IEEE Trans Image Process - A semisupervised segmentation model for collections of images.

Tópicos

{ imag(2675) segment(2577) method(1081) }
{ model(3480) simul(1196) paramet(876) }
{ estim(2440) model(1874) function(577) }
{ framework(1458) process(801) describ(734) }
{ algorithm(1844) comput(1787) effici(935) }
{ featur(1941) imag(1645) propos(1176) }
{ visual(1396) interact(850) tool(830) }
{ method(2212) result(1239) propos(1039) }
{ network(2748) neural(1063) input(814) }
{ data(2317) use(1299) case(1017) }
{ first(2504) two(1366) second(1323) }
{ sequenc(1873) structur(1644) protein(1328) }
{ imag(2830) propos(1344) filter(1198) }
{ problem(2511) optim(1539) algorithm(950) }
{ howev(809) still(633) remain(590) }
{ high(1669) rate(1365) level(1280) }
{ can(774) often(719) complex(702) }
{ take(945) account(800) differ(722) }
{ clinic(1479) use(1117) guidelin(835) }
{ perform(999) metric(946) measur(919) }
{ perform(1367) use(1326) method(1137) }
{ spatial(1525) area(1432) region(1030) }
{ health(3367) inform(1360) care(1135) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
{ analysi(2126) use(1163) compon(1037) }
{ structur(1116) can(940) graph(676) }
{ drug(1928) target(777) effect(648) }
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{ 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) }
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{ risk(3053) factor(974) diseas(938) }
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{ model(2341) predict(2261) use(1141) }
{ compound(1573) activ(1297) structur(1058) }
{ studi(1119) effect(1106) posit(819) }
{ blood(1257) pressur(1144) flow(957) }
{ record(1888) medic(1808) patient(1693) }
{ 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) }
{ signal(2180) analysi(812) frequenc(800) }
{ 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) }
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{ health(1844) social(1437) communiti(874) }
{ cancer(2502) breast(956) screen(824) }
{ use(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
{ result(1111) use(1088) new(759) }
{ survey(1388) particip(1329) question(1065) }
{ decis(3086) make(1611) patient(1517) }
{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

In this paper, we consider the problem of segmentation of large collections of images. We propose a semisupervised optimization model that determines an efficient segmentation of many input images. The advantages of the model are twofold. First, the segmentation is highly controllable by the user so that the user can easily specify what he/she wants. This is done by allowing the user to provide, either offline or interactively, some (fully or partially) labeled pixels in images as strong priors for the model. Second, the model requires only minimal tuning of model parameters during the initial stage. Once initial tuning is done, the setup can be used to automatically segment a large collection of images that are distinct but share similar features. We will show the mathematical properties of the model such as existence and uniqueness of solution and establish a maximum/minimum principle for the solution of the model. Extensive experiments on various collections of biological images suggest that the proposed model is effective for segmentation and is computationally efficient.

Resumo Limpo

paper consid problem segment larg collect imag propos semisupervis optim model determin effici segment mani input imag advantag model twofold first segment high control user user can easili specifi hesh want done allow user provid either offlin interact fulli partial label pixel imag strong prior model second model requir minim tune model paramet initi stage initi tune done setup can use automat segment larg collect imag distinct share similar featur will show mathemat properti model exist uniqu solut establish maximumminimum principl solut model extens experi various collect biolog imag suggest propos model effect segment comput effici

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