IEEE Trans Image Process - A unified spectral-domain approach for saliency detection and its application to automatic object segmentation.

Tópicos

{ imag(2675) segment(2577) method(1081) }
{ visual(1396) interact(850) tool(830) }
{ drug(1928) target(777) effect(648) }
{ featur(1941) imag(1645) propos(1176) }
{ imag(2830) propos(1344) filter(1198) }
{ algorithm(1844) comput(1787) effici(935) }
{ extract(1171) text(1153) clinic(932) }
{ model(2341) predict(2261) use(1141) }
{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }
{ design(1359) user(1324) use(1319) }
{ studi(1410) differ(1259) use(1210) }
{ research(1085) discuss(1038) issu(1018) }
{ ehr(2073) health(1662) electron(1139) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
{ analysi(2126) use(1163) compon(1037) }
{ structur(1116) can(940) graph(676) }
{ result(1111) use(1088) new(759) }
{ decis(3086) make(1611) patient(1517) }
{ model(3404) distribut(989) bayesian(671) }
{ sequenc(1873) structur(1644) protein(1328) }
{ treatment(1704) effect(941) patient(846) }
{ problem(2511) optim(1539) algorithm(950) }
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{ compound(1573) activ(1297) structur(1058) }
{ state(1844) use(1261) util(961) }
{ group(2977) signific(1463) compar(1072) }
{ first(2504) two(1366) second(1323) }
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{ featur(3375) classif(2383) classifi(1994) }
{ network(2748) neural(1063) input(814) }
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{ take(945) account(800) differ(722) }
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{ framework(1458) process(801) describ(734) }
{ error(1145) method(1030) estim(1020) }
{ chang(1828) time(1643) increas(1301) }
{ learn(2355) train(1041) set(1003) }
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{ data(1714) softwar(1251) tool(1186) }
{ control(1307) perform(991) simul(935) }
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{ care(1570) inform(1187) nurs(1089) }
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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) }
{ import(1318) role(1303) understand(862) }
{ perform(1367) use(1326) method(1137) }
{ studi(1119) effect(1106) posit(819) }
{ blood(1257) pressur(1144) flow(957) }
{ spatial(1525) area(1432) region(1030) }
{ record(1888) medic(1808) patient(1693) }
{ health(3367) inform(1360) care(1135) }
{ model(3480) simul(1196) paramet(876) }
{ monitor(1329) mobil(1314) devic(1160) }
{ 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) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ health(1844) social(1437) communiti(874) }
{ cancer(2502) breast(956) screen(824) }
{ use(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
{ implement(1333) system(1263) develop(1122) }
{ estim(2440) model(1874) function(577) }
{ process(1125) use(805) approach(778) }
{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }

Resumo

In this paper, a visual attention model is incorporated for efficient saliency detection, and the salient regions are employed as object seeds for our automatic object segmentation system. In contrast with existing interactive segmentation approaches that require considerable user interaction, the proposed method does not require it, i.e., the segmentation task is fulfilled in a fully automatic manner. First, we introduce a novel unified spectral-domain approach for saliency detection. Our visual attention model originates from a well-known property of the human visual system that the human visual perception is highly adaptive and sensitive to structural information in images rather than nonstructural information. Then, based on the saliency map, we propose an iterative self-adaptive segmentation framework for more accurate object segmentation. Extensive tests on a variety of cluttered natural images show that the proposed algorithm is an efficient indicator for characterizing the human perception and it can provide satisfying segmentation performance.

Resumo Limpo

paper visual attent model incorpor effici salienc detect salient region employ object seed automat object segment system contrast exist interact segment approach requir consider user interact propos method requir ie segment task fulfil fulli automat manner first introduc novel unifi spectraldomain approach salienc detect visual attent model origin wellknown properti human visual system human visual percept high adapt sensit structur inform imag rather nonstructur inform base salienc map propos iter selfadapt segment framework accur object segment extens test varieti clutter natur imag show propos algorithm effici indic character human percept can provid satisfi segment perform

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