164 resultados para Digital Image Analysis


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Pós-graduação em Agronomia (Energia na Agricultura) - FCA

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Pós-graduação em Engenharia Mecânica - FEG

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Pós-graduação em Ciências Cartográficas - FCT

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Pós-graduação em Ciência da Computação - IBILCE

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Techniques of image capture have advanced along with the technologies of information and communication and unthinkable numbers of information available and imagery are stored in digital environments. The objective of this study is point out difficulties found in the construction of imagetic representations of digital resources using the instruments available for the treatment of descriptive information. The results we have the mapping of descriptive elements to digital images derived from analyzing of the schemes to guide the construction of descriptive records (AACR2R, ISBD, Graphic Materials, RDA, CDWA, CCO) and the conceptual model FRBRer. The result of this analysis conducted the conceptual model, Functional Requirements for Digital Imagetic Data RFDID to the development of more efficient ways to represent the use of imagery in order to make it available, accessible and recoverable from the data persistence descriptive, flexibility, consistency and integrity as essential requirements for the representation of the digital image.

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With the widespread proliferation of computers, many human activities entail the use of automatic image analysis. The basic features used for image analysis include color, texture, and shape. In this paper, we propose a new shape description method, called Hough Transform Statistics (HTS), which uses statistics from the Hough space to characterize the shape of objects or regions in digital images. A modified version of this method, called Hough Transform Statistics neighborhood (HTSn), is also presented. Experiments carried out on three popular public image databases showed that the HTS and HTSn descriptors are robust, since they presented precision-recall results much better than several other well-known shape description methods. When compared to Beam Angle Statistics (BAS) method, a shape description method that inspired their development, both the HTS and the HTSn methods presented inferior results regarding the precision-recall criterion, but superior results in the processing time and multiscale separability criteria. The linear complexity of the HTS and the HTSn algorithms, in contrast to BAS, make them more appropriate for shape analysis in high-resolution image retrieval tasks when very large databases are used, which are very common nowadays. (C) 2014 Elsevier Inc. All rights reserved.