IEEE Trans Image Process - Hyperconnections and hierarchical representations for grayscale and multiband image processing.

Tópicos

{ imag(2830) propos(1344) filter(1198) }
{ framework(1458) process(801) describ(734) }
{ imag(1057) registr(996) error(939) }
{ general(901) number(790) one(736) }
{ process(1125) use(805) approach(778) }
{ featur(3375) classif(2383) classifi(1994) }
{ network(2748) neural(1063) input(814) }
{ algorithm(1844) comput(1787) effici(935) }
{ system(1976) rule(880) can(841) }
{ design(1359) user(1324) use(1319) }
{ studi(1410) differ(1259) use(1210) }
{ method(984) reconstruct(947) comput(926) }
{ blood(1257) pressur(1144) flow(957) }
{ first(2504) two(1366) second(1323) }
{ analysi(2126) use(1163) compon(1037) }
{ use(976) code(926) identifi(902) }
{ model(3404) distribut(989) bayesian(671) }
{ inform(2794) health(2639) internet(1427) }
{ method(1219) similar(1157) match(930) }
{ imag(2675) segment(2577) method(1081) }
{ take(945) account(800) differ(722) }
{ assess(1506) score(1403) qualiti(1306) }
{ extract(1171) text(1153) clinic(932) }
{ method(1557) propos(1049) approach(1037) }
{ data(1714) softwar(1251) tool(1186) }
{ control(1307) perform(991) simul(935) }
{ care(1570) inform(1187) nurs(1089) }
{ perform(999) metric(946) measur(919) }
{ research(1085) discuss(1038) issu(1018) }
{ system(1050) medic(1026) inform(1018) }
{ visual(1396) interact(850) tool(830) }
{ record(1888) medic(1808) patient(1693) }
{ model(2656) set(1616) predict(1553) }
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{ group(2977) signific(1463) compar(1072) }
{ gene(2352) biolog(1181) express(1162) }
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{ risk(3053) factor(974) diseas(938) }
{ import(1318) role(1303) understand(862) }
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{ compound(1573) activ(1297) structur(1058) }
{ perform(1367) use(1326) method(1137) }
{ studi(1119) effect(1106) posit(819) }
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{ health(3367) inform(1360) care(1135) }
{ model(3480) simul(1196) paramet(876) }
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{ ehr(2073) health(1662) electron(1139) }
{ state(1844) use(1261) util(961) }
{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ age(1611) year(1155) adult(843) }
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{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
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{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }

Resumo

Connections in image processing are an important notion that describes how pixels can be grouped together according to their spatial relationships and/or their gray-level values. In recent years, several works were devoted to the development of new theories of connections among which hyperconnection (h-connection) is a very promising notion. This paper addresses two major issues of this theory. First, we propose a new axiomatic that ensures that every h-connection generates decompositions that are consistent for image processing and, more precisely, for the design of h-connected filters. Second, we develop a general framework to represent the decomposition of an image into h-connections as a tree that corresponds to the generalization of the connected component tree. Such trees are indeed an efficient and intuitive way to design attribute filters or to perform detection tasks based on qualitative or quantitative attributes. These theoretical developments are applied to a particular fuzzy h-connection, and we test this new framework on several classical applications in image processing, i.e., segmentation, connected filtering, and document image binarization. The experiments confirm the suitability of the proposed approach: It is robust to noise, and it provides an efficient framework to design selective filters.

Resumo Limpo

connect imag process import notion describ pixel can group togeth accord spatial relationship andor graylevel valu recent year sever work devot develop new theori connect among hyperconnect hconnect promis notion paper address two major issu theori first propos new axiomat ensur everi hconnect generat decomposit consist imag process precis design hconnect filter second develop general framework repres decomposit imag hconnect tree correspond general connect compon tree tree inde effici intuit way design attribut filter perform detect task base qualit quantit attribut theoret develop appli particular fuzzi hconnect test new framework sever classic applic imag process ie segment connect filter document imag binar experi confirm suitabl propos approach robust nois provid effici framework design select filter

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