IEEE Trans Image Process - Outdoor scene image segmentation based on background recognition and perceptual organization.

Tópicos

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{ imag(2675) segment(2577) method(1081) }
{ compound(1573) activ(1297) structur(1058) }
{ imag(2830) propos(1344) filter(1198) }
{ system(1976) rule(880) can(841) }
{ featur(1941) imag(1645) propos(1176) }
{ can(774) often(719) complex(702) }
{ method(1219) similar(1157) match(930) }
{ data(3963) clinic(1234) research(1004) }
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{ age(1611) year(1155) adult(843) }
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{ first(2504) two(1366) second(1323) }
{ activ(1138) subject(705) human(624) }
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{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

In this paper, we propose a novel outdoor scene image segmentation algorithm based on background recognition and perceptual organization. We recognize the background objects such as the sky, the ground, and vegetation based on the color and texture information. For the structurally challenging objects, which usually consist of multiple constituent parts, we developed a perceptual organization model that can capture the nonaccidental structural relationships among the constituent parts of the structured objects and, hence, group them together accordingly without depending on a priori knowledge of the specific objects. Our experimental results show that our proposed method outperformed two state-of-the-art image segmentation approaches on two challenging outdoor databases (Gould data set and Berkeley segmentation data set) and achieved accurate segmentation quality on various outdoor natural scene environments.

Resumo Limpo

paper propos novel outdoor scene imag segment algorithm base background recognit perceptu organ recogn background object sky ground veget base color textur inform structur challeng object usual consist multipl constitu part develop perceptu organ model can captur nonaccident structur relationship among constitu part structur object henc group togeth accord without depend priori knowledg specif object experiment result show propos method outperform two stateoftheart imag segment approach two challeng outdoor databas gould data set berkeley segment data set achiev accur segment qualiti various outdoor natur scene environ

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