IEEE Trans Image Process - Robust processing of optical flow of fluids.

Tópicos

{ blood(1257) pressur(1144) flow(957) }
{ framework(1458) process(801) describ(734) }
{ motion(1329) object(1292) video(1091) }
{ imag(2830) propos(1344) filter(1198) }
{ process(1125) use(805) approach(778) }
{ method(984) reconstruct(947) comput(926) }
{ method(1969) cluster(1462) data(1082) }
{ model(3480) simul(1196) paramet(876) }
{ method(2212) result(1239) propos(1039) }
{ featur(1941) imag(1645) propos(1176) }
{ first(2504) two(1366) second(1323) }
{ activ(1138) subject(705) human(624) }
{ estim(2440) model(1874) function(577) }
{ detect(2391) sensit(1101) algorithm(908) }
{ learn(2355) train(1041) set(1003) }
{ signal(2180) analysi(812) frequenc(800) }
{ activ(1452) weight(1219) physic(1104) }
{ data(1737) use(1416) pattern(1282) }
{ bind(1733) structur(1185) ligand(1036) }
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{ take(945) account(800) differ(722) }
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{ compound(1573) activ(1297) structur(1058) }
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{ featur(3375) classif(2383) classifi(1994) }
{ network(2748) neural(1063) input(814) }
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{ model(2220) cell(1177) simul(1124) }
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{ perform(1367) use(1326) method(1137) }
{ studi(1119) effect(1106) posit(819) }
{ record(1888) medic(1808) patient(1693) }
{ health(3367) inform(1360) care(1135) }
{ monitor(1329) mobil(1314) devic(1160) }
{ ehr(2073) health(1662) electron(1139) }
{ state(1844) use(1261) util(961) }
{ research(1218) medic(880) student(794) }
{ 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) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ intervent(3218) particip(2042) group(1664) }
{ 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) }
{ use(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
{ result(1111) use(1088) new(759) }
{ survey(1388) particip(1329) question(1065) }

Resumo

This paper proposes a new approach, coupling physical models and image estimation techniques, for modelling the movement of fluids. The fluid flow is characterized by turbulent movement and dynamically changing patterns which poses challenges to existing optical flow estimation methods. The proposed methodology, which relies on Navier-Stokes equations, is used for processing fluid optical flow by using a succession of stages such as advection, diffusion and mass conservation. A robust diffusion step jointly considering the local data geometry and its statistics is embedded in the proposed framework. The diffusion kernel is Gaussian with the covariance matrix defined by the local second derivatives. Such an anisotropic kernel is able to implicitly detect changes in the vector field orientation and to diffuse accordingly. A new approach is developed for detecting fluid flow structures such as vortices. The proposed methodology is applied on artificially generated vector fields as well as on various image sequences.

Resumo Limpo

paper propos new approach coupl physic model imag estim techniqu model movement fluid fluid flow character turbul movement dynam chang pattern pose challeng exist optic flow estim method propos methodolog reli navierstok equat use process fluid optic flow use success stage advect diffus mass conserv robust diffus step joint consid local data geometri statist embed propos framework diffus kernel gaussian covari matrix defin local second deriv anisotrop kernel abl implicit detect chang vector field orient diffus accord new approach develop detect fluid flow structur vortic propos methodolog appli artifici generat vector field well various imag sequenc

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