Redukcija oblaka tačaka primenom SVM metode
Point cloud reduction using support vector machines
Апстракт
Ovaj rad istražuje mogućnosti smanjenja oblaka tačaka pomoću regresivne metode potporna vektorima (ε-SVR). ε-SVR je tehnika koja može da sprovede regresiju koristeći različite kernel funkcije (sigmoid, radial basis function, B-spline, spline, itd.) i pogodna je za detekcije ravnih regiona i regiona sa visokim neravninama. Korišćenjem ε-SVR gustina sačuvanih tačaka je prilagodljiva - sačuvane tačke su gušće u zakrivljenom a retke u ravnim regionima skeniranih podataka. Podešavanje regresivne greške, broj sačuvanih tačaka se mogu podešavati.
This paper explores the possibilities of point cloud reduction using ε insensitive support vector regression (ε-SVR). ε-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 ε-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:
SVM metoda / reverzibilno inženjerstvo / redukcija oblaka tačaka / support vector machines / reverse engineering / point cloud reductionИзвор:
Journal of Production Engineering, 2012, 15, 2, 59-62Издавач:
- Univerzitet u Novom Sadu - Fakultet tehničkih nauka - Departman za proizvodno mašinstvo, Novi Sad
Колекције
Институција/група
Mašinski fakultetTY - JOUR AU - Jakovljević, Živana PY - 2012 UR - https://machinery.mas.bg.ac.rs/handle/123456789/1419 AB - Ovaj rad istražuje mogućnosti smanjenja oblaka tačaka pomoću regresivne metode potporna vektorima (ε-SVR). ε-SVR je tehnika koja može da sprovede regresiju koristeći različite kernel funkcije (sigmoid, radial basis function, B-spline, spline, itd.) i pogodna je za detekcije ravnih regiona i regiona sa visokim neravninama. Korišćenjem ε-SVR gustina sačuvanih tačaka je prilagodljiva - sačuvane tačke su gušće u zakrivljenom a retke u ravnim regionima skeniranih podataka. Podešavanje regresivne greške, broj sačuvanih tačaka se mogu podešavati. AB - This paper explores the possibilities of point cloud reduction using ε insensitive support vector regression (ε-SVR). ε-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 ε-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. PB - Univerzitet u Novom Sadu - Fakultet tehničkih nauka - Departman za proizvodno mašinstvo, Novi Sad T2 - Journal of Production Engineering T1 - Redukcija oblaka tačaka primenom SVM metode T1 - Point cloud reduction using support vector machines EP - 62 IS - 2 SP - 59 VL - 15 UR - https://hdl.handle.net/21.15107/rcub_machinery_1419 ER -
@article{ author = "Jakovljević, Živana", year = "2012", abstract = "Ovaj rad istražuje mogućnosti smanjenja oblaka tačaka pomoću regresivne metode potporna vektorima (ε-SVR). ε-SVR je tehnika koja može da sprovede regresiju koristeći različite kernel funkcije (sigmoid, radial basis function, B-spline, spline, itd.) i pogodna je za detekcije ravnih regiona i regiona sa visokim neravninama. Korišćenjem ε-SVR gustina sačuvanih tačaka je prilagodljiva - sačuvane tačke su gušće u zakrivljenom a retke u ravnim regionima skeniranih podataka. Podešavanje regresivne greške, broj sačuvanih tačaka se mogu podešavati., This paper explores the possibilities of point cloud reduction using ε insensitive support vector regression (ε-SVR). ε-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 ε-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.", publisher = "Univerzitet u Novom Sadu - Fakultet tehničkih nauka - Departman za proizvodno mašinstvo, Novi Sad", journal = "Journal of Production Engineering", title = "Redukcija oblaka tačaka primenom SVM metode, Point cloud reduction using support vector machines", pages = "62-59", number = "2", volume = "15", url = "https://hdl.handle.net/21.15107/rcub_machinery_1419" }
Jakovljević, Ž.. (2012). Redukcija oblaka tačaka primenom SVM metode. in Journal of Production Engineering Univerzitet u Novom Sadu - Fakultet tehničkih nauka - Departman za proizvodno mašinstvo, Novi Sad., 15(2), 59-62. https://hdl.handle.net/21.15107/rcub_machinery_1419
Jakovljević Ž. Redukcija oblaka tačaka primenom SVM metode. in Journal of Production Engineering. 2012;15(2):59-62. https://hdl.handle.net/21.15107/rcub_machinery_1419 .
Jakovljević, Živana, "Redukcija oblaka tačaka primenom SVM metode" in Journal of Production Engineering, 15, no. 2 (2012):59-62, https://hdl.handle.net/21.15107/rcub_machinery_1419 .