On 2D-3D Image Feature Detections for Image-To-Geometry Registration in Virtual Dental Model

Author(s):  
Hui Tang ◽  
Tai-Chiu Hsung ◽  
Walter Y.H. Lam ◽  
Leo Y. Y. Cheng ◽  
Edmond H.N. Pow
Keyword(s):  
3D Image ◽  
2022 ◽  
Vol 2022 ◽  
pp. 1-8
Author(s):  
Li Xu ◽  
Ling Bai ◽  
Lei Li

Considering the problems of poor effect, long reconstruction time, large mean square error (MSE), low signal-to-noise ratio (SNR), and structural similarity index (SSIM) of traditional methods in three-dimensional (3D) image virtual reconstruction, the effect of 3D image virtual reconstruction based on visual communication is proposed. Using the distribution set of 3D image visual communication feature points, the feature point components of 3D image virtual reconstruction are obtained. By iterating the 3D image visual communication information, the features of 3D image virtual reconstruction in visual communication are decomposed, and the 3D image visual communication model is constructed. Based on the calculation of the difference of 3D image texture feature points, the spatial position relationship of 3D image feature points after virtual reconstruction is calculated to complete the texture mapping of 3D image. The deep texture feature points of 3D image are extracted. According to the description coefficient of 3D image virtual reconstruction in visual communication, the virtual reconstruction results of 3D image are constrained. The virtual reconstruction algorithm of 3D image is designed to realize the virtual reconstruction of 3D image. The results show that when the number of samples is 200, the virtual reconstruction time of this paper method is 2.1 s, and the system running time is 5 s; the SNR of the virtual reconstruction is 35.5 db. The MSE of 3D image virtual reconstruction is 3%, and the SSIM of virtual reconstruction is 1.38%, which shows that this paper method can effectively improve the ability of 3D image virtual reconstruction.


Author(s):  
W. Krakow ◽  
D. A. Smith

The successful determination of the atomic structure of [110] tilt boundaries in Au stems from the investigation of microscope performance at intermediate accelerating voltages (200 and 400kV) as well as a detailed understanding of how grain boundary image features depend on dynamical diffraction processes variation with specimen and beam orientations. This success is also facilitated by improving image quality by digital image processing techniques to the point where a structure image is obtained and each atom position is represented by a resolved image feature. Figure 1 shows an example of a low angle (∼10°) Σ = 129/[110] tilt boundary in a ∼250Å Au film, taken under tilted beam brightfield imaging conditions, to illustrate the steps necessary to obtain the atomic structure configuration from the image. The original image of Fig. 1a shows the regular arrangement of strain-field images associated with the cores of ½ [10] primary dislocations which are separated by ∼15Å.


2009 ◽  
Author(s):  
F. Jacob Seagull ◽  
Peter Miller ◽  
Ivan George ◽  
Paul Mlyniec ◽  
Adrian Park
Keyword(s):  
3D Image ◽  

2014 ◽  
Vol 75 (S 02) ◽  
Author(s):  
Gerlig Widmann ◽  
P. Schullian ◽  
R. Hoermann ◽  
E. Gassner ◽  
H. Riechelmann ◽  
...  

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