IEEE Trans Pattern Anal Mach Intell - Joint Histogram Based Cost Aggregation for Stereo Matching.

Tópicos

{ cost(1906) reduc(1198) effect(832) }
{ method(1219) similar(1157) match(930) }
{ gene(2352) biolog(1181) express(1162) }
{ imag(2830) propos(1344) filter(1198) }
{ method(1969) cluster(1462) data(1082) }
{ studi(1410) differ(1259) use(1210) }
{ spatial(1525) area(1432) region(1030) }
{ imag(1947) propos(1133) code(1026) }
{ can(981) present(881) function(850) }
{ data(1737) use(1416) pattern(1282) }
{ measur(2081) correl(1212) valu(896) }
{ take(945) account(800) differ(722) }
{ learn(2355) train(1041) set(1003) }
{ clinic(1479) use(1117) guidelin(835) }
{ import(1318) role(1303) understand(862) }
{ sampl(1606) size(1419) use(1276) }
{ high(1669) rate(1365) level(1280) }
{ drug(1928) target(777) effect(648) }
{ can(774) often(719) complex(702) }
{ imag(1057) registr(996) error(939) }
{ imag(2675) segment(2577) method(1081) }
{ motion(1329) object(1292) video(1091) }
{ framework(1458) process(801) describ(734) }
{ problem(2511) optim(1539) algorithm(950) }
{ algorithm(1844) comput(1787) effici(935) }
{ method(1557) propos(1049) approach(1037) }
{ data(1714) softwar(1251) tool(1186) }
{ design(1359) user(1324) use(1319) }
{ control(1307) perform(991) simul(935) }
{ search(2224) databas(1162) retriev(909) }
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{ model(3480) simul(1196) paramet(876) }
{ activ(1138) subject(705) human(624) }
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{ model(3404) distribut(989) bayesian(671) }
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{ featur(3375) classif(2383) classifi(1994) }
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{ patient(2315) diseas(1263) diabet(1191) }
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{ assess(1506) score(1403) qualiti(1306) }
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{ surgeri(1148) surgic(1085) robot(1054) }
{ error(1145) method(1030) estim(1020) }
{ chang(1828) time(1643) increas(1301) }
{ concept(1167) ontolog(924) domain(897) }
{ extract(1171) text(1153) clinic(932) }
{ model(2220) cell(1177) simul(1124) }
{ care(1570) inform(1187) nurs(1089) }
{ general(901) number(790) one(736) }
{ method(984) reconstruct(947) comput(926) }
{ featur(1941) imag(1645) propos(1176) }
{ case(1353) use(1143) diagnosi(1136) }
{ howev(809) still(633) remain(590) }
{ data(3963) clinic(1234) research(1004) }
{ risk(3053) factor(974) diseas(938) }
{ perform(999) metric(946) measur(919) }
{ research(1085) discuss(1038) issu(1018) }
{ system(1050) medic(1026) inform(1018) }
{ model(2341) predict(2261) use(1141) }
{ compound(1573) activ(1297) structur(1058) }
{ perform(1367) use(1326) method(1137) }
{ studi(1119) effect(1106) posit(819) }
{ blood(1257) pressur(1144) flow(957) }
{ record(1888) medic(1808) patient(1693) }
{ health(3367) inform(1360) care(1135) }
{ 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) }
{ 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) }
{ analysi(2126) use(1163) compon(1037) }
{ 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) }
{ 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) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

This paper presents a novel method for performing efficient cost aggregation in stereo matching. The cost aggregation problem is re-formulated from a perspective of a histogram, giving us a potential to reduce the complexity of the cost aggregation in stereo matching significantly. Different from previous methods which have tried to reduce the complexity in terms of the size of an image and a matching window, our approach focuses on reducing the computational redundancy which exists among the search range, caused by a repeated filtering for all the hypotheses. Moreover, we also reduce the complexity of the window-based filtering through an efficient sampling scheme inside the matching window. The trade-off between accuracy and complexity is extensively investigated by varying the parameters used in the proposed method. Experimental results show that the proposed method provides high-quality disparity maps with a low complexity and outperforms existing local methods. This work also provides new insights into complexity-constrained stereo matching algorithm design.

Resumo Limpo

paper present novel method perform effici cost aggreg stereo match cost aggreg problem reformul perspect histogram give us potenti reduc complex cost aggreg stereo match signific differ previous method tri reduc complex term size imag match window approach focus reduc comput redund exist among search rang caus repeat filter hypothes moreov also reduc complex windowbas filter effici sampl scheme insid match window tradeoff accuraci complex extens investig vari paramet use propos method experiment result show propos method provid highqual dispar map low complex outperform exist local method work also provid new insight complexityconstrain stereo match algorithm design

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