IEEE Trans Image Process - An edge embedded marker-based watershed algorithm for high spatial resolution remote sensing image segmentation.

Tópicos

{ imag(2675) segment(2577) method(1081) }
{ motion(1329) object(1292) video(1091) }
{ use(1733) differ(960) four(931) }
{ imag(1947) propos(1133) code(1026) }
{ cost(1906) reduc(1198) effect(832) }
{ health(1844) social(1437) communiti(874) }
{ method(1219) similar(1157) match(930) }
{ detect(2391) sensit(1101) algorithm(908) }
{ assess(1506) score(1403) qualiti(1306) }
{ method(1557) propos(1049) approach(1037) }
{ case(1353) use(1143) diagnosi(1136) }
{ import(1318) role(1303) understand(862) }
{ perform(1367) use(1326) method(1137) }
{ spatial(1525) area(1432) region(1030) }
{ sampl(1606) size(1419) use(1276) }
{ imag(2830) propos(1344) filter(1198) }
{ system(1050) medic(1026) inform(1018) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ structur(1116) can(940) graph(676) }
{ result(1111) use(1088) new(759) }
{ method(1969) cluster(1462) data(1082) }
{ model(3404) distribut(989) bayesian(671) }
{ can(774) often(719) complex(702) }
{ data(1737) use(1416) pattern(1282) }
{ inform(2794) health(2639) internet(1427) }
{ system(1976) rule(880) can(841) }
{ measur(2081) correl(1212) valu(896) }
{ imag(1057) registr(996) error(939) }
{ bind(1733) structur(1185) ligand(1036) }
{ sequenc(1873) structur(1644) protein(1328) }
{ featur(3375) classif(2383) classifi(1994) }
{ network(2748) neural(1063) input(814) }
{ patient(2315) diseas(1263) diabet(1191) }
{ take(945) account(800) differ(722) }
{ studi(2440) review(1878) systemat(933) }
{ treatment(1704) effect(941) patient(846) }
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{ framework(1458) process(801) describ(734) }
{ problem(2511) optim(1539) algorithm(950) }
{ error(1145) method(1030) estim(1020) }
{ chang(1828) time(1643) increas(1301) }
{ learn(2355) train(1041) set(1003) }
{ concept(1167) ontolog(924) domain(897) }
{ clinic(1479) use(1117) guidelin(835) }
{ algorithm(1844) comput(1787) effici(935) }
{ extract(1171) text(1153) clinic(932) }
{ data(1714) softwar(1251) tool(1186) }
{ design(1359) user(1324) use(1319) }
{ control(1307) perform(991) simul(935) }
{ model(2220) cell(1177) simul(1124) }
{ care(1570) inform(1187) nurs(1089) }
{ general(901) number(790) one(736) }
{ method(984) reconstruct(947) comput(926) }
{ search(2224) databas(1162) retriev(909) }
{ featur(1941) imag(1645) propos(1176) }
{ howev(809) still(633) remain(590) }
{ data(3963) clinic(1234) research(1004) }
{ studi(1410) differ(1259) use(1210) }
{ risk(3053) factor(974) diseas(938) }
{ perform(999) metric(946) measur(919) }
{ research(1085) discuss(1038) issu(1018) }
{ model(2341) predict(2261) use(1141) }
{ visual(1396) interact(850) tool(830) }
{ compound(1573) activ(1297) structur(1058) }
{ studi(1119) effect(1106) posit(819) }
{ blood(1257) pressur(1144) flow(957) }
{ 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) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ 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) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ can(981) present(881) function(850) }
{ analysi(2126) use(1163) compon(1037) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ use(976) code(926) identifi(902) }
{ drug(1928) target(777) effect(648) }
{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ process(1125) use(805) approach(778) }
{ activ(1452) weight(1219) physic(1104) }
{ method(2212) result(1239) propos(1039) }

Resumo

This correspondence proposes an edge embedded marker-based watershed algorithm for high spatial resolution remote sensing image segmentation. Two improvement techniques are proposed for the two key steps of maker extraction and pixel labeling, respectively, to make it more effective and efficient for high spatial resolution image segmentation. Moreover, the edge information, detected by the edge detector embedded with confidence, is used to direct the two key steps for detecting objects with weak boundary and improving the positional accuracy of the objects boundary. Experiments on different images show that the proposed method has a good generality in producing good segmentation results. It performs well both in retaining the weak boundary and reducing the undesired over-segmentation.

Resumo Limpo

correspond propos edg embed markerbas watersh algorithm high spatial resolut remot sens imag segment two improv techniqu propos two key step maker extract pixel label respect make effect effici high spatial resolut imag segment moreov edg inform detect edg detector embed confid use direct two key step detect object weak boundari improv posit accuraci object boundari experi differ imag show propos method good general produc good segment result perform well retain weak boundari reduc undesir oversegment

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