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In den WarenkorbPaperback. Zustand: Very Good. No Jacket. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less 2.51.
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In den WarenkorbPaperback. Zustand: Very Good. We are unable to ship to Canada at this time.Ex-library paperback in very nice condition with the usual markings and attachments.
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In den WarenkorbPaperback. Zustand: Very Good. We are unable to ship to Canada at this time.Ex-library paperback in very nice condition with the usual markings and attachments.
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In den WarenkorbBrand new book. Fast ship. Please provide full street address as we are not able to ship to P O box address.
Anbieter: Universitätsbuchhandlung Herta Hold GmbH, Berlin, Deutschland
Erstausgabe
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In den WarenkorbFirst Edition. 17 x 25 cm. 638 pages. HC Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Sprache: Englisch.
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In den WarenkorbZustand: New.
Verlag: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Berlin, 2001
ISBN 10: 3540417672 ISBN 13: 9783540417675
Sprache: Englisch
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In den WarenkorbPaperback. Zustand: new. Paperback. 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. 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). Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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In den WarenkorbZustand: New.
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In den WarenkorbZustand: As New. Unread book in perfect condition.
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In den WarenkorbZustand: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.
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In den WarenkorbZustand: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.
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In den WarenkorbBrand new book. Fast ship. Please provide full street address as we are not able to ship to P O box address.
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In den WarenkorbZustand: New.
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In den WarenkorbZustand: New.
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In den WarenkorbPaperback. Zustand: Brand New. 1st edition. 476 pages. 9.50x6.25x1.00 inches. In Stock.
Verlag: Springer Berlin Heidelberg, Springer Berlin Heidelberg Feb 2001, 2001
ISBN 10: 3540417672 ISBN 13: 9783540417675
Sprache: Englisch
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In den WarenkorbTaschenbuch. Zustand: 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.
Verlag: Springer Berlin Heidelberg, 2001
ISBN 10: 3540417672 ISBN 13: 9783540417675
Sprache: Englisch
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In den WarenkorbTaschenbuch. Zustand: 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).
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