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.

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 Multi-Objective Multidisciplinary Design Optimization of a Robotic Fish System

2021 ◽  
Vol 9 (5) ◽  
pp. 478
Author(s):  
Hao Chen ◽  
Weikun Li ◽  
Weicheng Cui ◽  
Ping Yang ◽  
Linke Chen
Keyword(s):  
Design Optimization ◽  
Optimization Algorithm ◽  
Multidisciplinary Design Optimization ◽  
Robotic Fish ◽  
Multidisciplinary Design ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Cfd Method ◽  
The Individual

Biomimetic robotic fish systems have attracted huge attention due to the advantages of flexibility and adaptability. They are typically complex systems that involve many disciplines. The design of robotic fish is a multi-objective multidisciplinary design optimization problem. However, the research on the design optimization of robotic fish is rare. In this paper, by combining an efficient multidisciplinary design optimization approach and a novel multi-objective optimization algorithm, a multi-objective multidisciplinary design optimization (MMDO) strategy named IDF-DMOEOA is proposed for the conceptual design of a three-joint robotic fish system. In the proposed IDF-DMOEOA strategy, the individual discipline feasible (IDF) approach is adopted. A novel multi-objective optimization algorithm, disruption-based multi-objective equilibrium optimization algorithm (DMOEOA), is utilized as the optimizer. The proposed MMDO strategy is first applied to the design optimization of the robotic fish system, and the robotic fish system is decomposed into four disciplines: hydrodynamics, propulsion, weight and equilibrium, and energy. The computational fluid dynamics (CFD) method is employed to predict the robotic fish’s hydrodynamics characteristics, and the backpropagation neural network is adopted as the surrogate model to reduce the CFD method’s computational expense. The optimization results indicate that the optimized robotic fish shows better performance than the initial design, proving the proposed IDF-DMOEOA strategy’s effectiveness.

Multimodal of Ear and Face Biometric Recognition Using AARK Threshold segmentation and Classifier with Score level fusion

2021 ◽  
Author(s):  
SANTHAM BHARATHY ALAGARSAMY ◽  
Kalpana Murugan
Keyword(s):  
Projection Data ◽  
Biometric System ◽  
Biometric Recognition ◽  
Data Set ◽  
Score Level Fusion ◽  
Multimodal Biometric System ◽  
The Individual ◽  
Individual Scores ◽  
Face Biometrics ◽  
Level Fusion

Abstract More than one biometric methodology of an individual is utilized by a multimodal biometric system to moderate a portion of the impediments of a unimodal biometric system and upgrade its precision, security, and so forth. In this paper, an incorporated multimodal biometric system has proposed for the identification of people utilizing ear and face as input and pre-preparing, ring projection, data standardization, AARK limit division, extraction of DWT highlights and classifiers are utilized. Afterward, singular matches gathered from the different modalities produce the individual scores. The proposed framework indicated got brings about the investigations than singular ear and face biometrics tried. To certify the individual as genuine or an impostor, the eventual outcomes are then utilized. On the IIT Delhi ear information base and ORL face data set, the proposed framework has checked and indicated an individual exactness of 96.24%

scholarly journals Multi-Biometric System Based on Cutting-Edge Equipment for Experimental Contactless Verification

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3709 ◽  
Author(s):  
Lukas Kolda ◽  
Ondrej Krejcar ◽  
Ali Selamat ◽  
Kamil Kuca ◽  
Oluwaseun Fadeyi
Keyword(s):  
Hamming Distance ◽  
Blood Stream ◽  
Data Generation ◽  
Biometric System ◽  
Biometric Verification ◽  
Verification Methods ◽  
Theoretical Evidence ◽  
The Individual ◽  
Modified Hausdorff Distance ◽  
Individual Metrics

Biometric verification methods have gained significant popularity in recent times, which has brought about their extensive usage. In light of theoretical evidence surrounding the development of biometric verification, we proposed an experimental multi-biometric system for laboratory testing. First, the proposed system was designed such that it was able to identify and verify a user through the hand contour, and blood flow (blood stream) at the upper part of the hand. Next, we detailed the hard and software solutions for the system. A total of 40 subjects agreed to be a part of data generation team, which produced 280 hand images. The core of this paper lies in evaluating individual metrics, which are functions of frequency comparison of the double type faults with the EER (Equal Error Rate) values. The lowest value was measured for the case of the modified Hausdorff distance metric - Maximally Helicity Violating (MHV). Furthermore, for the verified biometric characteristics (Hamming distance and MHV), appropriate and suitable metrics have been proposed and experimented to optimize system precision. Thus, the EER value for the designed multi-biometric system in the context of this work was found to be 5%, which proves that metrics consolidation increases the precision of the multi-biometric system. Algorithms used for the proposed multi-biometric device shows that the individual metrics exhibit significant accuracy but perform better on consolidation, with a few shortcomings.

scholarly journals A Novel Multi-Objective Five-Elements Cycle Optimization Algorithm

Algorithms ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 244 ◽  
Author(s):  
Chunling Ye ◽  
Zhengyan Mao ◽  
Mandan Liu
Keyword(s):  
Optimization Algorithm ◽  
Test Suite ◽  
Nsga Ii ◽  
Pareto Solution ◽  
Multi Objective ◽  
Chinese Traditional Culture ◽  
Knee Point ◽  
Five Elements ◽  
Fish Group ◽  
The Individual

Inspired by the mechanism of generation and restriction among five elements in Chinese traditional culture, we present a novel Multi-Objective Five-Elements Cycle Optimization algorithm (MOFECO). During the optimization process of MOFECO, we use individuals to represent the elements. At each iteration, we first divide the population into several cycles, each of which contains several individuals. Secondly, for every individual in each cycle, we judge whether to update it according to the force exerted on it by other individuals in the cycle. In the case of an update, a local or global update is selected by a dynamically adjustable probability P s ; otherwise, the individual is retained. Next, we perform combined mutation operations on the updated individuals, so that a new population contains both the reserved and updated individuals for the selection operation. Finally, the fast non-dominated sorting method is adopted on the current population to obtain an optimal Pareto solution set. The parameters’ comparison of MOFECO is given by an experiment and also the performance of MOFECO is compared with three classic evolutionary algorithms Non-dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Particle Swarm Optimization algorithm (MOPSO), Pareto Envelope-based Selection Algorithm II (PESA-II) and two latest algorithms Knee point-driven Evolutionary Algorithm (KnEA) and Non-dominated Sorting and Local Search (NSLS) on solving test function sets Zitzler et al’s Test suite (ZDT), Deb et al’s Test suite (DTLZ), Walking Fish Group (WFG) and Many objective Function (MaF). The experimental results indicate that the proposed MOFECO can approach the true Pareto-optimal front with both better diversity and convergence compared to the five other algorithms.

Image Matching Optimization Based on Taguchi Method and Adaptive Spatial Clustering with SIFT Features

2017 ◽  
Vol 31 (11) ◽  
pp. 1755014 ◽  
Author(s):  
Yuan Xu ◽  
Hehui Lu ◽  
Defu Zhou ◽  
Jiongbin Zheng ◽  
Jianguo Zhang
Keyword(s):  
Taguchi Method ◽  
Image Matching ◽  
Spatial Clustering ◽  
Signal To Noise Ratio ◽  
Matching Accuracy ◽  
Scale Invariant ◽  
Effect Factor ◽  
Optimum Parameters ◽  
Scale Invariant Feature ◽  
Selection Of

A novel image matching algorithm based on both Taguchi method and spatial clustering is proposed to optimize the Scale Invariant Feature Transform (SIFT) matching results. To improve the matching accuracy, adaptive spatial clustering is used. What is more, in order to get the fitting parameters to balance matching accuracy and quantity, Taguchi method is adopted to optimize the key parameter combination including the ratio threshold of Euclidean distance and the constrain parameters in the process of adaptive spatial clustering. Moreover, signal-to-noise ratio (SNR) results are analyzed by variance to get the effect factor which is taken as the basis for the selection of optimized parameters. The optimum parameters combination is obtained eventually. The final experimental results show that the matching quality based on SIFT feature are improved significantly.

An Effective Image Matching Algorithm Based on Rotation Independence

2013 ◽  
Vol 347-350 ◽  
pp. 3685-3690
Author(s):  
Xian Ying Huang ◽  
Wei Wei Chen
Keyword(s):  
Image Matching ◽  
Irregular Shape ◽  
Matching Accuracy ◽  
Similarity Comparison ◽  
Matching Algorithm ◽  
Irregular Shapes ◽  
Irregular Region ◽  
Histogram Intersection ◽  
Intersection Method ◽  
Traditional Image

Traditional image matching algorithms has poor accuracy in image comparing, such as histogram intersection method. A new image matching algorithm based on the similarity comparison of irregular shape is presented in this paper, which divides the image into a number of irregular regions according to different colors, and extracts the boundary points of the irregular region to compose an irregular shape. The direction and distance is used to comparing the two irregular shapes if the rotation of the image is not considered, otherwise circular list is used to ignore the image rotation. It can be used widely. If two irregular shapes are similar, the two images are considered similar. Experiment proves that this method can effectively improve the image matching accuracy.

Stereo Matching Algorithm Based on Edge Feature of Segmented Image

2013 ◽  
Vol 333-335 ◽  
pp. 948-953
Author(s):  
Yi Zhang ◽  
Cheng Liang Huang ◽  
Lian Fa Bai
Keyword(s):  
Stereo Matching ◽  
Mean Shift ◽  
Weighting Factor ◽  
Reference Image ◽  
Matching Accuracy ◽  
Matching Algorithm ◽  
Mean Shift Algorithm ◽  
Fast Speed ◽  
Segmented Image ◽  
Edge Feature

Stereo matching of the disparity discontinuous boundaries and weak texture regions is still a problem of computer vision. Local-based stereo matching algorithm with the advantages of fast speed and high accuracy is the most common method. In order to improve the matching accuracy of the mentioned regions,a stereo matching algorithm based on edge feature of segmented image is proposed. Firstly, the reference image was segmented by Mean-Shift algorithm. Then, support window was dynamically allocated based on edge feature of segmented image. Finally, the disparity distribution of support window was adjusted by introducing weighting factor. The experimental results show that this algorithm can reduce noise and effectively improve the matching accuracy.

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