Comput Math Methods Med - Image tracking for the high similarity drug tablets based on light intensity reflective energy and artificial neural network.

Tópicos

{ motion(1329) object(1292) video(1091) }
{ drug(1928) target(777) effect(648) }
{ method(984) reconstruct(947) comput(926) }
{ featur(1941) imag(1645) propos(1176) }
{ network(2748) neural(1063) input(814) }
{ studi(1119) effect(1106) posit(819) }
{ state(1844) use(1261) util(961) }
{ treatment(1704) effect(941) patient(846) }
{ studi(1410) differ(1259) use(1210) }
{ system(1050) medic(1026) inform(1018) }
{ model(3480) simul(1196) paramet(876) }
{ model(3404) distribut(989) bayesian(671) }
{ method(1219) similar(1157) match(930) }
{ featur(3375) classif(2383) classifi(1994) }
{ assess(1506) score(1403) qualiti(1306) }
{ model(2220) cell(1177) simul(1124) }
{ structur(1116) can(940) graph(676) }
{ cancer(2502) breast(956) screen(824) }
{ implement(1333) system(1263) develop(1122) }
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{ measur(2081) correl(1212) valu(896) }
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Resumo

It is obvious that tablet image tracking exerts a notable influence on the efficiency and reliability of high-speed drug mass production, and, simultaneously, it also emerges as a big difficult problem and targeted focus during production monitoring in recent years, due to the high similarity shape and random position distribution of those objectives to be searched for. For the purpose of tracking tablets accurately in random distribution, through using surface fitting approach and transitional vector determination, the calibrated surface of light intensity reflective energy can be established, describing the shape topology and topography details of objective tablet. On this basis, the mathematical properties of these established surfaces have been proposed, and thereafter artificial neural network (ANN) has been employed for classifying those moving targeted tablets by recognizing their different surface properties; therefore, the instantaneous coordinate positions of those drug tablets on one image frame can then be determined. By repeating identical pattern recognition on the next image frame, the real-time movements of objective tablet templates were successfully tracked in sequence. This paper provides reliable references and new research ideas for the real-time objective tracking in the case of drug production practices.

Resumo Limpo

obvious tablet imag track exert notabl influenc effici reliabl highspe drug mass product simultan also emerg big difficult problem target focus product monitor recent year due high similar shape random posit distribut object search purpos track tablet accur random distribut use surfac fit approach transit vector determin calibr surfac light intens reflect energi can establish describ shape topolog topographi detail object tablet basi mathemat properti establish surfac propos thereaft artifici neural network ann employ classifi move target tablet recogn differ surfac properti therefor instantan coordin posit drug tablet one imag frame can determin repeat ident pattern recognit next imag frame realtim movement object tablet templat success track sequenc paper provid reliabl refer new research idea realtim object track case drug product practic

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