IEEE Trans Pattern Anal Mach Intell - From Bits to Images: Inversion of Local Binary Descriptors.

Tópicos

{ featur(1941) imag(1645) propos(1176) }
{ can(774) often(719) complex(702) }
{ method(984) reconstruct(947) comput(926) }
{ data(1737) use(1416) pattern(1282) }
{ monitor(1329) mobil(1314) devic(1160) }
{ design(1359) user(1324) use(1319) }
{ system(1050) medic(1026) inform(1018) }
{ data(3963) clinic(1234) research(1004) }
{ research(1085) discuss(1038) issu(1018) }
{ group(2977) signific(1463) compar(1072) }
{ gene(2352) biolog(1181) express(1162) }
{ result(1111) use(1088) new(759) }
{ process(1125) use(805) approach(778) }
{ imag(1947) propos(1133) code(1026) }
{ imag(2830) propos(1344) filter(1198) }
{ patient(2315) diseas(1263) diabet(1191) }
{ assess(1506) score(1403) qualiti(1306) }
{ problem(2511) optim(1539) algorithm(950) }
{ error(1145) method(1030) estim(1020) }
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{ general(901) number(790) one(736) }
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{ first(2504) two(1366) second(1323) }
{ activ(1138) subject(705) human(624) }
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{ perform(999) metric(946) measur(919) }
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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) }
{ 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) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ data(3008) multipl(1320) sourc(1022) }
{ intervent(3218) particip(2042) group(1664) }
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{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ decis(3086) make(1611) patient(1517) }
{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Local Binary Descriptors are becoming more and more popular for image matching tasks, especially when going mobile. While they are extensively studied in this context, their ability to carry enough information in order to infer the original image is seldom addressed. In this work, we leverage an inverse problem approach to show that it is possible to directly reconstruct the image content from Local Binary Descriptors. This process relies on very broad assumptions besides the knowledge of the pattern of the descriptor at hand. This generalizes previous results that required either a prior learning database or non-binarized features. Furthermore, our reconstruction scheme reveals differences in the way different Local Binary Descriptors capture and encode image information. Hence, the potential applications of our work are multiple, ranging from privacy issues caused by eavesdropping image keypoints streamed by mobile devices to the design of better descriptors through the visualization and the analysis of their geometric content.

Resumo Limpo

local binari descriptor becom popular imag match task especi go mobil extens studi context abil carri enough inform order infer origin imag seldom address work leverag invers problem approach show possibl direct reconstruct imag content local binari descriptor process reli broad assumpt besid knowledg pattern descriptor hand general previous result requir either prior learn databas nonbinar featur furthermor reconstruct scheme reveal differ way differ local binari descriptor captur encod imag inform henc potenti applic work multipl rang privaci issu caus eavesdrop imag keypoint stream mobil devic design better descriptor visual analysi geometr content

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