Machine vision based surface roughness assessment system based on the Internet of Things and contourlet transforms
Author:
Publisher
Springer Science and Business Media LLC
Subject
Industrial and Manufacturing Engineering,Modeling and Simulation
Link
https://link.springer.com/content/pdf/10.1007/s12008-023-01515-1.pdf
Reference22 articles.
1. Umamaheswara Raju, R.S., Ramesh, R., Raju, V.R., et al.: Curvelet transforms and flower pollination algorithm based machine vision system for roughness estimation. J. Opt. 47, 243–250 (2018). https://doi.org/10.1007/s12596-018-0457-y
2. Umamaheswara Raju, R.S., Ramachandra Raju, V., Ramesh, R.: Curvelet transform for estimation of machining performance. Optik 131, 615–625 (2017). https://doi.org/10.1016/j.ijleo.2016.11.181
3. Dettori, Lucia, Semler, Lindsay: A comparison of wavelet, ridgelet, and curvelet-based texture classification algorithms in computed tomography. Comput. Biol. Med. 37(4), 486–498 (2007)
4. Kabir, S.M., Bhuiyan, M.I.H.: Correlated-weighted statistically modeled contourlet and curvelet coefficient image-based breast tumor classification using deep learning. Diagnostics (Basel). 13(1), 69 (2022). https://doi.org/10.3390/diagnostics13010069
5. Ansari, R.A., Buddhiraju, K.M.: Textural classification based on wavelet, curvelet and contourlet features. In: 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China, 2016, pp. 2753–2756, https://doi.org/10.1109/IGARSS.2016.7729711
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