Point Cloud Reduction Using Support Vector Machines
Апстракт
This paper explores the possibilities of point cloud reduction using \epsilon insensitive support vector regression (\epsilon-SVR). \epsilon-SVR is a technique that can carry out the regression using different kernel functions (sigmoid, radial basis function, B-spline, spline, etc.) and it is suitable for detection of flat regions and regions with high curvature in scanned data. Using \epsilon-SVR the density of preserved points is adaptive – preserved points are denser at highly curved region and rare at flat regions. Adjusting the error cost in the regression, the number of preserved points can be fine tuned.
Кључне речи:
Reverse engineering / point cloud reduction / support vector machinesИзвор:
Proceedings of the 11th International Scientific Conference MMA 2012 Advanced Production Technologies, 2012, 121-124Колекције
Институција/група
Mašinski fakultetTY - CONF AU - Jakovljević, Živana PY - 2012 UR - https://machinery.mas.bg.ac.rs/handle/123456789/5184 AB - This paper explores the possibilities of point cloud reduction using \epsilon insensitive support vector regression (\epsilon-SVR). \epsilon-SVR is a technique that can carry out the regression using different kernel functions (sigmoid, radial basis function, B-spline, spline, etc.) and it is suitable for detection of flat regions and regions with high curvature in scanned data. Using \epsilon-SVR the density of preserved points is adaptive – preserved points are denser at highly curved region and rare at flat regions. Adjusting the error cost in the regression, the number of preserved points can be fine tuned. C3 - Proceedings of the 11th International Scientific Conference MMA 2012 Advanced Production Technologies T1 - Point Cloud Reduction Using Support Vector Machines EP - 124 SP - 121 UR - https://hdl.handle.net/21.15107/rcub_machinery_5184 ER -
@conference{ author = "Jakovljević, Živana", year = "2012", abstract = "This paper explores the possibilities of point cloud reduction using \epsilon insensitive support vector regression (\epsilon-SVR). \epsilon-SVR is a technique that can carry out the regression using different kernel functions (sigmoid, radial basis function, B-spline, spline, etc.) and it is suitable for detection of flat regions and regions with high curvature in scanned data. Using \epsilon-SVR the density of preserved points is adaptive – preserved points are denser at highly curved region and rare at flat regions. Adjusting the error cost in the regression, the number of preserved points can be fine tuned.", journal = "Proceedings of the 11th International Scientific Conference MMA 2012 Advanced Production Technologies", title = "Point Cloud Reduction Using Support Vector Machines", pages = "124-121", url = "https://hdl.handle.net/21.15107/rcub_machinery_5184" }
Jakovljević, Ž.. (2012). Point Cloud Reduction Using Support Vector Machines. in Proceedings of the 11th International Scientific Conference MMA 2012 Advanced Production Technologies, 121-124. https://hdl.handle.net/21.15107/rcub_machinery_5184
Jakovljević Ž. Point Cloud Reduction Using Support Vector Machines. in Proceedings of the 11th International Scientific Conference MMA 2012 Advanced Production Technologies. 2012;:121-124. https://hdl.handle.net/21.15107/rcub_machinery_5184 .
Jakovljević, Živana, "Point Cloud Reduction Using Support Vector Machines" in Proceedings of the 11th International Scientific Conference MMA 2012 Advanced Production Technologies (2012):121-124, https://hdl.handle.net/21.15107/rcub_machinery_5184 .