References

• 1. 1)
     • 1. Neto, E.C., Gomes, S.L., Filho, P.P.R., et al: ‘Brazilian vehicle identification using a new embedded plate recognition system’, Measurement, 2015, 70, pp. 36–46 (doi: 10.1016/j.measurement.2015.03.039).
  2. 2)
     • 14. de Almeida, T.M., da Silveira Cavalcante, T., Cortez, P.C.: ‘Three-dimensional radial active contour model: a 3-d to 1-d image segmentation technique’, Latin Am. Trans., 2017, 15, (2), pp. 365–373 (doi: 10.1109/TLA.2017.7854634).
  3. 3)
     • 13. Meng, M., Xia, J., Luo, J., et al: ‘Unsupervised co-segmentation for 3d shapes using iterative multi-label optimization’, Comput.-Aided Des., 2013, 45, (2), pp. 312–320, solid and Physical Modeling 2012 (doi: 10.1016/j.cad.2012.10.014).
  4. 4)
     • 8. Xu, C., Sun, Y., Lombaert, H., et al: ‘A multilevel banded graph cuts method for fast image segmentation’. IEEE Int. Conf. on Computer Vision, 2005, vol. 01, no. undefined, pp. 259–265.
  5. 5)
     • 9. Srinark, T., Kambhamettu, C., Kambhamettu, R.: ‘An approach for 3d segmentation on multiresolution surfaces’. 2003.
  6. 6)
     • 3. Bessa, J.A., Cortez, P.C., da Silva Félix, J.H., et al: ‘Radial snakes: comparison of segmentation methods in synthetic noisy images’, Expert Syst. Appl., 2015, 42, (6), pp. 3079–3088 (doi: 10.1016/j.eswa.2014.11.036).
  7. 7)
     • 16. Delves, L.M., Wilkinson, R., Oliver, C.J., et al: ‘Comparing the performance of sar image segmentation algorithms’, Int. J. Remote Sens., 1992, 13, (11), pp. 2121–2149 (doi: 10.1080/01431169208904257).
  8. 8)
     • 4. Chabrier, S., Rosenberger, C., Laurent, H., et al: ‘Evaluating the segmentation result of a gray level image’. 2004 12th European Signal Processing Conf., September 2004, pp. 953–956.
  9. 9)
     • 12. de Bruin, P., Dercksen, V., Post, F., et al: ‘Interactive 3d segmentation using connected orthogonal contours’, Comput. Biol. Med., 2005, 35, (4), pp. 329–346 (doi: 10.1016/j.compbiomed.2004.02.006).
  10. 10)
      • 10. Filho, P.P.R., Cortez, P.C., da Silva Barros, A.C., et al: ‘Novel and powerful 3d adaptive crisp active contour method applied in the segmentation of {CT} lung images’, Med. Image Anal., 2017, 35, pp. 503–516 (doi: 10.1016/j.media.2016.09.002).
  11. 11)
      • 5. Razali, M.R.M., Ahmad, N.S., Hassan, R., et al: ‘Sobel and canny edges segmentations for the dental age assessment’. 2014 Int. Conf. on Computer Assisted System in Health, December 2014, pp. 62–66.
  12. 12)
      • 11. Boykov, Y., Jolly, M.-P.: ‘Interactive organ segmentation using graph cuts’ (Springer Berlin Heidelberg, Berlin, Heidelberg, 2000), pp. 276–286.
  13. 13)
      • 6. Farhangi, M.M., Frigui, H., Seow, A., et al: ‘3d active contour segmentation based on sparse linear combination of training shapes (scots)’, Trans. Med. Imaging, 2017, PP, (99), pp. 1–1.
  14. 14)
      • 2. Nascimento, J.C., Carneiro, G.: ‘Deep learning on sparse manifolds for faster object segmentation’, Trans. Image Process., 2017, PP, (99), pp. 1–1.
  15. 15)
      • 7. Gao, M., Chen, H., Zheng, S., et al: ‘Texture image segmentation using fused features and active contour’. 2016 23rd Int. Conf. on Pattern Recognition (ICPR), December 2016, pp. 2036–2041.
  16. 16)
      • 15. Gonzalez, R.C., Woods, R.E., Eddins, S.I.: ‘Digital image processing’ (Gatesmark Publishing, Knoxville, 2009, 3rd edn.).