Published May 30, 2024 | Version CC-BY-NC-ND 4.0
Journal article Open

Comparison of Signal to Noise Ratio of Colored and Gray Scale Image in Clustered Condition from the Contours of the Images with the Help of Different Image Filtering Method

Authors/Creators

  • 1. Department of Computer Science, Project Work Team Fellow, University of Coimbra.

Description

Abstract: As we know the image can be processed with the help of different types of coding for example mat-lab. Here in this paper we are primarily focusing on some common filtering methodologies [5] related to image contour in clustered conditions. For filtering purpose in this paper we have used three different filtering technologies such as prewitt [3], sobel [3], canny [3] filtering. But on the other hand we have used both colored [1] and non-colored [3] images for clustering operations. Our main aim in this paper to show variations of signal to noise ratios for the colored and non-colored contour images with and without filtering. As per my request study the discussion of results very carefully to realize the deeper meaning of the journal [4].

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Additional details

Identifiers

DOI
10.54105/ijipr.D1029.04030424
EISSN
2582-8037

Dates

Accepted
2024-04-15
Manuscript received on 14 March 2024 | Revised Manuscript received on 21 March 2024 | Manuscript Accepted on 15 April 2024 | Manuscript published on 30 May 2024.

References

  • Detection and Comparison of Signal to Noise Ratio's and Other Dimensions Related Specifications from Contours of Several Images - A Matlab Syntax Based Applications of Biomedical and General Jpeg Images- Abir Chakraborty, Dr. Somshekhar Bhat, Dr. Kumar Shama [Volume 10, Issue 9, September-2022, Impact Factor: 7.429, ISSN: 2455-6211]
  • Detectionofsignal Tonoise Ratio fom Image Contour -A Matlab Application [Volume: 06 Issue: 09 | September – 2022, Issn: 2582-3930]
  • Detection and Comparison of Signal To Noise Ratio's and Other Dimensions Related Specifications From Contours of Several Images - A Matlab Syntax Based Applications of Biomedical and General Jpeg Images-[Abir Chakraborty1, Dr. Somshekhar Bhat2, Dr. Kumar Shama3, 1,2,3Manipal Institute of Technology, Mahe , Karanataka, India, Volume 10, Issue 9, September-2022, Impact Factor: 7.429, ISSN: 2455-6211]
  • Ahmed, S. & Alone, M. R. (2014). Image Compression using Neural Network. International Journal of Innovative Science and Modern Engineering, 2(5), 24-28.
  • Balasubramani, P., & Murugan, P. R. (2015). Efficient image compression techniques for compressing multimodal medical images using neural network radial basis function approach. International Journal of Imaging Systems and Technology, 25(2), 115-122. https://doi.org/10.1002/ima.22127
  • Fukushima, K. (1980). Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics, 36(4), 193-202. https://doi.org/10.1007/ Bf00344251 https://doi.org/10.1007/BF00344251
  • Grgic, S., Grgic, M., & Zovko-Cihlar, B. (2001). Performance analysis of image compression using wavelets. IEEE Transactions on Industrial Electronics, 48(3), 682-695. https://doi.org/10.1109/41.925596
  • Hussain, A. J., Al-Jumeily, D., Radi, N., & Lisboa, P. (2015). Hybrid neural network predictive-wavelet image compression system. Neurocomputing, 151, 975-984. https://doi.org/10.1016/j.neucom.2014.02.078
  • Joe, A. R., & Rama, N. (2015). Neural network based image compression for memory consumption in cloud environment. Indian Journal of Science and Technology, 8(15), 1-6. https://doi.org/10.17485/i jst/2015/ v8i15/73855, https://doi.org/10.17485/ijst/2015/v8i15/73855
  • Khandelwal, R. R., & Purohit, P. K. (2019). Implementation of Direct Indexing and 2-V Golomb Coding of Lattice Vectors for Image Compression. In International Journal of Innovative Technology and Exploring Engineering (Vol. 8, Issue 9, pp. 1205–1210). https://doi.org/10.35940/ijitee.h7443.078919
  • Tamanna, & Bassan, N. (2019). Innovative Hybridization for Image Compression using PCA and Multilevel 2D-Wavelet. In International Journal of Recent Technology and Engineering (IJRTE) (Vol. 8, Issue 3, pp. 2411–2415). https://doi.org/10.35940/ijrte.c4668.098319
  • Sivasankari, Mrs. K., Singh, S., Kumar, K., & Dubey, A. (2021). A Robust and Dynamic Fire Detection Algorithm using Convolutional Neural Network. In Indian Journal of Image Processing and Recognition (Vol. 1, Issue 2, pp. 6–10). https://doi.org/10.54105/ijipr.b1007.061221
  • Rajeswari, C., & Prakasam, S. (2020). An Efficient Functionality Learning Image Compression by Ift Technique. In International Journal of Engineering and Advanced Technology (Vol. 10, Issue 1, pp. 451–455). https://doi.org/10.35940/ijeat.a1916.1010120
  • Young, L., York, J. R., & Kil Lee, B. (2023). Implications of Deep Compression with Complex Neural Networks. In International Journal of Soft Computing and Engineering (Vol. 13, Issue 3, pp. 1–6). https://doi.org/10.35940/ijsce.c3613.0713323