Implementation of Segmentation Scheme based on Wavelet Transform in Multi-Spectral Fluctuation Patterns
Keywords:Segmentation, Fluctuation Pattern, Peak Of Amplitude
AbstractSegmentation is one of signal processing methods that is fruitful to recognize some details of parts contained in a detail pattern before will be further processed. This study proposed a segmentation scheme, which is the input is based on the output of approximation of level two from wavelet transformation of 2D-DWT (Two Dimension Discrete Wavelet Transform). The segmentation scheme and algorithm process are implemented on the fluctuations patterns of HHF (High High-fluctuation) in multi spectral that previously are extracted. The fluctuation patterns of HHF are 2D (Dimensional) graphic that consist of matrix of the average value and standard deviation inside. The method that will be used is to apply the approach of segmentation scheme, which is suitable for treating HHF fluctuation pattern. Moreover, it also employs the approach to acquire the highest amplitude value and also to present the signals in the segments that have the top peak these fluctuations. In addition, there are some parameters that are very prominent in segmentation results to be analyzed, such as: the number of segments, the input frequency, spectral noise, the peak of amplitude value and VMR (variance to mean ratio). Furthermore, the analysis results of these parameters will be compared between the highest top three segments in the pattern fluctuation of HHF. Based on the results, it is noticeable to say that the segmentation scheme can be implemented for HHF pattern. Moreover, there are some segments that have the highest peak of amplitude values and also some parameters show quite striking phenomenon compare to other segments with the same parameters. In addition, the result of VMR is suitable with the statistical approach at VMR <1. It can be said that the proposed segmentation approach can provide the illustration of the segment each of pattern fluctuations HHF clearly.
Y. Li, “Wavelet-based fuzzy multiphase image segmentation method ✩,” Pattern Recognit. Lett., vol. 53, pp. 1–8, 2015.
H. Azami, H. Hassanpour, J. Escudero, and S. Sanei, “An intelligent approach for variable size segmentation of non-stationary signals,” J. Adv. Res., vol. 6, no. 5, pp. 687–698, 2015.
M. Chain and M. Carlo, “Image Segmentation by Data-Driven,” IEEE Trans. PATTERN Anal. Mach. I, vol. 24, no. 5, pp. 657–673, 2002.
A. C. Fan, J. W. F. Iii, W. M. W. Iii, J. J. Levitt, and A. S. Willsky, “MCMC Curve Sampling for Image Segmentation,” MICCAI 2007, pp. 477–485, 2007.
H. Li, H. He, and Y. Wen, “Optik Dynamic particle swarm optimization and K-means clustering algorithm for image segmentation,” Opt. - Int. J. Light Electron Opt., vol. 126, no. 24, pp. 4817–4822, 2015.
Y. Yang, Y. Wang, and X. Xue, “Optik A novel spectral clustering method with superpixels for image segmentation,” Opt. - Int. J. Light Electron Opt., vol. 127, no. 1, pp. 161–167, 2016.
V. Grau, A. U. J. Mewes, M. Alcañiz, R. Kikinis, and S. K. Warfield, “Improved Watershed Transform for Medical Image Segmentation Using Prior Information,” IEEE Trans. Med. Imaging, vol. 23, no. 4, pp. 447–458, 2004.
Melinda, S.T.Agus, G. Dadang, S. Dodi, “Consistency Analysis of Mapping System of Noise Spectral Fluctuations in Multi-Frequency using Two-Dimension Discrete Wavelet Transform (2D-DWT),” in ASEA-UNINET 2016, 2016, pp. 48–62.
Y. Li and X. Feng, “A multiscale image segmentation method,” Pattern Recognit., vol. 52, pp. 332–345, 2016.
K. Fatima, “A Novel Architecture for the Computation of 2D-DWT and its Implementation on Virtex-II Pro FPGA,” in Computational Intelligence and Security, 2007, pp. 531–535.
H. Imtiaz and S. A. Fattah, “A Wavelet-domain Local Feature Selection Scheme for Face Recognition,” IEEE, vol. 11, pp. 448–451, 2011.
K. H. Ghazali, M. F. Mansor, M. M. Mustafa, and A. Hussain, “Feature Extraction Technique using Discrete Wavelet Transform for Image Classification,” in SCOReD, 2007, no. December, pp. 5–8.
S. G. C. S, “Image Compression and Denoising Effects using Wavelets,” IJNTEC, vol. 2, pp. 1–4, 2014.
A. Materka, “Discrete Wavelet Transform – Derived Features For Digital Image Texture Analysis,” in Signal and Electronic Systems, 2001, no. September, pp. 163–168.
S. Mallat, “A Wavelet Tour of Signal Processing,” A Wavelet Tour Signal Process., pp. 20–41, 1999.
How to Cite
TRANSFER OF COPYRIGHT AGREEMENT
The manuscript is herewith submitted for publication in the Journal of Telecommunication, Electronic and Computer Engineering (JTEC). It has not been published before, and it is not under consideration for publication in any other journals. It contains no material that is scandalous, obscene, libelous or otherwise contrary to law. When the manuscript is accepted for publication, I, as the author, hereby agree to transfer to JTEC, all rights including those pertaining to electronic forms and transmissions, under existing copyright laws, except for the following, which the author(s) specifically retain(s):
- All proprietary right other than copyright, such as patent rights
- The right to make further copies of all or part of the published article for my use in classroom teaching
- The right to reuse all or part of this manuscript in a compilation of my own works or in a textbook of which I am the author; and
- The right to make copies of the published work for internal distribution within the institution that employs me
I agree that copies made under these circumstances will continue to carry the copyright notice that appears in the original published work. I agree to inform my co-authors, if any, of the above terms. I certify that I have obtained written permission for the use of text, tables, and/or illustrations from any copyrighted source(s), and I agree to supply such written permission(s) to JTEC upon request.