A Novel Algorithm for Ship Detection Based on Dynamic Fusion Model of Multi-feature and Support Vector Machine

2011 ◽  
Author(s):  
Yu Xia ◽  
Shouhong Wan ◽  
Lihua Yue
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Fusion Model ◽  
Ship Detection ◽  
Novel Algorithm

scholarly journals Texture classification of fabric defects using machine learning

2020 ◽  
Vol 10 (4) ◽  
pp. 4390
Author(s):  
Yassine Ben Salem ◽  
Mohamed Naceur Abdelkrim
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Comparative Study ◽  
Texture Classification ◽  
Support Vector ◽  
Defect Classification ◽  
Analysis Method ◽  
Fabric Defect ◽  
Novel Algorithm

In this paper, a novel algorithm for automatic fabric defect classification was proposed, based on the combination of a texture analysis method and a support vector machine SVM. Three texture methods were used and compared, GLCM, LBP, and LPQ. They were combined with SVM’s classifier. The system has been tested using TILDA database. A comparative study of the performance and the running time of the three methods was carried out. The obtained results are interesting and show that LBP is the best method for recognition and classification and it proves that the SVM is a suitable classifier for such problems. We demonstrate that some defects are easier to classify than others.

People Counting Based on the Analysis of the Mass

2014 ◽  
Vol 945-949 ◽  
pp. 1875-1879
Author(s):  
Tao Li ◽  
Dong Mei Li ◽  
Ren Jie Huang ◽  
Xue Zhu Zhao
Keyword(s):  
Support Vector Machine ◽  
Video Surveillance ◽  
Support Vector ◽  
Svm Classifier ◽  
The Novel ◽  
People Counting ◽  
The People ◽  
Objects Tracking ◽  
Counting Model ◽  
Novel Algorithm

In order to improve the accuracy of people counting in video surveillance, the method for people counting based on the analysis of the mass is proposed. The novel algorithm of objects tracking is designed to aim at people counting, and the people counting model is obtained by training a support vector machine (SVM) classifier with the input of the feature of mass. The experimental results show that the accuracy of counting is over 93%.

A FUSION METHOD FOR PARTIAL FINGERPRINT RECOGNITION

2013 ◽  
Vol 27 (06) ◽  
pp. 1356009 ◽  
Author(s):  
FANGLIN CHEN ◽  
MING LI ◽  
YI ZHANG
Keyword(s):  
Support Vector Machine ◽  
Fingerprint Recognition ◽  
Experimental Result ◽  
Support Vector ◽  
Fingerprint Matching ◽  
Fusion Method ◽  
Fusion Scheme ◽  
Partial Fingerprint ◽  
Simulation Scheme ◽  
Novel Algorithm

Conventional algorithms for fingerprint recognition are mainly based on minutiae information. However, the small number of minutiae in partial fingerprints is still a challenge in fingerprint matching. In this paper, a novel algorithm is proposed to improve the performance of partial fingerprint matching. A simulation scheme was firstly proposed to construct a serial of partial fingerprints with different area. Then, the influence of the fingerprint area in partial fingerprint recognition is studied. By comparing the performance of partial fingerprint recognition with different fingerprint area, some useful conclusions can be drawn: (1) The decrease of the fingerprint area degrades the performance of partial fingerprint recognition; (2) When the fingerprint area decreases, the genuine matching scores will decrease, whereas the imposter matching scores will increase. Based on these observations, we proposed a fusion scheme based on modified support vector machine (SVM) to combine the area information for fingerprint recognition. Experimental result illustrates the effectiveness of the proposed method.

scholarly journals Proposing WPOD-NET combining SVM system for detecting car number plate

2021 ◽  
Vol 10 (3) ◽  
pp. 657
Author(s):  
Phat Nguyen Huu ◽  
Cuong Vu Quoc
Keyword(s):  
Support Vector Machine ◽  
Object Detection ◽  
Detection System ◽  
Support Vector ◽  
Manual Labor ◽  
Wide Angle ◽  
Parking Lots ◽  
Detection Angle ◽  
Smart Parking ◽  
Novel Algorithm

<span lang="EN-US">Nowadays, there are many smart parking lots using plate detection system to control in/out vehicles. However, the disadvantages of systems are a fixed environment and necessity of manual labor and requirement of checkpoints in entrances. To solve the problems, a novel algorithm for wide-angle detecting car number plate using warped planar object detection (WPOD-NET) and a modified support vector machine (SVM) system is proposed. Comparing to other models, the proposal improves not only the range of detection angle but also the accuracy of detecting in shady conditions. The results show that the accuracy of proposal model is up to 95.1% with 1000 testing images in various scenarios.</span>

scholarly journals A LS-SVM based Measurement Points Classification Algorithm for Adjacent Targets in WSNs

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5555
Author(s):  
Xiang Wang ◽  
Zong-Min Zhao ◽  
Tao Wang ◽  
Zhun Zhang ◽  
Qiang Hao ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Sampling Period ◽  
Wireless Sensor ◽  
Support Vector ◽  
Moving Targets ◽  
Svm Algorithm ◽  
Sampling Points ◽  
Multi Target Tracking ◽  
Efficiency And Effectiveness ◽  
Novel Algorithm

In wireless sensor networks (WSNs), the problem of measurement origin uncertainty for observed data has a significant impact on the precision of multi-target tracking. In this paper, a novel algorithm based on least squares support vector machine (LS-SVM) is proposed to classify measurement points for adjacent targets. Extended Kalman filter (EKF) algorithm is firstly adopted to compute the predicted classification line for each sampling period, which will be used to classify sampling points and calculate observed centers of closely moving targets. Then LS-SVM algorithm is utilized to train the classified points and get the best classification line, which will then be the reference classification line for the next sampling period. Finally, the locations of the targets will be precisely estimated by using observed centers based on EKF. A series of simulations validate the feasibility and accuracy of the new algorithm, while the experimental results verify the efficiency and effectiveness of the proposal.

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