Background: The aim of this study was to establish a computer-aided automated method for cephalometric superimposition and to evaluate the accuracy of this method based on free-hand tracing. Methods: Twenty-eight pairs of pre-treatment (T 1 ) and post-treatment (T 2 ) cephalograms were selected. Structural superimpositions of the anterior cranial base, maxilla and mandible were independently completed by three operators performing traditional hand tracing methods and by computerized automation using the feature matching algorithm. To quantitatively evaluate the differences between the two methods, the hand superimposed patterns were digitized. After automated and hand superimposition of T 2 cephalograms to T 1 cephalometric templates, landmark distances between paired automated and hand T 2 cephalometric landmarks were measured. Differences in hand superimposition among the operators were also calculated.Results: The T 2 landmark differences in hand tracing between the operators ranged from 0.61 mm to 1.65 mm for the three types of superimposition. There were no significant differences in accuracy between hand and automated superimposition (p > 0.05). Conclusions:Computer-aided cephalometric superimposition provides comparably accurate results to those of traditional hand tracing and will provide a powerful tool for academic research.
Enhanced by artificial intelligence, industrial robots are becoming more powerful, gaining a large variety of applications in intelligent factories. Pose perception, the aim of which is to obtain the joint coordinates of the robot in the camera coordinate system, has been proposed as a promising technology for multi-robot intelligent interaction. A large number of efforts have been made on studying robot pose perception. However, most of existing studies estimate the position of robot joints in 3D space by just using 2D color images, and perform pose perception by estimating the position of joint key points in the image, which could become infeasible in scenarios with various camera views and background environments. To address the issue, considering the information about the robot itself as a prior knowledge, we propose a novel approach for robot pose perception, named 3D-RPP. We adopt a 3D visual point cloud to estimate the rigid transformation of the camera coordinate system with respect to the robot base coordinate system, which effectively improves the accuracy of the obtained robot joint position in the camera coordinate system. We conduct extensive experiments on ROKAE xMate3 robot to investigate the performance of 3D-RPP, and the experimental results show that 3D-RPP could solve the pose perception problem well.
Background: The aim of this study was to establish a computer-aided automated method for cephalometric superimposition and to evaluate the accuracy of this method based on free-hand tracing. Methods: Twenty-eight pairs of pre-treatment (T1 ) and post-treatment (T2 ) cephalograms were selected. Structural superimpositions of the cranial base, maxilla and mandible were independently completed by three operators performing traditional hand tracing methods and by computerized automation using the feature matching algorithm. To quantitatively evaluate the differences between the two methods, the hand superimposed patterns were digitized. After automated and hand superimposition of T2 cephalograms to T1 cephalometric templates, landmark distances between paired automated and hand T2 cephalometric landmarks were measured. Differences in hand superimposition among the operators were also calculated. Results: The T2 landmark differences in hand tracing between the operators ranged from 0.61 mm to 1.65 mm for the three types of superimposition. There were no significant differences in accuracy between hand and automated superimposition (p > 0.05). Conclusions: Computer-aided cephalometric superimposition provides comparably accurate results to those of traditional hand tracing and will provide a powerful tool for academic research. Keywords: Digital imaging/radiology, Orthodontic(s), Cephalometric superimposition, Feature Matching, Accuracy
Background: The aim of this study was to establish a computer-aided automated method for cephalometric superimposition and to evaluate the accuracy of this method based on free-hand tracing. Methods: Twenty-eight pairs of pre-treatment (T 1 ) and post-treatment (T 2 ) cephalograms were selected. Structural superimpositions of the cranial base, maxilla and mandible were independently completed by three operators performing traditional hand tracing methods and by computerized automation using the feature matching algorithm. To quantitatively evaluate the differences between the two methods, the hand superimposed patterns were digitized. After automated and hand superimposition of T 2 cephalograms to T 1 cephalometric templates, landmark distances between paired automated and hand T 2 cephalometric landmarks were measured. Differences in hand superimposition among the operators were also calculated. Results: The T 2 landmark differences in hand tracing between the operators ranged from 0.61 mm to 1.65 mm for the three types of superimposition. There were no significant differences in accuracy between hand and automated superimposition (p > 0.05). Conclusions: Computer-aided cephalometric superimposition provides comparably accurate results to those of traditional hand tracing and will provide a powerful tool for academic research. Trial registration: The clinical trial was registered on April 1 st 2016. The registration number is ChiCTR1800017694, and URL is: http://www.chictr.org.cn/showproj.aspx?proj=29144 Keywords: Digital imaging/radiology, Orthodontic(s), Cephalometric superimposition, Feature Matching, Accuracy
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