nan Pan, Yi Liu, and Yu Guo. "Study of laser displacement measurement data abnormal correction algorithm." Engineering Computations 34, no. 1 (March 6, 2017): 123–33. http://dx.doi.org/10.1108/ec-10-2015-0325.

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Purpose This paper aims to reduce and eliminate the abnormal peaks which, because of the reflection in the process of laser detection, make it easier to proceed with further analysis. Design/methodology/approach To solve the above problem, an abnormal data correction algorithm based on histogram, K-Means clustering and improved robust locally weighted scatter plot smoothing (LOWESS) is put forward. The proposed algorithm does section leveling for shear plant first and then applies histogram to define the abnormal fluctuation data between the neighboring points and utilizes a K-Means clustering to eliminate the abnormal data. After that, the improved robust LOWESS method, which is based on Euclidean distance, is used to remove the noise interference and finally obtain the waveform characteristics for next data processing. Findings The experiment result of liner tool mark laser test data correction demonstrates the accuracy and reliability of the proposed algorithm. Originality/value The study enables the following points: the detection signal automatic leveling; abnormal data identification and demarcation using K-Means clustering and histogram; and data smoothing using LOWESS.

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Kim, Seaho, and Hiseok Kim. "Luminance Correction for Stereo Images using Histogram Interval Calibration." Journal of the Institute of Electronics Engineers of Korea 50, no. 12 (December 25, 2013): 159–67. http://dx.doi.org/10.5573/ieek.2013.50.12.159.

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Shuang, Zhang, Jin Gang, and Qin Yu-ping. "Gray Imaging Extended Target Tracking Histogram Matching Correction Method." Procedia Engineering 15 (2011): 2255–59. http://dx.doi.org/10.1016/j.proeng.2011.08.422.

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Дисертації з теми "Histogram correction"

Gatti, Pruthvi Venkatesh, and Krishna Teja Velugubantla. "Contrast Enhancement of Colour Images using Transform Based Gamma Correction and Histogram Equalization." Thesis, Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-14424.

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Contrast is an important factor in any subjective evaluation of image quality. It is the difference in visual properties that makes an object distinguishable from other objects and background. Contrast Enhancement method is mainly used to enhance the contrast in the image by using its Histogram. Histogram is a distribution of numerical data in an image using graphical representation. Histogram Equalization is widely used in image processing to adjust the contrast in the image using histograms. Whereas Gamma Correction is often used to adjust luminance in an image. By combining Histogram Equalization and Gamma Correction we proposed a hybrid method, that is used to modify the histograms and enhance contrast of an image in a digital method. Our proposed method deals with the variants of histogram equalization and transformed based gamma correction. Our method is an automatically transformation technique that improves the contrast of dimmed images via the gamma correction and probability distribution of luminance pixels. The proposed method is converted into an android application. We succeeded in enhancing the contrast of an image by using our method and we have tested for different alpha values. Graphs of the gamma for different alpha values are plotted.

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Jirka, Roman. "Časosběrné video." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2011. http://www.nusl.cz/ntk/nusl-236934.

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This thesis deals with the introduction into the topic of time-lapse video creation. It focuses on cases where tripod is not used and therefore it is necessary to eliminate incurred shortcomings. The main shortcomings are different position of individual frames, different brightness and color adjustment. The next topic describes which principles should be followed during the creation process. Thesis describes and implements methods for elimination of main shortcomings during process long time-lapse videos, which are recorded by hand. Thesis also precisely describes image registration, correction of brightness and colors. Thesis is also considers histograms comparison. Result of this work is application, which eliminates problems described above.

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Аврунін, О. Г., V. Klymenko, A. Trubitcin, and O. Isaeva. "Development of Automated System for Video Intermatoscopy." Thesis, RS Global Sp. z O.O. Warsaw, Poland, 2019. http://openarchive.nure.ua/handle/document/7884.

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The work is devoted to the development of an automated system for video dermatoscopy. The main approaches in the design of such systems, methodological support and structural modules are considered. The main stages of the digital processing of dermatoscopic images are proposed. The main focus is on the methods of automated segmentation of dermatoscopy images and the analysis of the errors that arise. The processing of heterogeneous medical data requires an integrated approach aimed at developing complete specialized diagnostic systems taking into account the specifics of a particular area and the nature of the images obtained. The stages of preprocessing, segmentation, description, and analysis of digital dermatoscopy images are considered in detail related to each other. The diagnostic capabilities of the digital dermatoscopy method are discussed. The main aspects of the automated analysis of the processing of dermatoscopic images and the prospects for the use of such systems in medical practice are considered.

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Nilsson, Linus. "Quality and real-time performance assessment of color-correction methods : A comparison between histogram-based prefiltering and global color transfer." Thesis, Mittuniversitetet, Avdelningen för informationssystem och -teknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-33877.

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In the field of computer vision and more specifically multi-camera systems color correction is an important topic of discussion. The need for color-tone similarity among multiple images that are used to construct a single scene is self-evident. The strength and weaknesses of color- correction methods can be assessed by using metrics to measure structural and color-tone similarity and timing the methods. Color transfer has a better structural similarity than histogram-based prefiltering and a worse color-tone similarity. The color transfer method is faster than the histogram-based prefiltering. Color transfer is a better method if the focus is a structural similar image after correction, if better color-tone similarity at the cost of structural similarity is acceptable histogram-based prefiltering is a better choice. Color transfer is a faster method and is easier to run with a parallel computing approach then histogram-based prefiltering. Color transfer might therefore be a better pick for real-time applications. There is however more room to optimize an implementation of histogram-based prefiltering utilizing parallel computing.

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Pehrson, Skidén Ottar. "Automatic Exposure Correction And Local Contrast Setting For Diagnostic Viewing of Medical X-ray Images." Thesis, Linköping University, Department of Biomedical Engineering, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-56630.

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To properly display digital X-ray images for visual diagnosis, a proper display range needs to be identified. This can be difficult when the image contains collimators or large background areas which can dominate the histograms. Also, when there are both underexposed and overexposed areas in the image it is difficult to display these properly at the same time. The purpose of this thesis is to find a way to solve these problems. A few different approaches are evaluated to find their strengths and weaknesses. Based on Local Histogram Equalization, a new method is developed to put various constraints on the mapping. These include alternative ways to perform the histogram calculations and how to define the local histograms. The new method also includes collimator detection and background suppression to keep irrelevant parts of the image out of the calculations. Results show that the new method enables proper display of both underexposed and overexposed areas in the image simultaneously while maintaining the natural look of the image. More testing is required to find appropriate parameters for various image types.

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Langhi, Paula J. Piloto. "Ajuste radiométrico de imagens aéreas digitais /." Presidente Prudente : [s.n.], 2009. http://hdl.handle.net/11449/86772.

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Orientador: Antonio Maria Garcia Tommaselli
Banca: Aluir Porfírio Dal Poz
Banca: Edson Aparecido Mitishita
Resumo: As diferenças radiométricas entre áreas homólogas de imagens aéreas digitais são causadas por vários fatores e afetam os processos fotogramétricos, como a correspondência e a mosaicagem. Para que as imagens possam ser melhoradas, alguns procedimentos como correção geométrica e radiométrica são aplicados, sendo a correção radiométrica o tema de estudo deste trabalho. O processo de correção radiométrica em imagens aéreas digitais tem como principal objetivo tornar semelhantes os histogramas de imagens que possuam sobreposição, facilitando os processos posteriores de correspondência de pontos homólogos e a formação de mosaicos. O método proposto neste trabalho realiza em duas etapas a correção radiométrica para um bloco de imagens aéreas digitais. A primeira etapa é um processamento global, baseado nos dados dos histogramas. A segunda etapa é uma correção local, que pode ser realizada através de dois métodos estudados, sendo que o primeiro trata a interpolação dos valores de correção à partir de regiões homólogas, possibilitando a correção radiométrica da região de sobreposição das imagens; e o segundo método utiliza o ajuste de uma superfície parabolóide às diferenças radiométricas entre imagens, fornecendo, posteriormente, valores de correção para toda a imagem. Para realizar os processos descritos acima, foi implementado um programa em linguagem C, o qual realiza o ajuste local a partir de pontos correspondentes. Alguns experimentos foram realizados, utilizando três blocos de imagens aéreas obtidas com câmeras digitais Hasselblad de 22 e 39 megapixels, respectivamente, os quais apresentaram resultados satisfatórios.
Abstract: Radiometric differences between homologous areas in digital aerial imagens are caused by several factors and affect the photogrammetric processes like correspondence and mosaicking. In order to enhance those images, some procedures like geometric and radiometric corrections can be applied, the last being the aim of this work. The process of radiometric adjustment in digital aerial images aim at to make similar the histograms of overlapping images, favoring the processes of correspondence and mosaicking formation. The method proposed in this work is performed in two stages: firstly a global processing based on the histograms data and secondly a local strategy, for which two methods were studied. The first is an interpolation of correction values corresponding to the radiometric differences in the overlapping images. The second method for the local adjustment uses surface adjustment in order to correct the differences in the corresponding regions. In order to assess the proposed methodology, a program in C language was implemented to perform the local adjustment based on some corresponding points. Some experiments were accomplished, using three blocks of aerial images taken with 22 and 39 megapixels digital cameras, and good results were achieved.
Mestre

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Langhi, Paula J. Piloto [UNESP]. "Ajuste radiométrico de imagens aéreas digitais." Universidade Estadual Paulista (UNESP), 2009. http://hdl.handle.net/11449/86772.

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Made available in DSpace on 2014-06-11T19:22:25Z (GMT). No. of bitstreams: 0 Previous issue date: 2009-03-26Bitstream added on 2014-06-13T20:28:44Z : No. of bitstreams: 1 langhi_pjp_me_prud.pdf: 4558873 bytes, checksum: 6633a0b385f223c9200a10403ba4fa9b (MD5)
Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
As diferenças radiométricas entre áreas homólogas de imagens aéreas digitais são causadas por vários fatores e afetam os processos fotogramétricos, como a correspondência e a mosaicagem. Para que as imagens possam ser melhoradas, alguns procedimentos como correção geométrica e radiométrica são aplicados, sendo a correção radiométrica o tema de estudo deste trabalho. O processo de correção radiométrica em imagens aéreas digitais tem como principal objetivo tornar semelhantes os histogramas de imagens que possuam sobreposição, facilitando os processos posteriores de correspondência de pontos homólogos e a formação de mosaicos. O método proposto neste trabalho realiza em duas etapas a correção radiométrica para um bloco de imagens aéreas digitais. A primeira etapa é um processamento global, baseado nos dados dos histogramas. A segunda etapa é uma correção local, que pode ser realizada através de dois métodos estudados, sendo que o primeiro trata a interpolação dos valores de correção à partir de regiões homólogas, possibilitando a correção radiométrica da região de sobreposição das imagens; e o segundo método utiliza o ajuste de uma superfície parabolóide às diferenças radiométricas entre imagens, fornecendo, posteriormente, valores de correção para toda a imagem. Para realizar os processos descritos acima, foi implementado um programa em linguagem C, o qual realiza o ajuste local a partir de pontos correspondentes. Alguns experimentos foram realizados, utilizando três blocos de imagens aéreas obtidas com câmeras digitais Hasselblad de 22 e 39 megapixels, respectivamente, os quais apresentaram resultados satisfatórios.
Radiometric differences between homologous areas in digital aerial imagens are caused by several factors and affect the photogrammetric processes like correspondence and mosaicking. In order to enhance those images, some procedures like geometric and radiometric corrections can be applied, the last being the aim of this work. The process of radiometric adjustment in digital aerial images aim at to make similar the histograms of overlapping images, favoring the processes of correspondence and mosaicking formation. The method proposed in this work is performed in two stages: firstly a global processing based on the histograms data and secondly a local strategy, for which two methods were studied. The first is an interpolation of correction values corresponding to the radiometric differences in the overlapping images. The second method for the local adjustment uses surface adjustment in order to correct the differences in the corresponding regions. In order to assess the proposed methodology, a program in C language was implemented to perform the local adjustment based on some corresponding points. Some experiments were accomplished, using three blocks of aerial images taken with 22 and 39 megapixels digital cameras, and good results were achieved.

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Lin, Jun-Yu, and 林俊宇. "Defect correction algorithms for the Image Histogram." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/17225215578480383870.

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碩士
僑光科技大學
資訊科技研究所
101
The development and progress of modern video imaging technology is not only limited to imaging itself. It has also led to the development of panel display technology with the resolution of electronic devices becoming more and more detailed, with an increasingly wider colour gamut and colour accuracy becoming higher and higher. Hence the requirement for details of the colour image information has increased. However, due to the improper use of image processing technology or a lack of detailed calculation, these often cause the loss of detailed image or colour information. The main purpose of this paper is to provide a new algorithm which can repair the gradation of the image when detailed information is lost by calculations; re-patching the missing colour information so that the colour information of the image can be recovered with as much detail as possible. The image would then have better visual display for screen display or on print.

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Частини книг з теми "Histogram correction"

bt Shamsuddin, Norsila, Wan Fatimah bt Wan Ahmad, Baharum b Baharudin, Mohd Kushairi b Mohd Rajuddin, and Farahwahida bt Mohd. "Image Enhancement of Underwater Habitat Using Color Correction Based on Histogram." In Lecture Notes in Computer Science, 289–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25191-7_28.

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Singh, Himanshu, Anil Kumar, and L. K. Balyan. "Robustly Clipped Sub-equalized Histogram-Based Cosine-Transformed Energy-Redistributed Gamma Correction for Satellite Image Enhancement." In Proceedings of 3rd International Conference on Computer Vision and Image Processing, 483–95. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9291-8_38.

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Rajesh T. M., Kavyashree Dalawai, and Pradeep N. "Automatic Data Acquisition and Spot Disease Identification System in Plants Pathology Domain." In Modern Techniques for Agricultural Disease Management and Crop Yield Prediction, 111–41. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-5225-9632-5.ch006.

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Plants play one of the main roles in our ecosystem. Manual identification for the leaves sometimes leads to greater difference due to look alike. People often get confused with lookalike leaves which mostly end in loss of life. Authentication of original leaf with look-alike leaf is very essential nowadays. Disease identification of plants are proved to be beneficial for agro-industries, research, and eco-system balancing. In the era of industrialization, vegetation is shrinking. Early detection of diseases from the dataset of leaf can be rewarding and help in making our environment healthier and green. Implementation involves proper data acquisition where pre-processing of images is done for error correction if present in the raw dataset. It is followed by feature extraction stage to get the best results in further classification stage. K-mean, PCA, and ICA algorithms are used for identification and clustering of diseases in plants. The implementation proves that the proposed method shows promising result on the basis of histogram of gradient (HoG) features.

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Jindal, Sumit Kumar, Sayak Banerjee, Ritayan Patra, and Arin Paul. "Deep learning-based brain malignant neoplasm classification using MRI image segmentation assisted by bias field correction and histogram equalization." In Brain Tumor MRI Image Segmentation Using Deep Learning Techniques, 135–61. Elsevier, 2022. http://dx.doi.org/10.1016/b978-0-323-91171-9.00008-9.

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"Correcting a weak histogram." In Photoshop CS: Essential Skills, 68–70. Routledge, 2004. http://dx.doi.org/10.4324/9780080479996-29.

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Тези доповідей конференцій з теми "Histogram correction"

Pukhova, Ekaterina, and Vladislav Vereshchagin. "Compensation of defects in printing process with histogram methods." In 10th International Symposium on Graphic Engineering and Design. University of Novi Sad, Faculty of technical sciences, Department of graphic engineering and design,, 2020. http://dx.doi.org/10.24867/grid-2020-p41.

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A method of image preparation for printing reproduction is suggested. This method allows to automatically compensate transformations that occur during reproduction, by analyzing a histogram of test chart image and based on it, creating a compensation pre-correction function. It also takes into consideration the visual perception of images. Pre-correction function is applied to images at the prepress stage after all other corrections. It is aimed to compensate defects, occurring at the printing stage, caused by the process of tone value increase and restriction of tonal range reproduction. It is suggested to use a test chart, which is a gradient with an even increase of lightness in the range from 0 to 255. After printing the test chart its digital image is created by scanning. Then Gaussian filter is applied to the image with parameters according to the visual perception, and lightness distribution histogram is calculated. This histogram will have changes in lightness distribution in comparison with the original digital image. These changes will correspond to the influence of tone value increasing process during printing. The cumulative sum is calculated from the received histogram, and the pre-correction is being formed. And this precorrection applies to an image, prepared for printing in similar conditions as test chart. The algorithm was written on Python and allows to create a pre-correction using a press sheet with the test chart. It is shown that the use of the suggested method gives a positive result and doesn’t require expensive measurement equipment. Having a scanner calibrated for linear transmission of lightness and developed programming module is enough. This method was tested on electrographic printing equipment on three different types of paper. Statistic parameters of a histogram, such as mean, standard deviation and the Skewness, were used for evaluation. It is shown that the suggested method can be used as part of an automatized system based on histogram methods for image preparation before printing.

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Kyriakou, Y., M. Meyer, R. Lapp, and W. A. Kalender. "Histogram-driven cupping correction (HDCC) in CT." In SPIE Medical Imaging. SPIE, 2010. http://dx.doi.org/10.1117/12.844206.

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Chan-Su Park, Hi-Seok Kim, and Hyeong-Woo Cha. "Histogram color correction for multi-view video cording." In 2014 International SoC Design Conference (ISOCC). IEEE, 2014. http://dx.doi.org/10.1109/isocc.2014.7087633.

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Zhang, Dongni, Won-Jae Park, Seung-Jun Lee, Kang-A. Choi, and Sung-Jea Ko. "Histogram partition based gamma correction for image contrast enhancement." In 2012 IEEE 16th International Symposium on Consumer Electronics - (ISCE 2012). IEEE, 2012. http://dx.doi.org/10.1109/isce.2012.6241687.

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An, Qinghao, Jianjie Shi, Jinping Li, and Fudong Cai. "Elevator button recognition using auto-slant correction and projection histogram." In 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE, 2017. http://dx.doi.org/10.1109/cisp-bmei.2017.8302054.

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Jing, Yang, and Cao Shi. "The Goal Matching Algorithm Histogram-Based Color Correction and Space." In 2013 Fourth International Conference on Digital Manufacturing & Automation (ICDMA). IEEE, 2013. http://dx.doi.org/10.1109/icdma.2013.349.

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Chen, Yibin, Kai-Kuang Ma, and Canhui Cai. "Histogram-offset-based color correction for multi-view video coding." In 2010 17th IEEE International Conference on Image Processing (ICIP 2010). IEEE, 2010. http://dx.doi.org/10.1109/icip.2010.5650032.

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Dutta, Ayan, and Biswajit Kar. "Baseline drift correction and heart rate estimation using Histogram technique." In 2017 International Conference on Innovations in Electronics, Signal Processing and Communication (IESC). IEEE, 2017. http://dx.doi.org/10.1109/iespc.2017.8071876.

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Yi Liu, Yongfang Wang, and Zhaoyang Zhang. "Color correction method based on histogram segmentation for multiview video." In IET International Communication Conference on Wireless Mobile & Computing (CCWMC 2009). IET, 2009. http://dx.doi.org/10.1049/cp.2009.2009.

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