title: Image tracking for the high similarity drug tablets based on light intensity reflective energy and artificial neural network.
abstract: 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.
authors.affiliation: School of Mechanical & Electrical Engineering, Guangzhou University, Guangzhou 510006, China.
authors.forename: Zhongwei
authors.initials: Z
authors.lastname: Liang
authors.affiliation: School of Mathematics & Information Science, Shanghai Lixin University of Commerce, Shanghai 201620, China.
authors.forename: Liang
authors.initials: L
authors.lastname: Zhou
authors.affiliation: School of Mechanical & Electrical Engineering, Guangzhou University, Guangzhou 510006, China.
authors.forename: Xiaochu
authors.initials: X
authors.lastname: Liu
authors.affiliation: School of Mechanical & Electrical Engineering, Guangzhou University, Guangzhou 510006, China.
authors.forename: Xiaogang
authors.initials: X
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D002138:Calibration; D002626:Chemistry, Pharmaceutical / Q000379:methods; D007091:Image Processing, Computer-Assisted; D008027:Light; D015233:Models, Statistical; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D013499:Surface Properties; D013607:Tablets; D013678:Technology, Pharmaceutical / Q000379:methods*
chemical_list: D013607:Tablets