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Probabilistic Evaluation of 3D Surfaces Using Statistical Shape Models (SSM)

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Probabilistic Evaluation of 3D Surfaces Using Statistical Shape Models (SSM)

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Pérez, J.; Guardiola Garcia, JL.; Pérez Jiménez, AJ.; Perez-Cortes, J. (2020). Probabilistic Evaluation of 3D Surfaces Using Statistical Shape Models (SSM). Sensors. 20(22):1-16. https://doi.org/10.3390/s20226554

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Título: Probabilistic Evaluation of 3D Surfaces Using Statistical Shape Models (SSM)
Autor: Pérez, Javier Guardiola Garcia, Jose Luis Pérez Jiménez, Alberto José Perez-Cortes, Juan-Carlos
Entidad UPV: Universitat Politècnica de València. Departamento de Informática de Sistemas y Computadores - Departament d'Informàtica de Sistemes i Computadors
Fecha difusión:
Resumen:
[EN] Inspecting a 3D object which shape has elastic manufacturing tolerances in order to find defects is a challenging and time-consuming task. This task usually involves humans, either in the specification stage followed ...[+]
Palabras clave: 3D surface evaluation , 3D reconstruction , Statistical shape model , Quality assessment , 3D metrics
Derechos de uso: Reconocimiento (by)
Fuente:
Sensors. (eissn: 1424-8220 )
DOI: 10.3390/s20226554
Editorial:
MDPI AG
Versión del editor: https://doi.org/10.3390/s20226554
Código del Proyecto:
info:eu-repo/grantAgreement/IVACE//IMAMCN%2F2020%2F1_IA/
Agradecimientos:
This work was partially funded by Generalitat Valenciana through IVACE (Valencian Institute of Business Competitiveness) distributed nominatively to Valencian technological innovation centres under project expedient IMAMCN/2020/1.[+]
Tipo: Artículo

References

Brosed, F. J., Aguilar, J. J., Guillomía, D., & Santolaria, J. (2010). 3D Geometrical Inspection of Complex Geometry Parts Using a Novel Laser Triangulation Sensor and a Robot. Sensors, 11(1), 90-110. doi:10.3390/s110100090

Perez-Cortes, J.-C., Perez, A., Saez-Barona, S., Guardiola, J.-L., & Salvador, I. (2018). A System for In-Line 3D Inspection without Hidden Surfaces. Sensors, 18(9), 2993. doi:10.3390/s18092993

Bi, Z. M., & Wang, L. (2010). Advances in 3D data acquisition and processing for industrial applications. Robotics and Computer-Integrated Manufacturing, 26(5), 403-413. doi:10.1016/j.rcim.2010.03.003 [+]
Brosed, F. J., Aguilar, J. J., Guillomía, D., & Santolaria, J. (2010). 3D Geometrical Inspection of Complex Geometry Parts Using a Novel Laser Triangulation Sensor and a Robot. Sensors, 11(1), 90-110. doi:10.3390/s110100090

Perez-Cortes, J.-C., Perez, A., Saez-Barona, S., Guardiola, J.-L., & Salvador, I. (2018). A System for In-Line 3D Inspection without Hidden Surfaces. Sensors, 18(9), 2993. doi:10.3390/s18092993

Bi, Z. M., & Wang, L. (2010). Advances in 3D data acquisition and processing for industrial applications. Robotics and Computer-Integrated Manufacturing, 26(5), 403-413. doi:10.1016/j.rcim.2010.03.003

Fu, K., Peng, J., He, Q., & Zhang, H. (2020). Single image 3D object reconstruction based on deep learning: A review. Multimedia Tools and Applications, 80(1), 463-498. doi:10.1007/s11042-020-09722-8

Pichat, J., Iglesias, J. E., Yousry, T., Ourselin, S., & Modat, M. (2018). A Survey of Methods for 3D Histology Reconstruction. Medical Image Analysis, 46, 73-105. doi:10.1016/j.media.2018.02.004

Pathak, V. K., Singh, A. K., Sivadasan, M., & Singh, N. K. (2016). Framework for Automated GD&T Inspection Using 3D Scanner. Journal of The Institution of Engineers (India): Series C, 99(2), 197-205. doi:10.1007/s40032-016-0337-7

Bustos, B., Keim, D. A., Saupe, D., Schreck, T., & Vranić, D. V. (2005). Feature-based similarity search in 3D object databases. ACM Computing Surveys, 37(4), 345-387. doi:10.1145/1118890.1118893

Mian, A., Bennamoun, M., & Owens, R. (2009). On the Repeatability and Quality of Keypoints for Local Feature-based 3D Object Retrieval from Cluttered Scenes. International Journal of Computer Vision, 89(2-3), 348-361. doi:10.1007/s11263-009-0296-z

Liu, Z., Zhao, C., Wu, X., & Chen, W. (2017). An Effective 3D Shape Descriptor for Object Recognition with RGB-D Sensors. Sensors, 17(3), 451. doi:10.3390/s17030451

Barra, V., & Biasotti, S. (2013). 3D shape retrieval using Kernels on Extended Reeb Graphs. Pattern Recognition, 46(11), 2985-2999. doi:10.1016/j.patcog.2013.03.019

Xie, J., Dai, G., Zhu, F., Wong, E. K., & Fang, Y. (2017). DeepShape: Deep-Learned Shape Descriptor for 3D Shape Retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(7), 1335-1345. doi:10.1109/tpami.2016.2596722

Lague, D., Brodu, N., & Leroux, J. (2013). Accurate 3D comparison of complex topography with terrestrial laser scanner: Application to the Rangitikei canyon (N-Z). ISPRS Journal of Photogrammetry and Remote Sensing, 82, 10-26. doi:10.1016/j.isprsjprs.2013.04.009

Cook, K. L. (2017). An evaluation of the effectiveness of low-cost UAVs and structure from motion for geomorphic change detection. Geomorphology, 278, 195-208. doi:10.1016/j.geomorph.2016.11.009

Martínez-Carricondo, P., Agüera-Vega, F., Carvajal-Ramírez, F., Mesas-Carrascosa, F.-J., García-Ferrer, A., & Pérez-Porras, F.-J. (2018). Assessment of UAV-photogrammetric mapping accuracy based on variation of ground control points. International Journal of Applied Earth Observation and Geoinformation, 72, 1-10. doi:10.1016/j.jag.2018.05.015

Burdziakowski, P., Specht, C., Dabrowski, P. S., Specht, M., Lewicka, O., & Makar, A. (2020). Using UAV Photogrammetry to Analyse Changes in the Coastal Zone Based on the Sopot Tombolo (Salient) Measurement Project. Sensors, 20(14), 4000. doi:10.3390/s20144000

MARDIA, K. V., & DRYDEN, I. L. (1989). The statistical analysis of shape data. Biometrika, 76(2), 271-281. doi:10.1093/biomet/76.2.271

Heimann, T., & Meinzer, H.-P. (2009). Statistical shape models for 3D medical image segmentation: A review. Medical Image Analysis, 13(4), 543-563. doi:10.1016/j.media.2009.05.004

Ambellan, F., Tack, A., Ehlke, M., & Zachow, S. (2019). Automated segmentation of knee bone and cartilage combining statistical shape knowledge and convolutional neural networks: Data from the Osteoarthritis Initiative. Medical Image Analysis, 52, 109-118. doi:10.1016/j.media.2018.11.009

Avendi, M. R., Kheradvar, A., & Jafarkhani, H. (2016). A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI. Medical Image Analysis, 30, 108-119. doi:10.1016/j.media.2016.01.005

Booth, J., Roussos, A., Ponniah, A., Dunaway, D., & Zafeiriou, S. (2017). Large Scale 3D Morphable Models. International Journal of Computer Vision, 126(2-4), 233-254. doi:10.1007/s11263-017-1009-7

Erus, G., Zacharaki, E. I., & Davatzikos, C. (2014). Individualized statistical learning from medical image databases: Application to identification of brain lesions. Medical Image Analysis, 18(3), 542-554. doi:10.1016/j.media.2014.02.003

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