IEEE Trans Image Process - Video compressive sensing using Gaussian mixture models.

Tópicos

{ imag(1947) propos(1133) code(1026) }
{ error(1145) method(1030) estim(1020) }
{ model(3480) simul(1196) paramet(876) }
{ motion(1329) object(1292) video(1091) }
{ algorithm(1844) comput(1787) effici(935) }
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{ time(1939) patient(1703) rate(768) }
{ survey(1388) particip(1329) question(1065) }
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{ detect(2391) sensit(1101) algorithm(908) }
{ inform(2794) health(2639) internet(1427) }
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{ imag(1057) registr(996) error(939) }
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{ method(1219) similar(1157) match(930) }
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{ imag(2830) propos(1344) filter(1198) }
{ network(2748) neural(1063) input(814) }
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{ method(984) reconstruct(947) comput(926) }
{ search(2224) databas(1162) retriev(909) }
{ featur(1941) imag(1645) propos(1176) }
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{ data(3963) clinic(1234) research(1004) }
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{ risk(3053) factor(974) diseas(938) }
{ perform(999) metric(946) measur(919) }
{ research(1085) discuss(1038) issu(1018) }
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{ compound(1573) activ(1297) structur(1058) }
{ perform(1367) use(1326) method(1137) }
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{ record(1888) medic(1808) patient(1693) }
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{ 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) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ data(3008) multipl(1320) sourc(1022) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ 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) }
{ cancer(2502) breast(956) screen(824) }
{ use(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
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{ implement(1333) system(1263) develop(1122) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ process(1125) use(805) approach(778) }
{ method(1969) cluster(1462) data(1082) }

Resumo

A Gaussian mixture model (GMM)-based algorithm is proposed for video reconstruction from temporally compressed video measurements. The GMM is used to model spatio-temporal video patches, and the reconstruction can be efficiently computed based on analytic expressions. The GMM-based inversion method benefits from online adaptive learning and parallel computation. We demonstrate the efficacy of the proposed inversion method with videos reconstructed from simulated compressive video measurements, and from a real compressive video camera. We also use the GMM as a tool to investigate adaptive video compressive sensing, i.e., adaptive rate of temporal compression.

Resumo Limpo

gaussian mixtur model gmmbase algorithm propos video reconstruct tempor compress video measur gmm use model spatiotempor video patch reconstruct can effici comput base analyt express gmmbase invers method benefit onlin adapt learn parallel comput demonstr efficaci propos invers method video reconstruct simul compress video measur real compress video camera also use gmm tool investig adapt video compress sens ie adapt rate tempor compress

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