Fingerprint Image Identification Algorithm Based on Angle Direction of Objects

Abstract In order to reconstruct and identify three-dimensional (3D) images, an image identification algorithm based on a deep learning compensation transformation matrix of main component feature dimensionality reduction is proposed, including line matching with point matching as the base, 3D reconstruction of point and line integration, parallelization automatic differentiation applied to bundle adjustment, parallelization positive definite matrix system solution applied to bundle adjustment, and an improved classifier based on a deep compensation transformation matrix. Based on the INRIA database, the performance and reconstruction effect of the algorithm are verified. The accuracy rate and success rate are compared with L1APG, VTD, CT, MT, etc. The results show that random transformation and re-sampling of samples during training can improve the performance of the classifier prediction algorithm under the condition that the training time is short. The reconstructed image obtained by the algorithm described in this study has a low correlation with the original image, with high number of pixels change rate (NPCR) and unified average changing intensity (UACI) values and low peak signal to noise ratio (PSNR) values. Image reconstruction effect is better with image capacity advantage. Compared with other algorithms, the proposed algorithm has certain advantages in accuracy and success rate with stable performance and good robustness. Therefore, it can be concluded that image recognition based on the dimension reduction of principal component features provides good recognition effect, which is of guiding significance for research in the image recognition field.

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Effectiveness Evaluation of Air Pollution Intelligent Control Based on AI Guided Haze Image Identification Algorithm

2021 5th International Conference on Trends in Electronics and Informatics (ICOEI) ◽

10.1109/icoei51242.2021.9452967 ◽

2021 ◽

Author(s):

Jinpeng Guo ◽

Ting Xu

Keyword(s):

Air Pollution ◽

Intelligent Control ◽

Effectiveness Evaluation ◽

Identification Algorithm ◽

Image Identification

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The Wood Slice Cell Image Identification Algorithm Based on Singular Value Decomposition

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2015.4529 ◽

2015 ◽

Vol 12 (12) ◽

pp. 5372-5378

Author(s):

Yongke Sun ◽

Yong Cao ◽

Fei Xiong ◽

Xiaoguang Yue ◽

Jian Qiu ◽

...

Keyword(s):

Singular Value Decomposition ◽

Singular Value ◽

Identification Algorithm ◽

Image Identification ◽

Cell Image ◽

Value Decomposition

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Fingerprint Image Identification for Crime Detection

2019 International Conference on Communication and Signal Processing (ICCSP) ◽

10.1109/iccsp.2019.8698014 ◽

2019 ◽

Cited By ~ 2

Author(s):

Pavithra R. ◽

K.V. Suresh

Keyword(s):

Fingerprint Image ◽

Image Identification ◽

Crime Detection

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A method for fingerprint image identification based on Gabor filter and power spectrum

Pattern Recognition and Image Analysis ◽

10.1134/s1054661810020124 ◽

2010 ◽

Vol 20 (2) ◽

pp. 201-209 ◽

Cited By ~ 1

Author(s):

S. L. de O. Gonzaga

Keyword(s):

Power Spectrum ◽

Gabor Filter ◽

Fingerprint Image ◽

Image Identification

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Tire Rolling Kinematics Model for an Intelligent Tire Based on an Accelerometer

Tire Science and Technology ◽

10.2346/tire.20.190211 ◽

2020 ◽

Vol 48 (4) ◽

pp. 287-314

Author(s):

Yan Wang ◽

Zhe Liu ◽

Michael Kaliske ◽

Yintao Wei

Keyword(s):

Elastic Deformation ◽

Estimation Algorithm ◽

Force Sensor ◽

Euler Method ◽

Rolling Contact ◽

Body Motion ◽

Identification Algorithm ◽

Active Perception ◽

Kinematics Model ◽

Ring Model

ABSTRACT The idea of intelligent tires is to develop a tire into an active perception component or a force sensor with an embedded microsensor, such as an accelerometer. A tire rolling kinematics model is necessary to link the acceleration measured with the tire body elastic deformation, based on which the tire forces can be identified. Although intelligent tires have attracted wide interest in recent years, a theoretical model for the rolling kinematics of acceleration fields is still lacking. Therefore, this paper focuses on an explicit formulation for the tire rolling kinematics of acceleration, thereby providing a foundation for the force identification algorithms for an accelerometer-based intelligent tire. The Lagrange–Euler method is used to describe the acceleration field and contact deformation of rolling contact structures. Then, the three-axis acceleration vectors can be expressed by coupling rigid body motion and elastic deformation. To obtain an analytical expression of the full tire deformation, a three-dimensional tire ring model is solved with the tire–road deformation as boundary conditions. After parameterizing the ring model for a radial tire, the developed method is applied and validated by comparing the calculated three-axis accelerations with those measured by the accelerometer. Based on the features of acceleration, especially the distinct peak values corresponding to the tire leading and trailing edges, an intelligent tire identification algorithm is established to predict the tire–road contact length and tire vertical load. A simulation and experiments are conducted to verify the accuracy of the estimation algorithm, the results of which demonstrate good agreement. The proposed model provides a solid theoretical foundation for an acceleration-based intelligent tire.

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Localization and Non-Localization Node Identification Using Dream in MANET

SMART MOVES JOURNAL IJOSCIENCE ◽

10.24113/ijoscience.v2i2.77 ◽

2016 ◽

Vol 2 (2) ◽

Author(s):

Amit Singh ◽

Nitin Mishra ◽

Angad Singh

Keyword(s):

Ad Hoc Network ◽

Ad Hoc ◽

Mobile Ad Hoc Network ◽

Fundamental Problem ◽

Identification Algorithm ◽

Mobile Nodes ◽

Position Information ◽

Location Services ◽

Wireless Router ◽

Mobile Ad Hoc

A Wireless Mobile Ad-hoc Network consists of variety of mobile nodes that temporally kind a dynamic infrastructure less network. To modify communication between nodes that don’t have direct radio contact, every node should operate as a wireless router and potential forward knowledge traffic of behalf of the opposite node. In MANET Localization is a fundamental problem. Current localization algorithm mainly focuses on checking the localizability of a network and/or how to localize as many nodes as possible. It could provide accurate position information foe kind of expanding application. Localization provide information about coverage, deployment, routing, location, services, target tracking and rescue If high mobility among the mobile nodes occurs path failure breaks. Hence the location information cannot be predicted. Here we have proposed a localization based algorithm which will help to provide information about the localized and non-localized nodes in a network. In the proposed approach DREAM protocol and AODV protocol are used to find the localizability of a node in a network. DREAM protocol is a location protocol which helps to find the location of a node in a network whereas AODV is a routing protocol it discover route as and when necessary it does not maintain route from every node to every other. To locate the mobile nodes in a n/w an node identification algorithm is used. With the help of this algorithm localized and non-localized node can be easily detected in respect of radio range. This method helps to improve the performance of a module and minimize the location error and achieves improved performance in the form of UDP packet loss, received packet and transmitted packets, throughput, routing overhead, packet delivery fraction. All the simulation done through the NS-2 module and tested the mobile ad-hoc network.

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