Open Access

A Prototype Model for Semantic Segmentation of Curvilinear Meandering Regions by Deconvolutional Neural Networks

   | Jun 05, 2020

Cite

[1] H.-J. He, C. Zheng, and D.-W. Sun, “Image Segmentation Techniques,” in: Computer Vision Technology for Food Quality Evaluation, 2nd edition, Sun D.-W. (ed.). Academic Press, San Diego, 2016, pp. 45–63. https://doi.org/10.1016/B978-0-12-802232-0.00002-510.1016/B978-0-12-802232-0.00002-5Search in Google Scholar

[2] Ç. Kaymak and A. Uçar, “A Brief Survey and an Application of Semantic Image Segmentation for Autonomous Driving,” in: Handbook of Deep Learning Applications. Smart Innovation, Systems and Technologies, Balas V., Roy S., Sharma D., Samui P. (eds). Springer, Cham, 2019, pp. 161–198. https://doi.org/10.1007/978-3-030-11479-4_910.1007/978-3-030-11479-4_9Search in Google Scholar

[3] G. Neuhold, T. Ollmann, S. R. Bulò, and P. Kontschieder, “The Mapillary Vistas dataset for semantic understanding of street scenes,” 2017 IEEE International Conference on Computer Vision, Venice, 2017, pp. 5000–5009. https://doi.org/10.1109/ICCV.2017.53410.1109/ICCV.2017.534Search in Google Scholar

[4] J. Rogowska, “Overview and Fundamentals of Medical Image Segmentation,” in: Handbook of Medical Image Processing and Analysis, 2nd edition, Bankman I. N. (ed.). Academic Press, San Diego, 2009, pp. 73–90. https://doi.org/10.1016/B978-012373904-9.50013-110.1016/B978-012373904-9.50013-1Search in Google Scholar

[5] H. Liu, J. Xu, Y. Wu, Q. Guo, B. Ibragimov, and L. Xing, “Learning deconvolutional deep neural network for high resolution medical image reconstruction,” Information Sciences, vol. 468, pp. 142–154, 2018. https://doi.org/10.1016/j.ins.2018.08.02210.1016/j.ins.2018.08.022Search in Google Scholar

[6] Larsson C. Clustering, in: 5G Networks, Larsson C. (ed.). Academic Press, San Diego, 2018, pp. 123–141. https://doi.org/10.1016/B978-0-12-812707-0.00011-510.1016/B978-0-12-812707-0.00011-5Search in Google Scholar

[7] Shapiro L. G., Stockman G. C. Computer Vision. Prentice-Hall, New Jersey, 2001.Search in Google Scholar

[8] Sethian J. A. A fast marching level set method for monotonically advancing fronts. Proceedings of the National Academy of Sciences, 93(4): 1591–1595, 1996. http://dx.doi.org/10.1073/pnas.93.4.159110.1073/pnas.93.4.15913998611607632Search in Google Scholar

[9] V. Badrinarayanan, A. Kendall, and R. Cipolla, “SegNet: A deep convolutional encoder-decoder architecture for image segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, iss. 12, pp. 2481–2495, 2017. https://doi.org/10.1109/TPAMI.2016.264461510.1109/TPAMI.2016.264461528060704Search in Google Scholar

[10] V. V. Romanuke, “Generator of a toy dataset of multi-polygon monochrome images for rapidly testing and prototyping semantic image segmentation networks,” Electrical, Control and Communication Engineering, vol. 15, no. 2, pp. 1–8, 2019. https://doi.org/10.2478/ecce-2019-000810.2478/ecce-2019-0008Search in Google Scholar

[11] V. V. Romanuke, “An infinitely scalable dataset of single-polygon grayscale images as a fast test platform for semantic image segmentation,” KPI Science News, no. 1, pp. 24–34, 2019. https://doi.org/10.20535/kpisn.2019.1.15725910.20535/kpi-sn.2019.1.157259Search in Google Scholar

eISSN:
2255-8691
Language:
English