scholarly journals The Application and Accuracy of Feature Matching on Automated Cephalometric Superimposition

2020 ◽  
Author(s):  
Yiran Jiang ◽  
Guangying Song ◽  
Xiaonan Yu ◽  
Yuanbo Dou ◽  
Qingfeng Li ◽  
...  
Keyword(s):  
Digital Imaging ◽  
Feature Matching ◽  
Academic Research ◽  
Cranial Base ◽  
Matching Accuracy ◽  
Matching Algorithm ◽  
Automated Method ◽  
Computer Aided ◽  
Pre Treatment ◽  
Maxilla And Mandible

Abstract 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

scholarly journals The Application and Accuracy of Feature Matching on Automated Cephalometric Superimposition

2020 ◽  
Author(s):  
Yiran Jiang ◽  
Guangying Song ◽  
Xiaonan Yu ◽  
Yuanbo Dou ◽  
Qingfeng Li ◽  
...  
Keyword(s):  
Feature Matching ◽  
Academic Research ◽  
Cranial Base ◽  
Matching Accuracy ◽  
Matching Algorithm ◽  
Automated Method ◽  
Computer Aided ◽  
Registration Number ◽  
Pre Treatment ◽  
Maxilla And Mandible

Abstract 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

scholarly journals The Application and Accuracy of Feature Matching on Automated Cephalometric Superimposition

2019 ◽  
Author(s):  
Yiran Jiang ◽  
Guangying Song ◽  
Xiaonan Yu ◽  
Yuanbo Dou ◽  
Qingfeng Li ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Feature Matching ◽  
Academic Research ◽  
Cranial Base ◽  
Matching Method ◽  
Automated Method ◽  
Computer Aided ◽  
Registration Number ◽  
Pre Treatment ◽  
Maxilla And Mandible

Abstract Background: The aim of this study was to establish a computer-aided automated method for cephalometric superimposition and evaluate the accuracy of this method based on free-hand tracing. Methods: 28 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 method. To quantitatively evaluate the differences between the two methods, the manually superimposed patterns were digitized. In addition to registering automated and manual superimposition to digital T1 films, landmark distances between corresponding automated and manual T2 cephalometric landmarks were measured, and differences in manual superimposition among the operators were 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 manual 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 in the era of digital cephalograms and big data. Trial registration: The clinical trial has been registered in April 1st 2016, the registration number is ChiCTR1800017694, URL is: http://www.chictr.org.cn/showproj.aspx?proj=29144

Medical Image Feature Matching Based on Wavelet Transform and SIFT Algorithm

2011 ◽  
Vol 65 ◽  
pp. 497-502
Author(s):  
Yan Wei Wang ◽  
Hui Li Yu
Keyword(s):  
Wavelet Transform ◽  
Wavelet Transforms ◽  
Medical Image ◽  
Feature Matching ◽  
Image Feature ◽  
Scale Invariant ◽  
Sift Algorithm ◽  
Matching Algorithm ◽  
Invariant Feature ◽  
Scale Invariant Feature

A feature matching algorithm based on wavelet transform and SIFT is proposed in this paper, Firstly, Biorthogonal wavelet transforms algorithm is used for medical image to delaminating, and restoration the processed image. Then the SIFT (Scale Invariant Feature Transform) applied in this paper to abstracting key point. Experimental results show that our algorithm compares favorably in high-compressive ratio, the rapid matching speed and low storage of the image, especially for the tilt and rotation conditions.

Feature Matching Algorithm of Trees in Natrual Scenery

2009 ◽  
Author(s):  
Yan Yuqin ◽  
Diao Xunlin ◽  
He Shujuan ◽  
Cheng Lin ◽  
Ma Lvzhou ◽  
...  
Keyword(s):  
Feature Matching ◽  
Matching Algorithm

Recognising Facial Surfaces

Perception ◽  
1991 ◽  
Vol 20 (6) ◽  
pp. 755-769 ◽  
Author(s):  
Vicki Bruce ◽  
Patrick Healey ◽  
Mike Burton ◽  
Tony Doyle ◽  
Anne Coombes ◽  
...  
Keyword(s):  
Computer Aided Design ◽  
Three Dimensional ◽  
Head Model ◽  
Matching Accuracy ◽  
The People ◽  
Computer Aided ◽  
Closed Head ◽  
The Face ◽  
Aided Design ◽  
Dimensional Surface

The extent to which faces depicted as surfaces devoid of pigmentation and with minimal texture cues (‘head models’) could be matched with photographs (when unfamiliar) and identified (when familiar) was examined in three experiments. The head models were obtained by scanning the three-dimensional surface of the face with a laser, and by displaying the surface measured in this way by using standard computer-aided design techniques. Performance in all tasks was above chance but far from ceiling. Experiment 1 showed that matching of unfamiliar head models with photographs was affected by the resolution with which the surface was displayed, suggesting that subjects based their decisions, at least in part, on three-dimensional surface structure. Matching accuracy was also affected by other factors to do with the viewpoints shown in the head models and test photographs, and the type of lighting used to portray the head model. In experiment 2 further evidence for the importance of the nature of the illumination used was obtained, and it was found that the addition of a hairstyle (not that of the target face) did not facilitate matching. In experiment 3 identification of the head models by colleagues of the people shown was compared with identification of photographs where the hair was concealed and eyes were closed. Head models were identified less well than these photographs, suggesting that the difficulties in their recognition are not solely due to the lack of hair. Women's heads were disproportionately difficult to recognise from the head models. The results are discussed in terms of their implications for the use of such three-dimensional head models in forensic and surgical applications.

A monocular visual measurement system for UAV probe-and-drogue autonomous aerial refueling

2018 ◽  
Vol 11 (2) ◽  
pp. 166-180 ◽  
Author(s):  
Long Xin ◽  
Delin Luo ◽  
Han Li
Keyword(s):  
Measurement System ◽  
Feature Matching ◽  
Perspective Transformation ◽  
High Accuracy ◽  
Noise Elimination ◽  
Aerial Refueling ◽  
Visual Measurement ◽  
Matching Algorithm ◽  
Content Type ◽  
Elimination Algorithm

PurposeThe purpose of this paper is to develop a monocular visual measurement system for autonomous aerial refueling (AAR) for unmanned aerial vehicle, which can process images from an infrared camera to estimate the pose of the drogue in the tanker with high accuracy and real-time performance.Design/methodology/approachMethods and techniques for marker detection, feature matching and pose estimation have been designed and implemented in the visual measurement system.FindingsThe simple blob detection (SBD) method is adopted, which outperforms the Laplacian of Gaussian method. And a novel noise-elimination algorithm is proposed for excluding the noise points. Besides, a novel feature matching algorithm based on perspective transformation is proposed. Comparative experimental results indicated the rapidity and effectiveness of the proposed methods.Practical implicationsThe visual measurement system developed in this paper can be applied to estimate the pose of the drogue with a fast speed and high accuracy and it is a feasible measurement strategy which will considerably increase the autonomy and reliability for AAR.Originality/valueThe SBD method is used to detect the features and a novel noise-elimination algorithm is proposed. Besides, a novel feature matching algorithm based on perspective transformation is proposed which is robust and accurate.

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