IEEE Trans Image Process - Linear time distances between fuzzy sets with applications to pattern matching and classification.

Tópicos

{ perform(999) metric(946) measur(919) }
{ imag(2830) propos(1344) filter(1198) }
{ system(1976) rule(880) can(841) }
{ method(1219) similar(1157) match(930) }
{ featur(3375) classif(2383) classifi(1994) }
{ perform(1367) use(1326) method(1137) }
{ structur(1116) can(940) graph(676) }
{ method(1557) propos(1049) approach(1037) }
{ general(901) number(790) one(736) }
{ high(1669) rate(1365) level(1280) }
{ process(1125) use(805) approach(778) }
{ framework(1458) process(801) describ(734) }
{ control(1307) perform(991) simul(935) }
{ method(984) reconstruct(947) comput(926) }
{ gene(2352) biolog(1181) express(1162) }
{ can(981) present(881) function(850) }
{ network(2748) neural(1063) input(814) }
{ imag(2675) segment(2577) method(1081) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ signal(2180) analysi(812) frequenc(800) }
{ data(1737) use(1416) pattern(1282) }
{ sequenc(1873) structur(1644) protein(1328) }
{ error(1145) method(1030) estim(1020) }
{ learn(2355) train(1041) set(1003) }
{ data(1714) softwar(1251) tool(1186) }
{ featur(1941) imag(1645) propos(1176) }
{ case(1353) use(1143) diagnosi(1136) }
{ data(3963) clinic(1234) research(1004) }
{ studi(1410) differ(1259) use(1210) }
{ system(1050) medic(1026) inform(1018) }
{ visual(1396) interact(850) tool(830) }
{ data(2317) use(1299) case(1017) }
{ activ(1138) subject(705) human(624) }
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{ take(945) account(800) differ(722) }
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{ model(2220) cell(1177) simul(1124) }
{ care(1570) inform(1187) nurs(1089) }
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{ risk(3053) factor(974) diseas(938) }
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{ record(1888) medic(1808) patient(1693) }
{ health(3367) inform(1360) care(1135) }
{ model(3480) simul(1196) paramet(876) }
{ 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) }
{ 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) }
{ data(3008) multipl(1320) sourc(1022) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ use(976) code(926) identifi(902) }
{ drug(1928) target(777) effect(648) }
{ result(1111) use(1088) new(759) }
{ implement(1333) system(1263) develop(1122) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }
{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

We present four novel point-to-set distances defined for fuzzy or gray-level image data, two based on integration over a-cuts and two based on the fuzzy distance transform. We explore their theoretical properties. Inserting the proposed point-to-set distances in existing definitions of set-to-set distances, among which are the Hausdorff distance and the sum of minimal distances, we define a number of distances between fuzzy sets. These set distances are directly applicable for comparing gray-level images or fuzzy segmented objects, but also for detecting patterns and matching parts of images. The distance measures integrate shape and intensity/membership of observed entities, providing a highly applicable tool for image processing and analysis. Performance evaluation of derived set distances in real image processing tasks is conducted and presented. It is shown that the considered distances have a number of appealing theoretical properties and exhibit very good performance in template matching and object classification for fuzzy segmented images as well as when applied directly on gray-level intensity images. Examples include recognition of hand written digits and identification of virus particles. The proposed set distances perform excellently on the MNIST digit classification task, achieving the best reported error rate for classification using only rigid body transformations and a kNN classifier.

Resumo Limpo

present four novel pointtoset distanc defin fuzzi graylevel imag data two base integr acut two base fuzzi distanc transform explor theoret properti insert propos pointtoset distanc exist definit settoset distanc among hausdorff distanc sum minim distanc defin number distanc fuzzi set set distanc direct applic compar graylevel imag fuzzi segment object also detect pattern match part imag distanc measur integr shape intensitymembership observ entiti provid high applic tool imag process analysi perform evalu deriv set distanc real imag process task conduct present shown consid distanc number appeal theoret properti exhibit good perform templat match object classif fuzzi segment imag well appli direct graylevel intens imag exampl includ recognit hand written digit identif virus particl propos set distanc perform excel mnist digit classif task achiev best report error rate classif use rigid bodi transform knn classifi

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