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

Whale optimization algorithm based on lateral inhibition for image matching and vision-guided AUV docking

2020 ◽  
pp. 1-12
Author(s):  
Zheping Yan ◽  
Jinzhong Zhang ◽  
Jialing Tang
Keyword(s):  
Lateral Inhibition ◽  
Optimization Algorithm ◽  
Image Matching ◽  
Autonomous Underwater Vehicle ◽  
Optimal Solution ◽  
Search Space ◽  
Whale Optimization Algorithm ◽  
Inhibition Mechanism ◽  
Whale Optimization ◽  
Accuracy And Stability

The accuracy and stability of relative pose estimation of an autonomous underwater vehicle (AUV) and a target depend on whether the characteristics of the underwater image can be accurately and quickly extracted. In this paper, a whale optimization algorithm (WOA) based on lateral inhibition (LI) is proposed to solve the image matching and vision-guided AUV docking problem. The proposed method is named the LI-WOA. The WOA is motivated by the behavior of humpback whales, and it mainly imitates encircling prey, bubble-net attacking and searching for prey to obtain the globally optimal solution in the search space. The WOA not only balances exploration and exploitation but also has a faster convergence speed, higher calculation accuracy and stronger robustness than other approaches. The lateral inhibition mechanism can effectively perform image enhancement and image edge extraction to improve the accuracy and stability of image matching. The LI-WOA combines the optimization efficiency of the WOA and the matching accuracy of the LI mechanism to improve convergence accuracy and the correct matching rate. To verify its effectiveness and feasibility, the WOA is compared with other algorithms by maximizing the similarity between the original image and the template image. The experimental results show that the LI-WOA has a better average value, a higher correct rate, less execution time and stronger robustness than other algorithms. The LI-WOA is an effective and stable method for solving the image matching and vision-guided AUV docking problem.

scholarly journals Retinal Biometric System using Electromagnetism-like Optimization Algorithm

2019 ◽  
Vol 8 (2S5) ◽  
pp. 7-12
Keyword(s):  
Optimization Algorithm ◽  
Image Matching ◽  
Distance Measure ◽  
Biometric System ◽  
Matching Accuracy ◽  
Segmented Image ◽  
Behavioural Characteristics ◽  
Segmentation Accuracy ◽  
The Individual ◽  
Retinal Fundus

Biometric system is the technology used for the purpose of identifying the physiological and behavioural characteristics of an individual as input, analyzes it and identifies the individual as a genuine or imposter. Among all biometrics, retina based identification is perceived as a robust, unforgeable and reliable form of biometric solution. The blood vasculatures of retina are unique and used as features for retinal biometric system. In this work, an attempt has been made to employ an Electromagnetism-like Optimization Algorithm (EMOA) with Otsu Multilevel Thresholding (MLT) for segmentation of vascular pattern from the retinal fundus images for retinal biometric system. Retinal images are taken from the publicly available database such as DRIVE, STARE and HRF. The original images are subjected to preprocessing. Segmentation is carried out on the preprocessed images using EMOA Based Otsu MLT. This method provides comparatively better segmentation accuracy of 0.974 than other existing methods. Texture and vessel features are extracted from the segmented image. Matching is done between query and enrolled images using Euclidian distance measure. Decision is made using best matched image. This biometric system shows matching accuracy of 97%. Hence, this method could be recommended for retinal biometric system.

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.

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