POINT CLOUD SIMPLIFICATION METHOD BASED ON IMPROVED FUZZY C-MEANS WITH AUTOMATIC NUMBER OF CLUSTERS
Article Sidebar
Main Article Content
Abstract
The essence of the point cloud is to express geometric information about objects by getting discrete coordinates on their surfaces. However, with a million points, this data may record redundant details in which it is needless to be kept for the model’s analysis. In addition, there is also a limitation where the processors cannot process the large-size datasets. The point cloud simplification algorithm was developed to solve the stated obstacles in data processing. Numerous algorithms have been published to produce the best methods for data reduction process. Since the simplification process might eliminate essential features of the data, this study introduces the features preservation process to keep the important points before the simplification. This study employed the Fuzzy C-Means (FCM) algorithms for the simplification stage due to their simplicity and ability to generate an accurate result. Regardless, the FCM still suffers from drawbacks, where their initial cluster centres are prone to fall into local optima. This study improved the FCM by employing the Score and Minimum Distance (SMD) to determine the number of clusters and cluster centres. The SMD is enhanced by changing the Gaussian to Cosine kernel function to increase the accuracy. This new technique is named SMD(C)-IFCM. The method was then applied to the 3D point cloud of a box, cup, and Stanford bunny. The performance of the developed method was compared with the original SMD-FCM and SMD-IFCM for the percentage of the simplified data, error evaluation, and processing time. The result and analysis showed that the developed method had the best score, which was six (6) out of nine (9) measurements, compared to the other two methods with scores of one (1) and two (2) respectively. This score suggests that the developed algorithm successfully reduced the error evaluation and the processing time to generate the output.
Downloads
Article Details
Transfer of Copyrights
- In the event of publication of the manuscript entitled [INSERT MANUSCRIPT TITLE AND REF NO.] in the Malaysian Journal of Science, I hereby transfer copyrights of the manuscript title, abstract and contents to the Malaysian Journal of Science and the Faculty of Science, University of Malaya (as the publisher) for the full legal term of copyright and any renewals thereof throughout the world in any format, and any media for communication.
Conditions of Publication
- I hereby state that this manuscript to be published is an original work, unpublished in any form prior and I have obtained the necessary permission for the reproduction (or am the owner) of any images, illustrations, tables, charts, figures, maps, photographs and other visual materials of whom the copyrights is owned by a third party.
- This manuscript contains no statements that are contradictory to the relevant local and international laws or that infringes on the rights of others.
- I agree to indemnify the Malaysian Journal of Science and the Faculty of Science, University of Malaya (as the publisher) in the event of any claims that arise in regards to the above conditions and assume full liability on the published manuscript.
Reviewer’s Responsibilities
- Reviewers must treat the manuscripts received for reviewing process as confidential. It must not be shown or discussed with others without the authorization from the editor of MJS.
- Reviewers assigned must not have conflicts of interest with respect to the original work, the authors of the article or the research funding.
- Reviewers should judge or evaluate the manuscripts objective as possible. The feedback from the reviewers should be express clearly with supporting arguments.
- If the assigned reviewer considers themselves not able to complete the review of the manuscript, they must communicate with the editor, so that the manuscript could be sent to another suitable reviewer.
Copyright: Rights of the Author(s)
- Effective 2007, it will become the policy of the Malaysian Journal of Science (published by the Faculty of Science, University of Malaya) to obtain copyrights of all manuscripts published. This is to facilitate:
(a) Protection against copyright infringement of the manuscript through copyright breaches or piracy.
(b) Timely handling of reproduction requests from authorized third parties that are addressed directly to the Faculty of Science, University of Malaya. - As the author, you may publish the fore-mentioned manuscript, whole or any part thereof, provided acknowledgement regarding copyright notice and reference to first publication in the Malaysian Journal of Science and Faculty of Science, University of Malaya (as the publishers) are given.
You may produce copies of your manuscript, whole or any part thereof, for teaching purposes or to be provided, on individual basis, to fellow researchers. - You may include the fore-mentioned manuscript, whole or any part thereof, electronically on a secure network at your affiliated institution, provided acknowledgement regarding copyright notice and reference to first publication in the Malaysian Journal of Science and Faculty of Science, University of Malaya (as the publishers) are given.
- You may include the fore-mentioned manuscript, whole or any part thereof, on the World Wide Web, provided acknowledgement regarding copyright notice and reference to first publication in the Malaysian Journal of Science and Faculty of Science, University of Malaya (as the publishers) are given.
- In the event that your manuscript, whole or any part thereof, has been requested to be reproduced, for any purpose or in any form approved by the Malaysian Journal of Science and Faculty of Science, University of Malaya (as the publishers), you will be informed. It is requested that any changes to your contact details (especially e-mail addresses) are made known.
Copyright: Role and responsibility of the Author(s)
- In the event of the manuscript to be published in the Malaysian Journal of Science contains materials copyrighted to others prior, it is the responsibility of current author(s) to obtain written permission from the copyright owner or owners.
- This written permission should be submitted with the proof-copy of the manuscript to be published in the Malaysian Journal of Science
References
Alomoush, W., Alrosan, A., Almomani, A., Alissa, K., Khashan, O. A., & Al-Nawasrah, A. (2021). Spatial information of fuzzy clustering based mean best artificial bee colony algorithm for phantom brain image segmentation. International Journal of Electrical and Computer Engineering, 11(5). https://doi.org/10.11591/ijece.v11i5.pp4050-4058
Askari, S. (2021). Fuzzy C-Means clustering algorithm for data with unequal cluster sizes and contaminated with noise and outliers: Review and development. Expert Systems with Applications, 165. https://doi.org/10.1016/j.eswa.2020.113856
Hadi, N. A., & Alias, N. (2019). 3-Dimensional Human Head Reconstruction Using Cubic Spline Surface on CPU-GPU Platform. Proceedings of the 2019 4th International Conference on Intelligent Information Technology, 16–20. https://doi.org/10.1145/3321454.3321476
Hadi, N. A., Halim, S. A., & Alias, N. (2021). Statistical filtering on 3D cloud data points on the CPU-GPU platform. Journal of Physics: Conference Series, 1770(1). https://doi.org/10.1088/1742-6596/1770/1/012006
Halim, M. S. A., Hadi, N. A., Sulaiman, H., & Halim, S. A. (2017). An Algorithm for Beta-Spline Surface Reconstruction from Multi Slice CT Scan Images using MATLAB pmode. IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE), 1–6. https://doi.org/10.1109/ISCAIE.2017.8074939
Halim, S. A., Hadi, N. A., & Mat Lazim, N. S. (2021). Segmentation and features extraction of Malaysian herbs leaves. Journal of Physics: Conference Series, 1770(1), 1–7. https://doi.org/10.1088/1742-6596/1770/1/012005
He, Z., Jia, Z., & Zhang, X. (2019). A fast method for estimating the number of clusters based on score and the minimum distance of the center point. Information, 11(16), 1–14.
He, Z., Jia, Z., & Zhang, X. (2020). A fast method for estimating the number of clusters based on score and the minimum distance of the center point. Information (Switzerland), 11(1). https://doi.org/10.3390/info11010016
Ji, C., Li, Y., Fan, J., & Lan, S. (2019). A novel simplification method for 3d geometric point cloud based on the importance of point. IEEE Access, 7. https://doi.org/10.1109/ACCESS.2019.2939684
Krishna, P. G., & Bhaskari, D. L. (2016). Fuzzy C-means and fuzzy TLBO for fuzzy clustering. Advances in Intelligent Systems and Computing, 379. https://doi.org/10.1007/978-81-322-2517-1_46
Leal, N., Leal, E., & German, S. T. (2017). A linear programming approach for 3D point cloud simplification. IAENG International Journal of Computer Science, 44(1).
Mahdaoui, A., & Sbai, E. H. (2020). 3D point cloud simplification based on k-nearest neighbor and clustering. Advances in Multimedia, 2020.
Siringoringo, R., & Jamaluddin, J. (2019). Initializing the Fuzzy C-Means Cluster Center with Particle Swarm Optimization for Sentiment Clustering. Journal of Physics: Conference Series, 1361(1). https://doi.org/10.1088/1742-6596/1361/1/012002
Wang, Q., & Kim, M.-K. (2019). Applications of 3D point cloud data in the construction industry: A fifteen-year review from 2004 to 2018. Advanced Engineering Informatics, 39, 306–319.
Wu, Z., Zeng, Y., Li, D. S., Liu, J., & Feng, L. (2021). High-volume point cloud data simplification based on decomposed graph filtering. Automation in Construction, 129. https://doi.org/10.1016/j.autcon.2021.103815
Xuan, W., Hua, X., Chen, X., Zou, J., & He, X. (2018). A New Progressive Simplification Method for Point Cloud Using Local Entropy of Normal Angle. Journal of the Indian Society of Remote Sensing, 46(4). https://doi.org/10.1007/s12524-017-0730-6
Yang, Y., Li, M., & Ma, X. (2020). A Point Cloud Simplification Method Based on Modified Fuzzy C-Means Clustering Algorithm with Feature Information Reserved. Mathematical Problems in Engineering, 2020. https://doi.org/10.1155/2020/5713137
Zhang, L., Luo, M., Liu, J., Li, Z., & Zheng, Q. (2020). Diverse fuzzy c-means for image clustering. Pattern Recognition Letters, 130. https://doi.org/10.1016/j.patrec.2018.07.004