ARCHIVES

1. “Comparison Of Signal To Noise Ratio Of Colored And Gray Scale Image In Clustered Condition From TheContour Of The Images With
The Help Of Different Image Filtering Method”- Abir Chakraborty, Volume 9, Issue5 May 2024| Issn: 2456-4184
2. Detection And Comparison Of Signal To Noise Ratio’s And Other Dimensions Related Specifications From Contours Ofseveral Images -
AMatlab 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]
3. Detectionofsignal Tonoise Ratio From Image Contour -Amatlab Application [Volume: 06 Issue: 09 |
4. Application Of Image Processing Using Matlab- A Practical Handbook For Image Processing Laboratorty]-Abir Chakraborty
5. Detection and Comparison of Signal To Noise Ratio’s and OtherDimensions 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]
6. Ahmed, S. & Alone, M. R. (2014). Image Compression using Neural Network. International Journal of Innovative Science andModern
Engineering, 2(5), 24-28.
7. 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
8. Fukushima, K. (1980). Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffectedby shift
in position. Biological Cybernetics, 36(4), 193-202. https://doi.org/10.1007/ Bf00344251
9. Grgic, S., Grgic, M., & Zovko-Cihlar, B. (2001). Performance analysis of image compression using wavelets. IEEE Transactionson
Industrial Electronics, 48(3), 682-695. https://doi.org/10.1109/41.925596
10. Hussain, A. J., Al-Jumeily, D., Radi, N., & Lisboa, P. (2015).Hybrid neural network predictive-wavelet image compressionsystem.
Neurocomputing, 151, 975-984. https://doi.org/10.1016/j.neucom.2014.02.078.
11. Joe, A. R., & Rama, N. (2015). Neural network based image compression for memoryconsumption in cloud environment. IndianJournal of
Science and Technology, 8(15), 1-6.https://doi.org/10.17485/i jst/2015/ v8i15/73855,September – 2022, ISSN: 2582-3930]