Szczegóły publikacji
Opis bibliograficzny
Circular road signs recognition with affine moment invariants and the probabilistic neural classifier / Bogusław CYGANEK // W: Adaptive and natural computing algorithms : 8th international conference, ICANNGA 2007 : Warsaw, Poland, April 11–14, 2007 : proceedings, Pt. 2 / eds. Bartlomiej Beliczynski [et al.]. — Berlin ; Heidelberg : Springer-Verlag, cop. 2007. — (Lecture Notes in Computer Science ; ISSN 0302-9743 ; LNCS 4432). — Opis częśc. wg okł. — ISBN: 978-3-540-71590-0; ISBN: 3-540-71590-8. — S. 508–516. — Bibliogr. s. 515–516, Abstr. — Toż: W: Adaptive and natural computing algorithms [Dokument elektroniczny] : ICANNGA 2007 : 8th international conference : Warsaw, Poland, April 11–14, 2007 : proceedings. — Wersja do Windows. — Dane tekstowe. — [Berlin ; Heidelberg] : Springer, cop. 2007. — 1 dysk optyczny. — (LNCS 4431) ; (LNCS 4432). — ISBN 978-3-540-71589-1 ; ISBN 978-3-540-71590-0. — [Part 1 and 2]. — S. [1–9]. — Wymagania systemowe Adobe Acrobat Reader ; napęd CD-ROM. — Bibliogr. s. [8–9]
Autor
Dane bibliometryczne
| ID BaDAP | 32789 |
|---|---|
| Data dodania do BaDAP | 2007-04-24 |
| Rok publikacji | 2007 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Czasopisma/serie | Lecture Notes in Computer Science, Theoretical Computer Science and General Issues |
Abstract
In this paper the neural classifier for recognition of the circular shaped road signs is presented. This classifier belongs to the road signs recognition module, which in turn is a part of a driver assisting system. The circular shaped prohibition and obligation signs constitute the very important groups within the set of road signs. In this case however, it is not possible for a detector to determine rotation of the shapes that would allow dimension reduction of the search space. Thus the classifier has to be able to properly work with all possible affine deformations. To alleviate this problem we propose to use as features the statistical moments which were shown to be invariant within an affine group of transformations. The classification is performed by the probabilistic neural network which is trained with sign examples extracted from the real traffic scenes. The obtained results show good accuracy of classification and fast operation time.
