IEEE Trans Image Process - Region merging techniques using information theory statistical measures.

Tópicos

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{ imag(2675) segment(2577) method(1081) }
{ sampl(1606) size(1419) use(1276) }
{ assess(1506) score(1403) qualiti(1306) }
{ search(2224) databas(1162) retriev(909) }
{ analysi(2126) use(1163) compon(1037) }
{ problem(2511) optim(1539) algorithm(950) }
{ studi(1410) differ(1259) use(1210) }
{ signal(2180) analysi(812) frequenc(800) }
{ method(1219) similar(1157) match(930) }
{ error(1145) method(1030) estim(1020) }
{ algorithm(1844) comput(1787) effici(935) }
{ data(3963) clinic(1234) research(1004) }
{ perform(1367) use(1326) method(1137) }
{ ehr(2073) health(1662) electron(1139) }
{ imag(1057) registr(996) error(939) }
{ treatment(1704) effect(941) patient(846) }
{ concept(1167) ontolog(924) domain(897) }
{ clinic(1479) use(1117) guidelin(835) }
{ design(1359) user(1324) use(1319) }
{ health(1844) social(1437) communiti(874) }
{ use(976) code(926) identifi(902) }
{ result(1111) use(1088) new(759) }
{ network(2748) neural(1063) input(814) }
{ motion(1329) object(1292) video(1091) }
{ method(1557) propos(1049) approach(1037) }
{ data(1714) softwar(1251) tool(1186) }
{ general(901) number(790) one(736) }
{ method(984) reconstruct(947) comput(926) }
{ case(1353) use(1143) diagnosi(1136) }
{ research(1085) discuss(1038) issu(1018) }
{ model(2341) predict(2261) use(1141) }
{ health(3367) inform(1360) care(1135) }
{ model(2656) set(1616) predict(1553) }
{ activ(1138) subject(705) human(624) }
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{ perform(999) metric(946) measur(919) }
{ system(1050) medic(1026) inform(1018) }
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{ visual(1396) interact(850) tool(830) }
{ compound(1573) activ(1297) structur(1058) }
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{ spatial(1525) area(1432) region(1030) }
{ record(1888) medic(1808) patient(1693) }
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{ monitor(1329) mobil(1314) devic(1160) }
{ state(1844) use(1261) util(961) }
{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ 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) }
{ gene(2352) biolog(1181) express(1162) }
{ 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) }
{ can(981) present(881) function(850) }
{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
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{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
{ 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

The purpose of the current work is to propose, under a statistical framework, a family of unsupervised region merging techniques providing a set of the most relevant region-based explanations of an image at different levels of analysis. These techniques are characterized by general and nonparametric region models, with neither color nor texture homogeneity assumptions, and a set of innovative merging criteria, based on information theory statistical measures. The scale consistency of the partitions is assured through i) a size regularization term into the merging criteria and a classical merging order, or ii) using a novel scale-based merging order to avoid the region size homogeneity imposed by the use of a size regularization term. Moreover, a partition significance index is defined to automatically determine the subset of most representative partitions from the created hierarchy. Most significant automatically extracted partitions show the ability to represent the semantic content of the image from a human point of view. Finally, a complete and exhaustive evaluation of the proposed techniques is performed, using not only different databases for the two main addressed problems (object-oriented segmentation of generic images and texture image segmentation), but also specific evaluation features in each case: under- and oversegmentation error, and a large set of region-based, pixel-based and error consistency indicators, respectively. Results are promising, outperforming in most indicators both object-oriented and texture state-of-the-art segmentation techniques.

Resumo Limpo

purpos current work propos statist framework famili unsupervis region merg techniqu provid set relev regionbas explan imag differ level analysi techniqu character general nonparametr region model neither color textur homogen assumpt set innov merg criteria base inform theori statist measur scale consist partit assur size regular term merg criteria classic merg order ii use novel scalebas merg order avoid region size homogen impos use size regular term moreov partit signific index defin automat determin subset repres partit creat hierarchi signific automat extract partit show abil repres semant content imag human point view final complet exhaust evalu propos techniqu perform use differ databas two main address problem objectori segment generic imag textur imag segment also specif evalu featur case oversegment error larg set regionbas pixelbas error consist indic respect result promis outperform indic objectori textur stateoftheart segment techniqu

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