SEGMENTATION OF DIGITAL IMAGES WITH WAVELET TRANSFORMATION USING MATLAB VERSION R2010B

Authors

  • Muhammad Zainal Abidin Universitas Muhammadiyah Jakarta

DOI:

https://doi.org/10.46799/ijssr.v4i10.1086

Keywords:

Segmentation, Wavelet Transformation, MATLAB

Abstract

The purpose of this research is to explore and apply the use of wavelet transformation for the segmentation of digital images, utilizing MATLAB version R2010B. The study aims to analyze how wavelet transform can be used to enhance the accuracy and effectiveness of image segmentation, which is a critical process in image processing and computer vision. The research contributes to the field of digital image processing by demonstrating the application of wavelets transformation for segmenting digital images. In this study, the study provides insights into how wavelets can be utilized to improve the detection of image features, especially in identifying image features more accurately. The experimental results show that only the Canny operator's edge detection method has the best edge detector in detecting the edges of objects in wavelet images. The technique of determining the threshold value (thresholding) can be carried out in two ways, namely, automatic method and technique carried out by trial and error. Finally, improving automatic thresholding techniques using AI-driven algorithms to reduce the reliance on trial-and-error methods could provide more consistent results in varied application areas.

References

Baek, Y. S., & Park, Y. K. (2021). Intensity-based holographic imaging via space-domain Kramers–Kronig relations. Nature Photonics, 15(5). https://doi.org/10.1038/s41566-021-00760-8

Bhandari, A., Kadambi, A., & Raskar, R. (2022). Computational Imaging. MIT Press. https://books.google.co.id/books?id=EVRFEAAAQBAJ

Blahnik, V., & Schindelbeck, O. (2021). Smartphone imaging technology and its applications. Advanced Optical Technologies, 10(3). https://doi.org/10.1515/aot-2021-0023

Brady, D. J., Fang, L., & Ma, Z. (2020). Deep learning for camera data acquisition, control, and image estimation. Advances in Optics and Photonics, 12(4). https://doi.org/10.1364/aop.398263

Chávez Heras, D., & Blanke, T. (2021). On machine vision and photographic imagination. AI and Society, 36(4). https://doi.org/10.1007/s00146-020-01091-y

Fernandes, A. F. A., Dórea, J. R. R., & Rosa, G. J. de M. (2020). Image Analysis and Computer Vision Applications in Animal Sciences: An Overview. Frontiers in Veterinary Science, 7. https://doi.org/10.3389/fvets.2020.551269

Fratz, M., Seyler, T., Bertz, A., & Carl, D. (2021). Digital holography in production: an overview. Light: Advanced Manufacturing, 2(3). https://doi.org/10.37188/lam.2021.015

Gurevich, I., & Yashina, V. (2021). Basic Models of Descriptive Image Analysis. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12665 LNCS. https://doi.org/10.1007/978-3-030-68821-9_26

He, S., Wang, R., & Luo, H. (2022). Computing metasurfaces for all-optical image processing: A brief review. Nanophotonics, 11(6). https://doi.org/10.1515/nanoph-2021-0823

Hua, Z., Zhu, Z., Yi, S., Zhang, Z., & Huang, H. (2021). Cross-plane colour image encryption using a two-dimensional logistic tent modular map. Information Sciences, 546. https://doi.org/10.1016/j.ins.2020.09.032

Mennel, L., Symonowicz, J., Wachter, S., Polyushkin, D. K., Molina-Mendoza, A. J., & Mueller, T. (2020). Ultrafast machine vision with 2D material neural network image sensors. Nature, 579(7797). https://doi.org/10.1038/s41586-020-2038-x

Prakash, D. C., Narayanan, R. C., Ganesh, N., Ramachandran, M., Chinnasami, S., & Rajeshwari, R. (2022). A study on image processing with data analysis. AIP Conference Proceedings, 2393. https://doi.org/10.1063/5.0074764

Salvi, M., Acharya, U. R., Molinari, F., & Meiburger, K. M. (2021). The impact of pre- and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis. Computers in Biology and Medicine, 128. https://doi.org/10.1016/j.compbiomed.2020.104129

Schultheiss, V. H., Batz, S., & Peschel, U. (2020). Light in curved two-dimensional space. Advances in Physics: X, 5(1). https://doi.org/10.1080/23746149.2020.1759451

Sutoyo, T. (2009). Teori Pengolahan Citra Digital. Universitas Dian Nuswantoro.

Thakur, R., & Rohilla, R. (2020). Recent advances in digital image manipulation detection techniques: A brief review. Forensic Science International, 312. https://doi.org/10.1016/j.forsciint.2020.110311

Zhou, J., Qian, H., Zhao, J., Tang, M., Wu, Q., Lei, M., Luo, H., Wen, S., Chen, S., & Liu, Z. (2021). Two-dimensional optical spatial differentiation and high-contrast imaging. National Science Review, 8(6). https://doi.org/10.1093/nsr/nwaa176

Downloads

Published

2024-10-25