Araştırma Makalesi
BibTex RIS Kaynak Göster

Analysing detail preserving capabilities of bilateral, laplacian and taubin mesh filtering methods

Yıl 2023, Cilt: 5 Sayı: 2, 67 - 74, 15.12.2023

Erkan Beşdok Pınar Çivicioğlu

https://doi.org/10.53093/mephoj.1349021

Öz

Mesh filtering of surfaces is crucial for noise reduction, feature preservation, and mesh simplification in graphics, visualization, and computer vision. In this paper, the detail preservation capacities of 3 frequently used filters, i.e., Bilateral, Laplacian, and Taubin mesh filters, in mesh filtering have been thoroughly examined by experiments conducted on 4 different test meshes. While the Bilateral filter excels in preserving sharp features due to its integration of geometric proximity with intensity similarity, the Laplacian filter prioritizes smoothness by averaging neighboring vertex positions, and the Taubin filter offers a balanced approach by merging attributes of both Laplacian and high-pass filters. The Bilateral filter's primary strength lies in its ability to maintain sharp features on a mesh, ensuring that intricate details are preserved by considering both the spatial closeness and intensity similarity of vertices. The Laplacian filter, although effective in achieving mesh smoothness, has the propensity to excessively smooth out sharp and defining features, potentially causing a loss of critical details in the processed mesh. The Taubin filter integrates the best of both worlds, ensuring smoothness without excessive mesh shrinkage; however, it might not excel in feature preservation as effectively as the Bilateral filter or smooth as uniformly as the Laplacian filter, making it a middle-ground option for certain applications. The statistical analysis of the experimental results has shown that the Taubin method is statistically a more successful mesh filtering method for the test sets used in this paper.

Anahtar Kelimeler

Mesh Filtering Bilateral Filter Laplacian Filter Taubin Filter

Kaynakça

  • Liu, Y., Coombes, M., & Liu, C. (2023). Mesh-based consensus distributed particle filtering for sensor networks. IEEE Transactions on Signal and Information Processing over Networks, 9, 346-356. https://doi.org/10.1109/TSIPN.2023.3278469
  • Liu, B., Li, B., Cao, J., Wang, W., & Liu, X. (2023). Adaptive and propagated mesh filtering. Computer-Aided Design, 154, 103422. https://doi.org/10.1016/j.cad.2022.103422
  • Fábián, G. (2023). Generalized Savitzky–Golay filter for smoothing triangular meshes. Computer Aided Geometric Design, 100, 102167. https://doi.org/10.1016/j.cagd.2022.102167
  • Han, H. D., & Han, J. K. (2022). Modified bilateral filter for feature enhancement in mesh denoising. IEEE Access, 10, 56845-56862. https://doi.org/10.1109/ACCESS.2022.3176961
  • Zhong, S., Song, Z., Liu, Z., Xie, Z., Chen, J., Liu, L., & Chen, R. (2021). Shape-aware mesh normal filtering. Computer-Aided Design, 140, 103088. https://doi.org/10.1016/j.cad.2021.103088
  • Zhao, W., Liu, X., Wang, S., Fan, X., & Zhao, D. (2019). Graph-based feature-preserving mesh normal filtering. IEEE Transactions on Visualization and Computer Graphics, 27(3), 1937-1952. https://do.iorg/10.1109/TVCG.2019.2944357
  • Zhang, J., Deng, B., Hong, Y., Peng, Y., Qin, W., & Liu, L. (2018). Static/dynamic filtering for mesh geometry. IEEE transactions on visualization and computer graphics, 25(4), 1774-1787. https://do.org/10.1109/TVCG.2018.2816926
  • Noel, G., Djouani, K., Van Wyk, B., & Hamam, Y. (2012). Bilateral mesh filtering. Pattern Recognition Letters, 33(9), 1101-1107. https://doi.org/10.1016/j.patrec.2012.02.008
  • Loménie, N., & Stamon, G. (2008). Morphological mesh filtering and α-objects. Pattern Recognition Letters, 29(10), 1571-1579. https://doi.org/10.1016/j.patrec.2008.03.019
  • Kim, B., & Rossignac, J. (2005). Geofilter: Geometric selection of mesh filter parameters. In Computer Graphics Forum, 24(3), 295-302.
  • Leipoldt, K. J., Happich, T., Kreysa, E., & Gemünd, H. P. (1991). Scattering matrix methods for far-infrared metal mesh filters. International Journal of Infrared and Millimeter Waves, 12, 263-274. https://doi.org/10.1007/BF01010300
  • Chen, P. A. (1987). The performance of dielectric coated mesh filter. International Journal of Infrared and Millimeter Waves, 8, 29-33. https://doi.org/10.1007/BF01010643
  • Byrne, D. M., Brouns, A. J., Case, F. C., Tiberio, R. C., Whitehead, B. L., & Wolf, E. D. (1985). Infrared mesh filters fabricated by electron‐beam lithography. Journal of Vacuum Science & Technology B: Microelectronics Processing and Phenomena, 3(1), 268-271. https://doi.org/10.1116/1.583243
  • Byrne, D. M., Brouns, A. J., & Case, F. C. (1984). Infrared mesh filters (A). Journal of the Optical Society of America A, 1, 1330.
  • Civicioglu, P. (2009). Removal of random-valued impulsive noise from corrupted images. IEEE Transactions on Consumer Electronics, 55(4), 2097-2104. https://do.org/10.1109/TCE.2009.5373774
  • Civicioglu, P. (2007). Using uncorrupted neighborhoods of the pixels for impulsive noise suppression with ANFIS. IEEE Transactions on Image Processing, 16(3), 759-773. https://doi.org/10.1109/TIP.2007.891067
  • Çivicioğlu, P. (2005). Using LM artificial neural networks and η-closest-pixels for impulsive noise suppression from highly corrupted images. In International Symposium on Neural Networks (pp. 679-684). https://doi.org/10.1007/11427445_110
  • Beşdok, E., Çivicioğlu, P., & Alçı, M. (2005). Using Anfis with circular polygons for impulsive noise suppression from highly distorted images. AEU-International Journal of Electronics and Communications, 59(4), 213-221. https://doi.org/10.1016/j.aeue.2004.11.041
  • Çivicioğlu, P., Alçı, M., & Beṣdok, E. (2004). Using an exact radial basis function artificial neural network for impulsive noise suppression from highly distorted image databases. In International Conference on Advances in Information Systems, 383-391. https://doi.org/10.1007/978-3-540-30198-1_39
  • Çivicioğlu, P., Alçı, M., & Beşdok, E. (2004). Impulsive noise suppression from images with the noise exclusive filter. EURASIP Journal on Advances in Signal Processing, 16, 2434–2440. https://doi.org/10.1155/S1110865704403151
  • Çivicioğlu, P., & Alçı, M. (2004). Edge detection of highly distorted images suffering from impulsive noise. AEU-International Journal of Electronics and Communications, 58(6), 413-419. https://doi.org/10.1078/1434-8411-54100262
  • Liu, B., Cao, J., Wang, W., Ma, N., Li, B., Liu, L., & Liu, X. (2018). Propagated mesh normal filtering. Computers & Graphics, 74, 119-125. https://doi.org/10.1016/j.cag.2018.05.003
  • Zhang, W., Deng, B., Zhang, J., Bouaziz, S., & Liu, L. (2015). Guided mesh normal filtering. In Computer Graphics Forum, 34(7), 23-34. https://doi.org/10.1111/cgf.12742
  • Wei, M., Yu, J., Pang, W. M., Wang, J., Qin, J., Liu, L., & Heng, P. A. (2014). Bi-normal filtering for mesh denoising. IEEE Transactions on Visualization and Computer Graphics, 21(1), 43-55. https://doi.org/10.1109/TVCG.2014.2326872
  • Shen, J. G., Zhang, S. Y., Chen, Z. Y., Zhang, Y., & Ye, X. Z. (2009). Mesh sharpening via normal filtering. Journal of Zhejiang University-Science A, 10(4), 546-553. https://doi.org/10.1631/jzus.A0820505
  • Mao, Z., Ma, L., Zhao, M., & Xiao, X. (2006). SUSAN structure preserving filtering for mesh denoising. The Visual Computer, 22, 276-284. https://doi.org/10.1007/s00371-006-0005-7
  • Hou, Q., Bai, L., & Wang, Y. (2005). Mesh smoothing via adaptive bilateral filtering. In International Conference on Computational Science, 273-280. https://doi.org/10.1007/11428848_34
  • Balan, R., & Taubin, G. (2000). 3d mesh geometry filtering algorithms for progressive transmission schemes. Computer-aided design, 32(13), 825-846. https://doi.org/10.1016/S0010-4485(00)00069-5
  • Liu, S., Rho, S., Wang, R., & Jiang, F. (2018). Feature-preserving mesh denoising based on guided normal filtering. Multimedia Tools and Applications, 77, 23009-23021. https://doi.org/10.1007/s11042-018-5735-9
  • Zheng, Y., Fu, H., Au, O. K. C., & Tai, C. L. (2010). Bilateral normal filtering for mesh denoising. IEEE Transactions on Visualization and Computer Graphics, 17(10), 1521-1530. https://doi.org/10.1109/TVCG.2010.264
  • Agathos, A., Azariadis, P., & Kyratzi, S. (2022). Elliptic Gabriel Taubin smoothing of point clouds. Computers & Graphics, 106, 20-32. https://doi.org/10.1016/j.cag.2022.05.009
  • Nousias, S., Arvanitis, G., Lalos, A. S., & Moustakas, K. (2020). Fast mesh denoising with data driven normal filtering using deep variational autoencoders. IEEE Transactions on Industrial Informatics, 17(2), 980-990. https://doi.org/10.1109/TII.2020.3000491
  • Li, X., Li, R., Zhu, L., Fu, C. W., & Heng, P. A. (2020). DNF-Net: A deep normal filtering network for mesh denoising. IEEE Transactions on Visualization and Computer Graphics, 27(10), 4060-4072. https://doi.org/10.1109/TVCG.2020.3001681
  • Civicioglu, P. (2013). Backtracking search optimization algorithm for numerical optimization problems. Applied Mathematics and computation, 219(15), 8121-8144. https://doi.org/10.1016/j.amc.2013.02.017
  • Civicioglu, P., & Besdok, E. (2019). Bernstain-search differential evolution algorithm for numerical function optimization. Expert Systems with Applications, 138, 112831. https://doi.org/10.1016/j.eswa.2019.112831
  • Civicioglu, P., & Besdok, E. (2023). Bernstein-Levy differential evolution algorithm for numerical function optimization. Neural Computing and Applications, 35(9), 6603-6621. https://doi.org/10.1007/s00521-022-08013-7
  • Civicioglu, P., Besdok, E., Gunen, M. A., & Atasever, U. H. (2020). Weighted differential evolution algorithm for numerical function optimization: a comparative study with cuckoo search, artificial bee colony, adaptive differential evolution, and backtracking search optimization algorithms. Neural Computing and Applications, 32, 3923-3937. https://doi.org/10.1007/s00521-018-3822-5

Yıl 2023, Cilt: 5 Sayı: 2, 67 - 74, 15.12.2023

Erkan Beşdok Pınar Çivicioğlu

https://doi.org/10.53093/mephoj.1349021

Öz

Kaynakça

  • Liu, Y., Coombes, M., & Liu, C. (2023). Mesh-based consensus distributed particle filtering for sensor networks. IEEE Transactions on Signal and Information Processing over Networks, 9, 346-356. https://doi.org/10.1109/TSIPN.2023.3278469
  • Liu, B., Li, B., Cao, J., Wang, W., & Liu, X. (2023). Adaptive and propagated mesh filtering. Computer-Aided Design, 154, 103422. https://doi.org/10.1016/j.cad.2022.103422
  • Fábián, G. (2023). Generalized Savitzky–Golay filter for smoothing triangular meshes. Computer Aided Geometric Design, 100, 102167. https://doi.org/10.1016/j.cagd.2022.102167
  • Han, H. D., & Han, J. K. (2022). Modified bilateral filter for feature enhancement in mesh denoising. IEEE Access, 10, 56845-56862. https://doi.org/10.1109/ACCESS.2022.3176961
  • Zhong, S., Song, Z., Liu, Z., Xie, Z., Chen, J., Liu, L., & Chen, R. (2021). Shape-aware mesh normal filtering. Computer-Aided Design, 140, 103088. https://doi.org/10.1016/j.cad.2021.103088
  • Zhao, W., Liu, X., Wang, S., Fan, X., & Zhao, D. (2019). Graph-based feature-preserving mesh normal filtering. IEEE Transactions on Visualization and Computer Graphics, 27(3), 1937-1952. https://do.iorg/10.1109/TVCG.2019.2944357
  • Zhang, J., Deng, B., Hong, Y., Peng, Y., Qin, W., & Liu, L. (2018). Static/dynamic filtering for mesh geometry. IEEE transactions on visualization and computer graphics, 25(4), 1774-1787. https://do.org/10.1109/TVCG.2018.2816926
  • Noel, G., Djouani, K., Van Wyk, B., & Hamam, Y. (2012). Bilateral mesh filtering. Pattern Recognition Letters, 33(9), 1101-1107. https://doi.org/10.1016/j.patrec.2012.02.008
  • Loménie, N., & Stamon, G. (2008). Morphological mesh filtering and α-objects. Pattern Recognition Letters, 29(10), 1571-1579. https://doi.org/10.1016/j.patrec.2008.03.019
  • Kim, B., & Rossignac, J. (2005). Geofilter: Geometric selection of mesh filter parameters. In Computer Graphics Forum, 24(3), 295-302.
  • Leipoldt, K. J., Happich, T., Kreysa, E., & Gemünd, H. P. (1991). Scattering matrix methods for far-infrared metal mesh filters. International Journal of Infrared and Millimeter Waves, 12, 263-274. https://doi.org/10.1007/BF01010300
  • Chen, P. A. (1987). The performance of dielectric coated mesh filter. International Journal of Infrared and Millimeter Waves, 8, 29-33. https://doi.org/10.1007/BF01010643
  • Byrne, D. M., Brouns, A. J., Case, F. C., Tiberio, R. C., Whitehead, B. L., & Wolf, E. D. (1985). Infrared mesh filters fabricated by electron‐beam lithography. Journal of Vacuum Science & Technology B: Microelectronics Processing and Phenomena, 3(1), 268-271. https://doi.org/10.1116/1.583243
  • Byrne, D. M., Brouns, A. J., & Case, F. C. (1984). Infrared mesh filters (A). Journal of the Optical Society of America A, 1, 1330.
  • Civicioglu, P. (2009). Removal of random-valued impulsive noise from corrupted images. IEEE Transactions on Consumer Electronics, 55(4), 2097-2104. https://do.org/10.1109/TCE.2009.5373774
  • Civicioglu, P. (2007). Using uncorrupted neighborhoods of the pixels for impulsive noise suppression with ANFIS. IEEE Transactions on Image Processing, 16(3), 759-773. https://doi.org/10.1109/TIP.2007.891067
  • Çivicioğlu, P. (2005). Using LM artificial neural networks and η-closest-pixels for impulsive noise suppression from highly corrupted images. In International Symposium on Neural Networks (pp. 679-684). https://doi.org/10.1007/11427445_110
  • Beşdok, E., Çivicioğlu, P., & Alçı, M. (2005). Using Anfis with circular polygons for impulsive noise suppression from highly distorted images. AEU-International Journal of Electronics and Communications, 59(4), 213-221. https://doi.org/10.1016/j.aeue.2004.11.041
  • Çivicioğlu, P., Alçı, M., & Beṣdok, E. (2004). Using an exact radial basis function artificial neural network for impulsive noise suppression from highly distorted image databases. In International Conference on Advances in Information Systems, 383-391. https://doi.org/10.1007/978-3-540-30198-1_39
  • Çivicioğlu, P., Alçı, M., & Beşdok, E. (2004). Impulsive noise suppression from images with the noise exclusive filter. EURASIP Journal on Advances in Signal Processing, 16, 2434–2440. https://doi.org/10.1155/S1110865704403151
  • Çivicioğlu, P., & Alçı, M. (2004). Edge detection of highly distorted images suffering from impulsive noise. AEU-International Journal of Electronics and Communications, 58(6), 413-419. https://doi.org/10.1078/1434-8411-54100262
  • Liu, B., Cao, J., Wang, W., Ma, N., Li, B., Liu, L., & Liu, X. (2018). Propagated mesh normal filtering. Computers & Graphics, 74, 119-125. https://doi.org/10.1016/j.cag.2018.05.003
  • Zhang, W., Deng, B., Zhang, J., Bouaziz, S., & Liu, L. (2015). Guided mesh normal filtering. In Computer Graphics Forum, 34(7), 23-34. https://doi.org/10.1111/cgf.12742
  • Wei, M., Yu, J., Pang, W. M., Wang, J., Qin, J., Liu, L., & Heng, P. A. (2014). Bi-normal filtering for mesh denoising. IEEE Transactions on Visualization and Computer Graphics, 21(1), 43-55. https://doi.org/10.1109/TVCG.2014.2326872
  • Shen, J. G., Zhang, S. Y., Chen, Z. Y., Zhang, Y., & Ye, X. Z. (2009). Mesh sharpening via normal filtering. Journal of Zhejiang University-Science A, 10(4), 546-553. https://doi.org/10.1631/jzus.A0820505
  • Mao, Z., Ma, L., Zhao, M., & Xiao, X. (2006). SUSAN structure preserving filtering for mesh denoising. The Visual Computer, 22, 276-284. https://doi.org/10.1007/s00371-006-0005-7
  • Hou, Q., Bai, L., & Wang, Y. (2005). Mesh smoothing via adaptive bilateral filtering. In International Conference on Computational Science, 273-280. https://doi.org/10.1007/11428848_34
  • Balan, R., & Taubin, G. (2000). 3d mesh geometry filtering algorithms for progressive transmission schemes. Computer-aided design, 32(13), 825-846. https://doi.org/10.1016/S0010-4485(00)00069-5
  • Liu, S., Rho, S., Wang, R., & Jiang, F. (2018). Feature-preserving mesh denoising based on guided normal filtering. Multimedia Tools and Applications, 77, 23009-23021. https://doi.org/10.1007/s11042-018-5735-9
  • Zheng, Y., Fu, H., Au, O. K. C., & Tai, C. L. (2010). Bilateral normal filtering for mesh denoising. IEEE Transactions on Visualization and Computer Graphics, 17(10), 1521-1530. https://doi.org/10.1109/TVCG.2010.264
  • Agathos, A., Azariadis, P., & Kyratzi, S. (2022). Elliptic Gabriel Taubin smoothing of point clouds. Computers & Graphics, 106, 20-32. https://doi.org/10.1016/j.cag.2022.05.009
  • Nousias, S., Arvanitis, G., Lalos, A. S., & Moustakas, K. (2020). Fast mesh denoising with data driven normal filtering using deep variational autoencoders. IEEE Transactions on Industrial Informatics, 17(2), 980-990. https://doi.org/10.1109/TII.2020.3000491
  • Li, X., Li, R., Zhu, L., Fu, C. W., & Heng, P. A. (2020). DNF-Net: A deep normal filtering network for mesh denoising. IEEE Transactions on Visualization and Computer Graphics, 27(10), 4060-4072. https://doi.org/10.1109/TVCG.2020.3001681
  • Civicioglu, P. (2013). Backtracking search optimization algorithm for numerical optimization problems. Applied Mathematics and computation, 219(15), 8121-8144. https://doi.org/10.1016/j.amc.2013.02.017
  • Civicioglu, P., & Besdok, E. (2019). Bernstain-search differential evolution algorithm for numerical function optimization. Expert Systems with Applications, 138, 112831. https://doi.org/10.1016/j.eswa.2019.112831
  • Civicioglu, P., & Besdok, E. (2023). Bernstein-Levy differential evolution algorithm for numerical function optimization. Neural Computing and Applications, 35(9), 6603-6621. https://doi.org/10.1007/s00521-022-08013-7
  • Civicioglu, P., Besdok, E., Gunen, M. A., & Atasever, U. H. (2020). Weighted differential evolution algorithm for numerical function optimization: a comparative study with cuckoo search, artificial bee colony, adaptive differential evolution, and backtracking search optimization algorithms. Neural Computing and Applications, 32, 3923-3937. https://doi.org/10.1007/s00521-018-3822-5
Toplam 37 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Fotogrametri ve Uzaktan Algılama
Bölüm Araştırma Makaleleri
Yazarlar

Erkan Beşdok 0000-0001-9309-375X

Pınar Çivicioğlu 0000-0003-1850-8489

Erken Görünüm Tarihi 17 Ekim 2023
Yayımlanma Tarihi 15 Aralık 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 5 Sayı: 2

Kaynak Göster

APA Beşdok, E., & Çivicioğlu, P. (2023). Analysing detail preserving capabilities of bilateral, laplacian and taubin mesh filtering methods. Mersin Photogrammetry Journal, 5(2), 67-74. https://doi.org/10.53093/mephoj.1349021
AMA Beşdok E, Çivicioğlu P. Analysing detail preserving capabilities of bilateral, laplacian and taubin mesh filtering methods. MEPHOJ. Aralık 2023;5(2):67-74. doi:10.53093/mephoj.1349021
Chicago Beşdok, Erkan, ve Pınar Çivicioğlu. “Analysing Detail Preserving Capabilities of Bilateral, Laplacian and Taubin Mesh Filtering Methods”. Mersin Photogrammetry Journal 5, sy. 2 (Aralık 2023): 67-74. https://doi.org/10.53093/mephoj.1349021.
EndNote Beşdok E, Çivicioğlu P (01 Aralık 2023) Analysing detail preserving capabilities of bilateral, laplacian and taubin mesh filtering methods. Mersin Photogrammetry Journal 5 2 67–74.
IEEE E. Beşdok ve P. Çivicioğlu, “Analysing detail preserving capabilities of bilateral, laplacian and taubin mesh filtering methods”, MEPHOJ, c. 5, sy. 2, ss. 67–74, 2023, doi: 10.53093/mephoj.1349021.
ISNAD Beşdok, Erkan - Çivicioğlu, Pınar. “Analysing Detail Preserving Capabilities of Bilateral, Laplacian and Taubin Mesh Filtering Methods”. Mersin Photogrammetry Journal 5/2 (Aralık 2023), 67-74. https://doi.org/10.53093/mephoj.1349021.
JAMA Beşdok E, Çivicioğlu P. Analysing detail preserving capabilities of bilateral, laplacian and taubin mesh filtering methods. MEPHOJ. 2023;5:67–74.
MLA Beşdok, Erkan ve Pınar Çivicioğlu. “Analysing Detail Preserving Capabilities of Bilateral, Laplacian and Taubin Mesh Filtering Methods”. Mersin Photogrammetry Journal, c. 5, sy. 2, 2023, ss. 67-74, doi:10.53093/mephoj.1349021.
Vancouver Beşdok E, Çivicioğlu P. Analysing detail preserving capabilities of bilateral, laplacian and taubin mesh filtering methods. MEPHOJ. 2023;5(2):67-74.