918 resultados para Image of mathematics


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The emergence of digital imaging and of digital networks has made duplication of original artwork easier. Watermarking techniques, also referred to as digital signature, sign images by introducing changes that are imperceptible to the human eye but easily recoverable by a computer program. Usage of error correcting codes is one of the good choices in order to correct possible errors when extracting the signature. In this paper, we present a scheme of error correction based on a combination of Reed-Solomon codes and another optimal linear code as inner code. We have investigated the strength of the noise that this scheme is steady to for a fixed capacity of the image and various lengths of the signature. Finally, we compare our results with other error correcting techniques that are used in watermarking. We have also created a computer program for image watermarking that uses the newly presented scheme for error correction.

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AMS Subj. Classification: 03C05, 08B20

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This article discusses techniques for organization of propaedeutic stage of teaching proof in mathematics course. It identifies types of tasks that allow students of 5–6 classes to form the ability to carry out simple proofs. This article describes each type of tasks features, it gives some examples.

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This article presents the principal results of the doctoral thesis “Recognition of neume notation in historical documents” by Lasko Laskov (Institute of Mathematics and Informatics at Bulgarian Academy of Sciences), successfully defended before the Specialized Academic Council for Informatics and Mathematical Modelling on 07 June 2010.

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2000 Mathematics Subject Classification: 46B26, 46B03, 46B04.

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ACM Computing Classification System (1998): J.2.

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ACM Computing Classification System (1998): I.2.8, I.2.10, I.5.1, J.2.

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ACM Computing Classification System (1998): I.7, I.7.5.

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ACM Computing Classification System (1998): I.2.8 , I.2.10, I.5.1, J.2.

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ACM Computing Classification System (1998): I.7, I.7.5.

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This article presents the principal results of the Ph.D. thesis A Novel Method for Content-Based Image Retrieval in Art Image Collections Utilizing Colour Semantics by Krassimira Ivanova (Institute of Mathematics and Informatics, BAS), successfully defended at Hasselt Uni-versity in Belgium, Faculty of Science, on 15 November 2011.

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The paper presents in brief the Bulgarian Digital Mathematical Library BulDML and the Czech Digital Mathematical Library DML-CZ. Both libraries use the open source software DSpace and both are partners in the European Digital Mathematics Library EuDML. We describe their content and metadata schemas; outline the architecture system and overview the statistics of its use.

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Within the framework of heritage preservation, 3D scanning and modeling for heritage documentation has increased significantly in recent years, mainly due to the evolution of laser and image-based techniques, modeling software, powerful computers and virtual reality. 3D laser acquisition constitutes a real development opportunity for 3D modeling based previously on theoretical data. The representation of the object information rely on the knowledge of its historic and theoretical frame to reconstitute a posteriori its previous states. This project proposes an approach dealing with data extraction based on architectural knowledge and Laser statement informing measurements, the whole leading to 3D reconstruction. The experimented Khmer objects are exposed at Guimet museum in Paris. The purpose of this digital modeling meets the need of exploitable models for simulation projects, prototyping, exhibitions, promoting cultural tourism and particularly for archiving against any likely disaster and as an aided tool for the formulation of virtual museum concept.

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An approach to building a CBIR-system for searching computer tomography images using the methods of wavelet-analysis is presented in this work. The index vectors are constructed on the basis of the local features of the image and on their positions. The purpose of the proposed system is to extract visually similar data from the individual personal records and from analogous analysis of other patients.

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ACM Computing Classification System (1998): I.4.9, I.4.10.