Stochastic Flows of Diffeomorphisms and Image Matching

2019 ◽  
pp. 319-342
Author(s):  
Amarjit Budhiraja ◽  
Paul Dupuis
Keyword(s):  
Image Matching ◽  
Stochastic Flows

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.

Image matching method based on perceptual organization of linear features in stereoscopic images

2010 ◽  
Vol 30 (8) ◽  
pp. 2088-2090
Author(s):  
Hai-tao GUO ◽  
Qing XU ◽  
Feng-bo CONG ◽  
Bao-ming ZHANG
Keyword(s):  
Perceptual Organization ◽  
Image Matching ◽  
Matching Method ◽  
Linear Features ◽  
Stereoscopic Images

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

A SAR Image Matching Algorithm Based on Target Detection

2011 ◽  
Vol 30 (12) ◽  
pp. 2818-2821 ◽  
Author(s):  
Hui Zhang ◽  
Jian-guo Wang
Keyword(s):  
Target Detection ◽  
Image Matching ◽  
Sar Image ◽  
Matching Algorithm

An Image Matching Algorithm Based on SCCH Feature Descriptor

2011 ◽  
Vol 33 (9) ◽  
pp. 2152-2157 ◽  
Author(s):  
Yong-he Tang ◽  
Huan-zhang Lu ◽  
Mou-fa Hu
Keyword(s):  
Image Matching ◽  
Feature Descriptor ◽  
Matching Algorithm

Gel Image Matching Using Hopfield Neural Network

2006 ◽  
Vol 13B (3) ◽  
pp. 323-328
Author(s):  
Yukhuu Ankhbayar ◽  
Suk-Hyung Hwang ◽  
Young-Sup Hwang
Keyword(s):  
Neural Network ◽  
Image Matching ◽  
Hopfield Neural Network

Stochastic Flows in Networks.

1987 ◽  
Author(s):  
Richard F. Serfozo
Keyword(s):  
Stochastic Flows ◽  
Flows In Networks

An Image Matching Optimization Algorithm Based on Pixel Shift Clustering RANSAC

2021 ◽  
Author(s):  
Shuhua Ma ◽  
Peikai Guo ◽  
Hairong You ◽  
Ping He ◽  
Guanglin Li ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Image Matching
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