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https://doi.org/10.2498/cit.2003.01.03

Feature Extraction and Classification from Boundary Representation

David Podgorelec
Borut Žalik

Puni tekst: engleski, pdf (498 KB) str. 41-54 preuzimanja: 788* citiraj
APA 6th Edition
Podgorelec, D. i Žalik, B. (2003). Feature Extraction and Classification from Boundary Representation. Journal of computing and information technology, 11 (1), 41-54. https://doi.org/10.2498/cit.2003.01.03
MLA 8th Edition
Podgorelec, David i Borut Žalik. "Feature Extraction and Classification from Boundary Representation." Journal of computing and information technology, vol. 11, br. 1, 2003, str. 41-54. https://doi.org/10.2498/cit.2003.01.03. Citirano 11.12.2019.
Chicago 17th Edition
Podgorelec, David i Borut Žalik. "Feature Extraction and Classification from Boundary Representation." Journal of computing and information technology 11, br. 1 (2003): 41-54. https://doi.org/10.2498/cit.2003.01.03
Harvard
Podgorelec, D., i Žalik, B. (2003). 'Feature Extraction and Classification from Boundary Representation', Journal of computing and information technology, 11(1), str. 41-54. https://doi.org/10.2498/cit.2003.01.03
Vancouver
Podgorelec D, Žalik B. Feature Extraction and Classification from Boundary Representation. Journal of computing and information technology [Internet]. 2003 [pristupljeno 11.12.2019.];11(1):41-54. https://doi.org/10.2498/cit.2003.01.03
IEEE
D. Podgorelec i B. Žalik, "Feature Extraction and Classification from Boundary Representation", Journal of computing and information technology, vol.11, br. 1, str. 41-54, 2003. [Online]. https://doi.org/10.2498/cit.2003.01.03

Sažetak
In the paper, an algorithm for explicit feature extraction and classification from boundary representation is presented. It operates in two phases: the topological and the geometrical. While the topological part is just an adaptation of an already known algorithm, the geometrical part represents an original and new solution. In this part, the algorithm manipulates with features filled by material and the empty ones. The algorithm classifies extracted features into eight classes. It successfully and efficiently handles voids, nested features and many cases of mutual feature intersections. The time complexity depends on input data, and never exceeds O(n^2).

Hrčak ID: 44763

URI
https://hrcak.srce.hr/44763

Posjeta: 898 *