IEEE Trans Image Process - Supervised ordering in IRp: application to morphological processing of hyperspectral images.

Tópicos

{ learn(2355) train(1041) set(1003) }
{ medic(1828) order(1363) alert(1069) }
{ framework(1458) process(801) describ(734) }
{ imag(2830) propos(1344) filter(1198) }
{ take(945) account(800) differ(722) }
{ imag(2675) segment(2577) method(1081) }
{ can(981) present(881) function(850) }
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{ data(3963) clinic(1234) research(1004) }
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{ method(2212) result(1239) propos(1039) }

Resumo

A novel approach for vector ordering is introduced in this paper. The generic framework is based on a supervised learning formulation which leads to reduced orderings. A training set for the background and another training set for the foreground are needed as well as a supervised method to construct the ordering mapping. Two particular cases of learning techniques are considered in detail: 1) kriging-based vector ordering and 2) support vector machines-based vector ordering. These supervised orderings may then be used for the extension of mathematical morphology to vector images. In particular, in this paper, we focus on the application of morphological processing to hyperspectral images, illustrating the performance with practical examples.

Resumo Limpo

novel approach vector order introduc paper generic framework base supervis learn formul lead reduc order train set background anoth train set foreground need well supervis method construct order map two particular case learn techniqu consid detail krigingbas vector order support vector machinesbas vector order supervis order may use extens mathemat morpholog vector imag particular paper focus applic morpholog process hyperspectr imag illustr perform practic exampl

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