25,568 research outputs found

    Ensaio comparativo avançado de arroz de sequeiro em Altamira, Pará - ano agrícola 1998/1999.

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    bitstream/item/52948/1/PesquisaAnd0160001.pd

    BRS Apinajé: cultivar de arroz para a agricultura familiar nas condições de terra firme do Estado do Pará.

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    bitstream/item/27797/1/Com.tec.207.pdfDisponível também on-line

    BRS Jaçanã: cultivar de arroz para áreas de várzea do Estado do Pará.

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    bitstream/item/27800/1/Com.tec.197.pd

    Comportamento da cultivar de arroz BRS Aroma em terra firme no Estado do Pará.

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    bitstream/item/27840/1/Com.Tec.217.pdfVersão eletrônica. 1ª impressão: 2010

    Ensaio comparativo avançado de arroz de sequeiro em Capitão Poço, Pará - ano agrícola 1998/1999.

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    bitstream/item/52949/1/PesquisaAnd0150001.pd

    BRS Sertaneja: cultivar precoce de arroz para terra firme do Estado do Pará.

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    bitstream/item/27808/1/Com.tec.198.pd

    Performing edge detection by difference of Gaussians using q-Gaussian kernels

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    In image processing, edge detection is a valuable tool to perform the extraction of features from an image. This detection reduces the amount of information to be processed, since the redundant information (considered less relevant) can be unconsidered. The technique of edge detection consists of determining the points of a digital image whose intensity changes sharply. This changes are due to the discontinuities of the orientation on a surface for example. A well known method of edge detection is the Difference of Gaussians (DoG). The method consists of subtracting two Gaussians, where a kernel has a standard deviation smaller than the previous one. The convolution between the subtraction of kernels and the input image results in the edge detection of this image. This paper introduces a method of extracting edges using DoG with kernels based on the q-Gaussian probability distribution, derived from the q-statistic proposed by Constantino Tsallis. To demonstrate the method's potential, we compare the introduced method with the traditional DoG using Gaussians kernels. The results showed that the proposed method can extract edges with more accurate details.Comment: 5 pages, 5 figures, IC-MSQUARE 201
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