IEEE Trans Image Process - Neighborhood Supported Model Level Fuzzy Aggregation for Moving Object Segmentation.

Tópicos

{ model(2656) set(1616) predict(1553) }
{ motion(1329) object(1292) video(1091) }
{ featur(3375) classif(2383) classifi(1994) }
{ data(3008) multipl(1320) sourc(1022) }
{ method(1969) cluster(1462) data(1082) }
{ data(1737) use(1416) pattern(1282) }
{ can(981) present(881) function(850) }
{ method(1219) similar(1157) match(930) }
{ perform(999) metric(946) measur(919) }
{ learn(2355) train(1041) set(1003) }
{ research(1085) discuss(1038) issu(1018) }
{ compound(1573) activ(1297) structur(1058) }
{ time(1939) patient(1703) rate(768) }
{ framework(1458) process(801) describ(734) }
{ problem(2511) optim(1539) algorithm(950) }
{ spatial(1525) area(1432) region(1030) }
{ model(3480) simul(1196) paramet(876) }
{ use(1733) differ(960) four(931) }
{ system(1976) rule(880) can(841) }
{ concept(1167) ontolog(924) domain(897) }
{ model(2220) cell(1177) simul(1124) }
{ general(901) number(790) one(736) }
{ method(984) reconstruct(947) comput(926) }
{ featur(1941) imag(1645) propos(1176) }
{ patient(2837) hospit(1953) medic(668) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ analysi(2126) use(1163) compon(1037) }
{ survey(1388) particip(1329) question(1065) }
{ decis(3086) make(1611) patient(1517) }
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{ detect(2391) sensit(1101) algorithm(908) }
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{ imag(2675) segment(2577) method(1081) }
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{ 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) }
{ 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) }
{ 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) }
{ 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) }
{ drug(1928) target(777) effect(648) }
{ result(1111) use(1088) new(759) }
{ implement(1333) system(1263) develop(1122) }
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{ activ(1452) weight(1219) physic(1104) }
{ method(2212) result(1239) propos(1039) }

Resumo

We propose a new algorithm for moving object detection in the presence of challenging dynamic background conditions. We use a set of fuzzy aggregated multifeature similarity measures applied on multiple models corresponding to multimodal backgrounds. The algorithm is enriched with a neighborhood-supported model initialization strategy for faster convergence. A model level fuzzy aggregation measure driven background model maintenance ensures more robustness. Similarity functions are evaluated between the corresponding elements of the current feature vector and the model feature vectors. Concepts from Sugeno and Choquet integrals are incorporated in our algorithm to compute fuzzy similarities from the ordered similarity function values for each model. Model updating and the foreground/background classification decision is based on the set of fuzzy integrals. Our proposed algorithm is shown to outperform other multi-model background subtraction algorithms. The proposed approach completely avoids explicit offline training to initialize background model and can be initialized with moving objects also. The feature space uses a combination of intensity and statistical texture features for better object localization and robustness. Our qualitative and quantitative studies illustrate the mitigation of varieties of challenging situations by our approach.

Resumo Limpo

propos new algorithm move object detect presenc challeng dynam background condit use set fuzzi aggreg multifeatur similar measur appli multipl model correspond multimod background algorithm enrich neighborhoodsupport model initi strategi faster converg model level fuzzi aggreg measur driven background model mainten ensur robust similar function evalu correspond element current featur vector model featur vector concept sugeno choquet integr incorpor algorithm comput fuzzi similar order similar function valu model model updat foregroundbackground classif decis base set fuzzi integr propos algorithm shown outperform multimodel background subtract algorithm propos approach complet avoid explicit offlin train initi background model can initi move object also featur space use combin intens statist textur featur better object local robust qualit quantit studi illustr mitig varieti challeng situat approach

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