Advances in pattern recognition - ICAPR 2001 : second international conference, Rio de Janeiro, Brazil, March 11-14, 2001 : proceedings
The paper is organized as follows: In section 2, we describe the no- orientation-discontinuity interfering model based on a Gaussian stochastic model in analyzing the properties of the interfering strokes. In section 3, we describe the improved canny edge detector with an ed- orientation constraint...
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| Otros Autores: | , , |
| Formato: | Livre numérique |
| Lenguaje: | Anglais |
| Publicado: |
Berlin [etc.] :
Springer
[20..].
Cham : Springer Nature |
| Colección: | Lecture notes in computer science
2013 |
| Materias: | |
| Acceso en línea: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Nota: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Advances in pattern recognition, ICAPR 2001, second international conference, Rio de Janeiro, Brazil, March 11-14, 2001, proceedings, Sameer Singh, Nabeel Murshed, Walter Kropatsch (eds.), 2001, New York, Springer, 1 vol. (XIV-476 p.), Lecture notes in computer science, 3-540-41767-2 • Advances in Pattern Recognition - ICAPR 2001, Texte imprimé, 9783662165126 |
| Sumario: | The paper is organized as follows: In section 2, we describe the no- orientation-discontinuity interfering model based on a Gaussian stochastic model in analyzing the properties of the interfering strokes. In section 3, we describe the improved canny edge detector with an ed- orientation constraint to detect the edges and recover the weak ones of the foreground words and characters; In section 4, we illustrate, discuss and evaluate the experimental results of the proposed method, demonstrating that our algorithm significantly improves the segmentation quality; Section 5 concludes this paper. 2. The norm-orientation-discontinuity interfering stroke model Figure 2 shows three typical samples of original image segments from the original documents and their magnitude of the detected edges respectively. The magnitude of the gradient is converted into the gray level value. The darker the edge is, the larger is the gradient magnitude. It is obvious that the topmost strong edges correspond to foreground edges. It should be noted that, while usually, the foreground writing appears darker than the background image, as shown in sample image Figure 2(a), there are cases where the foreground and background have similar intensities as shown in Figure 2(b), or worst still, the background is more prominent than the foreground as in Figure 2(c). So using only the intensity value is not enough to differentiate the foreground from the background. (a) (b) (c) (d) (e) (f). |
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| Notas: | Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| ISBN: | 9783540447320 (PDF) |
| ISSN: | 1611-3349 |
| Acceso: | Accès en ligne pour les établissements français bénéficiaires des licences nationales Accès soumis à abonnement pour tout autre établissement Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 |