IEEE Trans Image Process - A Bayesian network model for automatic and interactive image segmentation.

Tópicos

{ imag(2675) segment(2577) method(1081) }
{ model(3404) distribut(989) bayesian(671) }
{ model(2656) set(1616) predict(1553) }
{ featur(1941) imag(1645) propos(1176) }
{ take(945) account(800) differ(722) }
{ bind(1733) structur(1185) ligand(1036) }
{ problem(2511) optim(1539) algorithm(950) }
{ control(1307) perform(991) simul(935) }
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{ result(1111) use(1088) new(759) }
{ method(1557) propos(1049) approach(1037) }
{ intervent(3218) particip(2042) group(1664) }
{ network(2748) neural(1063) input(814) }
{ error(1145) method(1030) estim(1020) }
{ system(1050) medic(1026) inform(1018) }
{ inform(2794) health(2639) internet(1427) }
{ studi(2440) review(1878) systemat(933) }
{ gene(2352) biolog(1181) express(1162) }
{ use(976) code(926) identifi(902) }
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{ imag(1057) registr(996) error(939) }
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{ spatial(1525) area(1432) region(1030) }
{ state(1844) use(1261) util(961) }
{ group(2977) signific(1463) compar(1072) }
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{ visual(1396) interact(850) tool(830) }
{ compound(1573) activ(1297) structur(1058) }
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{ record(1888) medic(1808) patient(1693) }
{ health(3367) inform(1360) care(1135) }
{ model(3480) simul(1196) paramet(876) }
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{ 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) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ sampl(1606) size(1419) use(1276) }
{ first(2504) two(1366) second(1323) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ analysi(2126) use(1163) compon(1037) }
{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
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{ survey(1388) particip(1329) question(1065) }
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{ method(1969) cluster(1462) data(1082) }
{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

We propose a new Bayesian network (BN) model for both automatic and interactive image segmentation. A multilayer BN is constructed from an oversegmentation to model the statistical dependencies among superpixel regions, edge segments, vertices, and their measurements. The BN also incorporates various local constraints to further restrain the relationships among these image entities. Given the BN model and various image measurements, belief propagation is performed to update the probability of each node. Image segmentation is generated by the most probable explanation inference of the true states of both region and edge nodes from the updated BN. Besides the automatic image segmentation, the proposed model can also be used for interactive image segmentation. While existing interactive segmentation (IS) approaches often passively depend on the user to provide exact intervention, we propose a new active input selection approach to provide suggestions for the user's intervention. Such intervention can be conveniently incorporated into the BN model to perform actively IS. We evaluate the proposed model on both the Weizmann dataset and VOC2006 cow images. The results demonstrate that the BN model can be used for automatic segmentation, and more importantly, for actively IS. The experiments also show that the IS with active input selection can improve both the overall segmentation accuracy and efficiency over the IS with passive intervention.

Resumo Limpo

propos new bayesian network bn model automat interact imag segment multilay bn construct oversegment model statist depend among superpixel region edg segment vertic measur bn also incorpor various local constraint restrain relationship among imag entiti given bn model various imag measur belief propag perform updat probabl node imag segment generat probabl explan infer true state region edg node updat bn besid automat imag segment propos model can also use interact imag segment exist interact segment approach often passiv depend user provid exact intervent propos new activ input select approach provid suggest user intervent intervent can conveni incorpor bn model perform activ evalu propos model weizmann dataset voc cow imag result demonstr bn model can use automat segment import activ experi also show activ input select can improv overal segment accuraci effici passiv intervent

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