Statistics

    Map

Twitter

Mathematical Methods applied in Image Enhancement using Matlab
( Vol-5,Issue-5,May 2018 )
Author(s):

Felipe Resende de Souza, Pedro Américo Almeida Magalhães Júnior

Keywords:

convolution; differential equations; image enhancement; image processing; Kernel; Laplacian; matlab.

Abstract:

In order to characterize complex engineering problems involvinging image data acquisition, different techniques in image processing can be used. One of those techniques is called the Laplacian Filter, commonly used to reduce noise and improving images. Based on that, image segmentation is a widely applied tool in engineering and it can greatly contribute in the acceleration of processes instead of adopting conventional methods, thus providing applications of such technique in the medical, spatial and other sectors linked to engineering. Therefore, this work aims to use image segmentation through differential equations (Laplacian Filter) in different images using Matlab mathematical software in order to enhance images details.

ijaers doi crossref DOI:

10.22161/ijaers.5.5.39

Paper Statistics:
  • Total View : 57
  • Downloads : 14
  • Page No: 305-307
Cite this Article:
MLA
Felipe Resende de Souza et al ."Mathematical Methods applied in Image Enhancement using Matlab". International Journal of Advanced Engineering Research and Science(ISSN : 2349-6495(P) | 2456-1908(O)),vol 5, no. 5, 2018, pp.305-307 AI Publications, doi:10.22161/ijaers.5.5.39
APA
Felipe Resende de Souza, Pedro Américo Almeida Magalhães Júnior(2018).Mathematical Methods applied in Image Enhancement using Matlab. International Journal of Advanced Engineering Research and Science(ISSN : 2349-6495(P) | 2456-1908(O)),5(5), 305-307. http://dx.doi.org/10.22161/ijaers.5.5.39
Chicago
Felipe Resende de Souza, Pedro Américo Almeida Magalhães Júnior. 2018,"Mathematical Methods applied in Image Enhancement using Matlab". International Journal of Advanced Engineering Research and Science(ISSN : 2349-6495(P) | 2456-1908(O)).5(5):305-307. Doi: 10.22161/ijaers.5.5.39
Harvard
Felipe Resende de Souza, Pedro Américo Almeida Magalhães Júnior. 2018,Mathematical Methods applied in Image Enhancement using Matlab, International Journal of Advanced Engineering Research and Science(ISSN : 2349-6495(P) | 2456-1908(O)).5(5), pp:305-307
IEEE
Felipe Resende de Souza, Pedro Américo Almeida Magalhães Júnior."Mathematical Methods applied in Image Enhancement using Matlab", International Journal of Advanced Engineering Research and Science(ISSN : 2349-6495(P) | 2456-1908(O)),vol.5,no. 5, pp.305-307,2018.
Bibtex
@article {feliperesendedesouza2018mathematical,
title={Mathematical Methods applied in Image Enhancement using Matlab},
author={Felipe Resende de Souza, Pedro Américo Almeida Magalhães Júnior},
journal={International Journal of Advanced Engineering Research and Science},
volume={5},
year= {2018},
}
Share:
References:

[1] Buenos, G; Albalá, A. M; Cosías, P. Fuzziness and PDE based Models for the Segmentation of Medical Images. Nuclear Science Symposium Conference Record, 2004 IEEE, doi: 10.1109/NSSMIC.2004.1466702
[2] Dusik, K; Junhee, Y; Changyoon, K. Automatic Photovoltaic Panel Area Extraction from UAV Thermal Infrared Images. Journal of the Korean Society of Surveying Geodesy, Photogrammetry and Cartography. 2016. https://doi.org/10.7848/ksgpc.2016.34.6.559
[3] Gonzales, R. C; Woods, R. E; Eddins, S. L. Digital Image Processing using Matlab. Pearson, 2nded.
[4] Khan W. Image Segmentation Techniques: A Survey. Journal of Image and Graphics Vol.1, No. 4, December 2013, doi: 10.12720/joig.1.4.166-170
[5] Kim, D; Youn J; Kim C. Automatic fault recognition of photovoltaic modules based on statistical analysis of UAV thermography. International Conference on Unmanned Aerial Vehicles in Geomatics, 4-7 September 2017, Bonn, Germany. https://doi.org/10.5194/isprs-archives-XLII-2_W6-179-2017
[6] Neto, J. L. R; Junior, P. A. A. M. Utilização de Técnicas de Inteligência Artificial na Segmentação de Imagens para Análise de Modelos Fotoelásticos. XXXVII Iberian Latin American Congress on computational methods in engineering. Brasília, November 2016.
[7] Sasikala, N. Shruthi, G. Texture Analysis of a Color Image using Traditional and Circular Gabor Filters. International Journal of Advanced Engineering Research and Science. (ISSN: 2349-6495(P) | 2456-1908(O). https://dx.doi.org/10.22161/ijaers
[8] Tsanakas, J. A; Chrysostomou, D; Botsaris, P. N; Gasteratos, A. Fault diagnosis of photovoltaic modules through image processing and Canny edge detection on field thermographic measurements. International Journal of Sustainable Energy, 2015. http://dx.doi.org/10.1080/14786451.2013.826223
[9] Vale, G. M; Poz, A. P. D. O Processo de detecção de bordas de canny: fundamentos, algoritmos e avaliação experimental. Anais do Simpósio Brasileiro de Geomática, Presidente Prudente – SP, Brasil, 9-13 July 2002, p. 292-303.
[10] Wang, S; Xu, Y; Wan, L. Marine image enhancement using fuzzy algorithm based on modified fuzzy partition. International Conference on Photonics, 3D-Imaging, and Visualization, 2011, Guangzhou, China, doi: 10:1117/12.905914