Registration algorithm based on image matching for outdoor AR system with fixed viewing position

2006 ◽  
Vol 153 (1) ◽  
pp. 57 ◽  
Author(s):  
L. Lin ◽  
Y. Liu ◽  
W. Zheng ◽  
Y. Wang
Keyword(s):  
Image Matching ◽  
Registration Algorithm ◽  
Viewing Position

scholarly journals Video Superresolution Reconstruction Using Iterative Back Projection with Critical-Point Filters Based Image Matching

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Yixiong Zhang ◽  
Mingliang Tao ◽  
Kewei Yang ◽  
Zhenmiao Deng
Keyword(s):  
Critical Point ◽  
Image Matching ◽  
Video Sequence ◽  
Sliding Window ◽  
Reconstruction Algorithm ◽  
Back Projection ◽  
Registration Algorithm ◽  
Real Video ◽  
Sr Reconstruction ◽  
Iterative Back Projection

To improve the spatial resolution of reconstructed images/videos, this paper proposes a Superresolution (SR) reconstruction algorithm based on iterative back projection. In the proposed algorithm, image matching using critical-point filters (CPF) is employed to improve the accuracy of image registration. First, a sliding window is used to segment the video sequence. CPF based image matching is then performed between frames in the window to obtain pixel-level motion fields. Finally, high-resolution (HR) frames are reconstructed based on the motion fields using iterative back projection (IBP) algorithm. The CPF based registration algorithm can adapt to various types of motions in real video scenes. Experimental results demonstrate that, compared to optical flow based image matching with IBP algorithm, subjective quality improvement and an average PSNR score of 0.53 dB improvement are obtained by the proposed algorithm, when applied to video sequence.

Compositional Analysis of III-V Interface Lattice Images

1980 ◽  
Vol 38 ◽  
pp. 318-319
Author(s):  
A. Olsen ◽  
J.C.H. Spence ◽  
P. Petroff
Keyword(s):  
Crystal Structure ◽  
Image Matching ◽  
Compositional Analysis ◽  
Depth Of Field ◽  
Limit Set ◽  
High Contrast ◽  
Resolution Limit ◽  
Fourier Image ◽  
Lattice Images ◽  
True Structure

Since the point resolution of the JEOL 200CX electron microscope is up = 2.6Å it is not possible to obtain a true structure image of any of the III-V or elemental semiconductors with this machine. Since the information resolution limit set by electronic instability (1) u0 = (2/πλΔ)½ = 1.4Å for Δ = 50Å, it is however possible to obtain, by choice of focus and thickness, clear lattice images both resembling (see figure 2(b)), and not resembling, the true crystal structure (see (2) for an example of a Fourier image which is structurally incorrect). The crucial difficulty in using the information between Up and u0 is the fractional accuracy with which Af and Cs must be determined, and these accuracies Δff/4Δf = (2λu2Δf)-1 and ΔCS/CS = (λ3u4Cs)-1 (for a π/4 phase change, Δff the Fourier image period) are strongly dependent on spatial frequency u. Note that ΔCs(up)/Cs ≈ 10%, independent of CS and λ. Note also that the number n of identical high contrast spurious Fourier images within the depth of field Δz = (αu)-1 (α beam divergence) decreases with increasing high voltage, since n = 2Δz/Δff = θ/α = λu/α (θ the scattering angle). Thus image matching becomes easier in semiconductors at higher voltage because there are fewer high contrast identical images in any focal series.

Research on the Topology Preservation of the Demons Non-rigid Registration Algorithm

2010 ◽  
Vol 36 (1) ◽  
pp. 179-183
Author(s):  
Xiang-Bo LIN ◽  
Tian-Shuang QIU ◽  
Su RUAN ◽  
NICOLIER Frédéric
Keyword(s):  
Topology Preservation ◽  
Rigid Registration ◽  
Registration Algorithm

A SIFT Pruning Algorithm for Efficient Near-Duplicate Image Matching

2010 ◽  
Vol 22 (6) ◽  
pp. 1042-1049 ◽  
Author(s):  
Jinde Wang ◽  
Xiaoyan Li ◽  
Lidan Shou ◽  
Gang Chen
Keyword(s):  
Image Matching ◽  
Pruning Algorithm

Improvement of the adversarial optimization approach for outlier removal in image matching

2013 ◽  
Vol 32 (11) ◽  
pp. 3157-3160
Author(s):  
Zhen-hua XUE ◽  
Ping WANG ◽  
Chu-han ZHANG ◽  
Si-jia CAI
Keyword(s):  
Image Matching ◽  
Optimization Approach ◽  
Outlier Removal
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