293
Views
0
CrossRef citations to date
0
Altmetric
Articles

Texture recognition under scale and illumination variations

& ORCID Icon
Pages 130-148 | Received 19 Dec 2022, Accepted 26 Sep 2023, Published online: 07 Oct 2023

References

  • Ahonen, T., Matas, J., He, C., & Pietikainen, M. (2009). Rotation invariant image description with local binary pattern histogram fourier features. In SCIA (pp. 61–70). Springer-Verlag, Berlin Heidelberg
  • Bell, S., Upchurch, P., Snavely, N., & Bala, K. (2015). Material recognition in the wild with the materials in context database. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 3479–3487). IEEE
  • Bovik, A. (1991). Analysis of multichannel narrow-band filters for image texture segmentation. IEEE Transactions on Signal Processing, 39(9), 2025–2043. https://doi.org/10.1109/78.134435
  • Burghouts, G. J., & Geusebroek, J. -M. (2009). Material-specific adaptation of color invariant features. Pattern Recognition Letters, 30(3), 306–313. https://doi.org/10.1016/j.patrec.2008.10.005
  • Finlayson, G., Schaefer, G., & Tian, G. (2000). The UEA uncalibrated colour image database. Technical Report SYS-C00, School of Information System, University of East Anglia, Norwich, UK.
  • Fu, X., & Wei, W. (2008). Centralized binary patterns embedded with image Euclidean distance for facial expression recognition. In Natural Computation, 2008. ICNC '08. Fourth International Conference on Vol. 4, (pp. 115–119).
  • Gibert, X., Patel, V. M., & Chellappa, R. (2015). Material classification and semantic segmentation of railway track images with deep convolutional neural networks. In 2015 IEEE International Conference on Image Processing (ICIP) (pp. 621–625). IEEE.
  • Grigorescu, S. E., Petkov, N., & Kruizinga, P. (2002). Comparison of texture features based on gabor filters. IEEE Transactions on Image Processing, 11(10), 1160–1167. https://doi.org/10.1109/TIP.2002.804262
  • Haindl, M. (2012). Visual data recognition and modeling based on local markovian models. In L. Florack, R. Duits, G. Jongbloed, M. C. Lieshout, and L. Davies (Eds.), Mathematical methods for signal and image analysis and representation, volume 41 of Computational imaging and vision, chapter 14, (pp. 241–259). Springer London. https://doi.org/10.1007/978-1-4471-2353-8_14
  • Haindl, M. (2023). Bidirectional texture function modeling, chapter 28, (pp. 1023–1064). Springer International Publishing, Gewerbestrasse 11, 6330 Cham, Switzerland.
  • Haindl, M., & Filip, J. (2013). Visual texture. Advances in computer vision and pattern recognition. Springer-Verlag London.
  • Haindl, M., Filip, J., & Vávra, R. (2012). Digital material appearance: The curse of tera-bytes. ERCIM News, 90, 49–50.
  • Haindl, M., Mikeš, S., & Kudo, M. (2015). Unsupervised surface reflectance field multi-segmenter. In G. Azzopardi and N. Petkov, (Eds.), Computer analysis of images and patterns, Vol. 9256 of Lecture notes in computer science, (pp. 261 – 273). Springer International Publishing.
  • Haindl, M., & Vacha, P. (2015). Wood veneer species recognition using Markovian textural features. In G. Azzopardi and N. Petkov, (Eds.), Computer analysis of images and patterns, volume 9256 of Lecture notes in computer science, (pp. 300–311). Springer International Publishing.
  • Haindl, M., & Vácha, P. (2017). Scale sensitivity of textural features. In C. Beltrán-Castañón, I. Nyström, and F. Famili (Eds.), Progress in pattern recognition, image analysis, computer vision, and applications: 21st Iberoamerican Congress, CIARP 2016, Lima, Peru, November 8–11, 2016, Proceedings, Vol. 10125 of Lecture notes in computer science, (pp. 84 – 92). Gewerbestrasse 11, Cham, CH-6330, Switzerland. Springer International Publishing AG.
  • Han, J., & Ma, K.-K. (2007). Rotation-invariant and scale-invariant gabor features for texture image retrieval. Image and Vision Computing, 25(9), 1474–1481. https://doi.org/10.1016/j.imavis.2006.12.015
  • Heikkilä, M., Pietikäinen, M., & Schmid, C. (2009). Description of interest regions with local binary patterns. Pattern Recognition, 42(3), 425–436. https://doi.org/10.1016/j.patcog.2008.08.014
  • Hlaing, C. S., & Zaw, S. M. M. (2018). Tomato plant diseases classification using statistical texture feature and color feature. In 2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS) (pp. 439–444). IEEE.
  • Jain, A. K., & Healey, G. (1998). A multiscale representation including opponent color features for texture recognition. IEEE Transactions on Image Processing, 7(1), 124–128. https://doi.org/10.1109/83.650858
  • Khellah, F. (2011). Texture classification using dominant neighborhood structure. IEEE Transactions on Image Processing, 20(11), 3270–3279. https://doi.org/10.1109/TIP.2011.2143422
  • Li, Z., Liu, G., Jiang, H., & Qian, X. (2009). Image copy detection using a robust gabor texture descriptor. In Proceedings of the First ACM Workshop on Large-scale Multimedia Retrieval and Mining, LS-MMRM '09 (pp. 65–72). New York, NY, USA. ACM.
  • Liao, S., Law, M. W. K., & Chung, A. C. S. (2009). Dominant local binary patterns for texture classification. IEEE Transactions on Image Processing, 18(5), 1107–1118. https://doi.org/10.1109/TIP.2009.2015682
  • Liu, L., Chen, J., Fieguth, P., Zhao, G., Chellappa, R., & Pietikainen, M. (2018). A survey of recent advances in texture representation. arXiv preprint arXiv:1801.10324.
  • Liu, L., Fieguth, P., Wang, X., Pietikäinen, M., & Hu, D. (2016). Evaluation of lbp and deep texture descriptors with a new robustness benchmark. In European Conference on Computer Vision, (pp. 69–86). Springer.
  • Luo, Q., Su, J., Yang, C., Silven, O., & Liu, L. (2022). Scale-selective and noise-robust extended local binary pattern for texture classification. Pattern Recognition, 132, 108901. https://doi.org/10.1016/j.patcog.2022.108901
  • Manjunath, B. S., & Ma, W. Y. (1996). Texture features for browsing and retrieval of image data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(8), 837–842. https://doi.org/10.1109/34.531803
  • Muzaffar, A. W., Riaz, F., Abuain, T., Abu-Ain, W. A. K., Hussain, F., Farooq, M. U., & Azad, M. A. (2023). Gabor contrast patterns: A novel framework to extract features from texture images. IEEE Access, 11, 60324–60334. https://doi.org/10.1109/ACCESS.2023.3280053
  • Nanni, L., Lumini, A., & Brahnam, S. (2012). Survey on {LBP} based texture descriptors for image classification. Expert Systems with Applications, 39(3), 3634–3641. https://doi.org/10.1016/j.eswa.2011.09.054
  • Ojala, T., Pietikäinen, M., & Mäenpää, T. (2002). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7), 971–987. https://doi.org/10.1109/TPAMI.2002.1017623
  • Randen, T., & Husøy, J. H. (1999). Filtering for texture classification: A comparative study. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(4), 291–310. https://doi.org/10.1109/34.761261
  • Remeš, V., & Haindl, M. (2019). Bark recognition using novel rotationally invariant multispectral textural features. Pattern Recognition Letters, 125, 612–617. https://doi.org/10.1016/j.patrec.2019.06.027
  • Roy, S. K., Bhattacharya, N., Chanda, B., Chaudhuri, B. B., & Ghosh, D. K. (2018). Fwlbp: A scale invariant descriptor for texture classification. arXiv preprint arXiv:1801.03228.
  • Santini, S., & Jain, R. (1999). Similarity measures. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(9), 871–883. https://doi.org/10.1109/34.790428
  • Shivashankar, S., Kudari, M., & Hiremath, P. S. (2018). Galois field-based approach for rotation and scale invariant texture classification. International Journal of Image, Graphics and Signal Processing (IJIGSP), 10(9), 56–64. https://doi.org/10.5815/ijigsp
  • Shu, X., Pan, H., Shi, J., Song, X., & Wu, X.-J. (2022). Using global information to refine local patterns for texture representation and classification. Pattern Recognition, 131, 108843. https://doi.org/10.1016/j.patcog.2022.108843
  • Sidiropoulos, G. K., Ouzounis, A. G., Papakostas, G. A., Sarafis, I. T., Stamkos, A., & Solakis, G. (2021). Texture analysis for machine learning based marble tiles sorting. In 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC) (pp. 0045–0051). IEEE.
  • Simon, P., & Uma, V. (2018). Review of texture descriptors for texture classification. In Data Engineering and Intelligent Computing, (pp. 159–176). Springer.
  • Stricker, M. A., & Orengo, M. (1995). Similarity of color images. Vol. 2420, (pp. 381–392). SPIE.
  • Vácha, P., & Haindl, M. (2012). Texture recognition using Robust Markovian features. In Salerno, E., Çetin, A., and Salvetti, O. (Eds.), Computational intelligence for multimedia understanding, volume 7252 of lecture notes in computer science, (pp. 126–137). Springer Berlin/Heidelberg. https://doi.org/10.1007/978-3-642-32436-9_11
  • Vácha, P., & Haindl, M. (2022). Textural features sensitivity to scale and illumination variations. In Bădică, C., Treur, J., Benslimane, D., Hnatkowska, B., and Krótkiewicz, M., (Eds.), Advances in computational collective intelligence Vol. 1653 of Communications in computer and information science (pp. 237–249), Gewerbestrasse 11, Cham, CH-6330, Switzerland. Springer International Publishing.
  • Vácha, P., Haindl, M., & Suk, T. (2011). Colour and rotation invariant textural features based on Markov random fields. Pattern Recognition Letters, 32(6), 771–779. https://doi.org/10.1016/j.patrec.2011.01.002
  • Varma, M., & Zisserman, A. (2009). A statistical approach to material classification using image patch exemplars. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(11), 2032–2047. https://doi.org/10.1109/TPAMI.2008.182
  • Veerashetty, S., & Patil, N. B. (2020). Novel lbp based texture descriptor for rotation, illumination and scale invariance for image texture analysis and classification using multi-kernel svm. Multimedia Tools and Applications, 79(15–16), 9935–9955. https://doi.org/10.1007/s11042-019-7345-6
  • Yang, P., Zhang, F., & Yang, G. (2018). Fusing dtcwt and lbp based features for rotation, illumination and scale invariant texture classification. IEEE Access, 6, 13336–13349. https://doi.org/10.1109/ACCESS.2018.2797072
  • Zhang, B., Gao, Y., Zhao, S., & Liu, J. (2010). Local derivative pattern versus local binary pattern: Face recognition with high-order local pattern descriptor. IEEE Transactions on Image Processing, 19(2), 533–544. https://doi.org/10.1109/TIP.2009.2035882