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Advances in Pattern Recognition - ICAPR 2001: Second International Conference Rio de Janeiro, Brazil, March 11-14, 2001 Proceedings: 2013 (Lecture Notes in Computer Science, 2013) - Softcover
Synopsis
This book constitutes the refereed proceedings of the Second International Conference on Advances in Pattern Recognition, ICAPR 2001, held in Rio de Janeiro, Brazil in March 2001. The 40 revised full papers presented together with three invited papers and two tutorial presentations were carefully reviewed and selected for inclusion in the proceedings. The book is organized in topical sections on neural networks and computational intelligence, character recognition and document analysis, feature selection and analysis, pattern recognition and classification, image and signal processing applications, and image feature analysis and retrieval.
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Advances in Pattern Recognition - ICAPR 2001 : Second International Conference Rio de Janeiro, Brazil, March 11-14, 2001 Proceedings
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Advances in Pattern Recognition - ICAPR 2001: Second International Conference Rio de Janeiro, Brazil, March 11-14, 2001 Proceedings (Lecture Notes in Computer Science, 2013)
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Advances in Pattern Recognition - ICAPR 2001
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Springer Berlin Heidelberg Feb 2001, 2001
ISBN 10: 3540417672
ISBN 13: 9783540417675
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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) 496 pp. Englisch. Seller Inventory # 9783540417675
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Advances in Pattern Recognition - ICAPR 2001 : Second International Conference Rio de Janeiro, Brazil, March 11-14, 2001 Proceedings
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Springer Berlin Heidelberg, 2001
ISBN 10: 3540417672
ISBN 13: 9783540417675
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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). Seller Inventory # 9783540417675
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Advances in Pattern Recognition - ICAPR 2001
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ISBN 13: 9783540417675
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 d. Seller Inventory # 4889429
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Advances in Pattern Recognition: Icapr 2001 : Second International Conference, Rio De Janeiro, Brazil, March 11-14, 2001 : Proceedings
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Advances in Pattern Recognition - ICAPR 2001
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ISBN 10: 3540417672
ISBN 13: 9783540417675
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Taschenbuch. Condition: Neu. Neuware -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)Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 496 pp. Englisch. Seller Inventory # 9783540417675
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Advances in Pattern Recognition - ICAPR 2001: Second International Conference Rio de Janeiro, Brazil, March 11-14, 2001 Proceedings (Lecture Notes in Computer Science)
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Advances in Pattern Recognition - ICAPR 2001: Second International Conference Rio de Janeiro, Brazil, March 11-14, 2001 Proceedings (Lecture Notes in Computer Science, 2013)
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