IEEE Trans Image Process - Compressive source separation: theory and methods for hyperspectral imaging.

Tópicos

{ imag(1947) propos(1133) code(1026) }
{ data(3008) multipl(1320) sourc(1022) }
{ model(3480) simul(1196) paramet(876) }
{ method(1969) cluster(1462) data(1082) }
{ can(774) often(719) complex(702) }
{ imag(2830) propos(1344) filter(1198) }
{ activ(1452) weight(1219) physic(1104) }
{ method(1557) propos(1049) approach(1037) }
{ monitor(1329) mobil(1314) devic(1160) }
{ group(2977) signific(1463) compar(1072) }
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{ method(1219) similar(1157) match(930) }
{ imag(2675) segment(2577) method(1081) }
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{ general(901) number(790) one(736) }
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{ import(1318) role(1303) understand(862) }
{ spatial(1525) area(1432) region(1030) }
{ research(1218) medic(880) student(794) }
{ signal(2180) analysi(812) frequenc(800) }
{ result(1111) use(1088) new(759) }
{ implement(1333) system(1263) develop(1122) }
{ estim(2440) model(1874) function(577) }
{ method(2212) result(1239) propos(1039) }
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{ motion(1329) object(1292) video(1091) }
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{ control(1307) perform(991) simul(935) }
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{ featur(1941) imag(1645) propos(1176) }
{ case(1353) use(1143) diagnosi(1136) }
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{ data(3963) clinic(1234) research(1004) }
{ risk(3053) factor(974) diseas(938) }
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{ system(1050) medic(1026) inform(1018) }
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{ visual(1396) interact(850) tool(830) }
{ compound(1573) activ(1297) structur(1058) }
{ perform(1367) use(1326) method(1137) }
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{ blood(1257) pressur(1144) flow(957) }
{ record(1888) medic(1808) patient(1693) }
{ health(3367) inform(1360) care(1135) }
{ ehr(2073) health(1662) electron(1139) }
{ state(1844) use(1261) util(961) }
{ patient(2837) hospit(1953) medic(668) }
{ model(2656) set(1616) predict(1553) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ cost(1906) reduc(1198) effect(832) }
{ gene(2352) biolog(1181) express(1162) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ drug(1928) target(777) effect(648) }
{ survey(1388) particip(1329) question(1065) }
{ decis(3086) make(1611) patient(1517) }
{ process(1125) use(805) approach(778) }

Resumo

We propose and analyze a new model for hyperspectral images (HSIs) based on the assumption that the whole signal is composed of a linear combination of few sources, each of which has a specific spectral signature, and that the spatial abundance maps of these sources are themselves piecewise smooth and therefore efficiently encoded via typical sparse models. We derive new sampling schemes exploiting this assumption and give theoretical lower bounds on the number of measurements required to reconstruct HSI data and recover their source model parameters. This allows us to segment HSIs into their source abundance maps directly from compressed measurements. We also propose efficient optimization algorithms and perform extensive experimentation on synthetic and real datasets, which reveals that our approach can be used to encode HSI with far less measurements and computational effort than traditional compressive sensing methods.

Resumo Limpo

propos analyz new model hyperspectr imag hsis base assumpt whole signal compos linear combin sourc specif spectral signatur spatial abund map sourc piecewis smooth therefor effici encod via typic spars model deriv new sampl scheme exploit assumpt give theoret lower bound number measur requir reconstruct hsi data recov sourc model paramet allow us segment hsis sourc abund map direct compress measur also propos effici optim algorithm perform extens experiment synthet real dataset reveal approach can use encod hsi far less measur comput effort tradit compress sens method

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