IEEE Trans Pattern Anal Mach Intell - Linear Scale and Rotation Invariant Matching.

Tópicos

{ problem(2511) optim(1539) algorithm(950) }
{ featur(1941) imag(1645) propos(1176) }
{ method(984) reconstruct(947) comput(926) }
{ howev(809) still(633) remain(590) }
{ method(1969) cluster(1462) data(1082) }
{ imag(1057) registr(996) error(939) }
{ sequenc(1873) structur(1644) protein(1328) }
{ sampl(1606) size(1419) use(1276) }
{ assess(1506) score(1403) qualiti(1306) }
{ search(2224) databas(1162) retriev(909) }
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{ drug(1928) target(777) effect(648) }
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{ data(1737) use(1416) pattern(1282) }
{ method(1219) similar(1157) match(930) }
{ featur(3375) classif(2383) classifi(1994) }
{ imag(2830) propos(1344) filter(1198) }
{ framework(1458) process(801) describ(734) }
{ error(1145) method(1030) estim(1020) }
{ system(1050) medic(1026) inform(1018) }
{ state(1844) use(1261) util(961) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
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{ network(2748) neural(1063) input(814) }
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{ take(945) account(800) differ(722) }
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{ motion(1329) object(1292) video(1091) }
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{ control(1307) perform(991) simul(935) }
{ model(2220) cell(1177) simul(1124) }
{ care(1570) inform(1187) nurs(1089) }
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{ studi(1119) effect(1106) posit(819) }
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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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{ ehr(2073) health(1662) electron(1139) }
{ 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) }
{ gene(2352) biolog(1181) express(1162) }
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{ intervent(3218) particip(2042) group(1664) }
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{ can(981) present(881) function(850) }
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{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
{ use(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
{ survey(1388) particip(1329) question(1065) }
{ 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

Matching visual patterns that appear scaled, rotated, and deformed with respect to each other is a challenging problem. We propose a linear formulation that simultaneously matches feature points and estimates global geometrical transformation in a constrained linear space. The linear scheme enables search space reduction based on the lower convex hull property so that the problem size is largely decoupled from the original hard combinatorial problem. Our method therefore can be used to solve large scale problems that involve a very large number of candidate feature points. Without using prepruning in the search, this method is more robust in dealing with weak features and clutter. We apply the proposed method to action detection and image matching. Our results on a variety of images and videos demonstrate that our method is accurate, efficient, and robust.

Resumo Limpo

match visual pattern appear scale rotat deform respect challeng problem propos linear formul simultan match featur point estim global geometr transform constrain linear space linear scheme enabl search space reduct base lower convex hull properti problem size larg decoupl origin hard combinatori problem method therefor can use solv larg scale problem involv larg number candid featur point without use preprun search method robust deal weak featur clutter appli propos method action detect imag match result varieti imag video demonstr method accur effici robust

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