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
Licensee MJS, Universiti Malaya, Malaysia. This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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