25,568 research outputs found
Ensaio comparativo avançado de arroz de sequeiro em Altamira, Pará - ano agrícola 1998/1999.
bitstream/item/52948/1/PesquisaAnd0160001.pd
BRS Apinajé: cultivar de arroz para a agricultura familiar nas condições de terra firme do Estado do Pará.
bitstream/item/27797/1/Com.tec.207.pdfDisponível também on-line
BRS Jaçanã: cultivar de arroz para áreas de várzea do Estado do Pará.
bitstream/item/27800/1/Com.tec.197.pd
Comportamento da cultivar de arroz BRS Aroma em terra firme no Estado do Pará.
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.
bitstream/item/52949/1/PesquisaAnd0150001.pd
BRS Sertaneja: cultivar precoce de arroz para terra firme do Estado do Pará.
bitstream/item/27808/1/Com.tec.198.pd
CNA 8170: linhagem de arroz adaptada para o ecossistema terra firme do Estado do Pará.
Cópia de trabalho editado em CD-ROM
Performing edge detection by difference of Gaussians using q-Gaussian kernels
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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