Linear Target Feature Extraction Applied in Target Identification

2014 ◽  
Vol 602-605 ◽  
pp. 1964-1967
Author(s):  
Man Zhao ◽  
Jin Jiang Cui ◽  
He Nan Wu ◽  
Guang Yang ◽  
Da Yong Jiang
Keyword(s):  
Target Identification ◽  
Target Recognition ◽  
Remote Sensing Image ◽  
Recognition System ◽  
Computer Processing ◽  
Target Feature ◽  
C Language ◽  
Line Extraction ◽  
Target Extraction ◽  
Recognition Efficiency

Linear target is the most widely used in remote sensing image. Effective extraction of the linear target can make us reduce a lot of practical work, thus greatly improve the target extraction and identification of timeliness. According to this situation, in the process of building a recognition system, the recognition efficiency can be realized by joining human recognize and identify, combining with the intelligence of computer processing and powerful place. So in this paper, the method based on edge detection and Hough transform algorithm is exploded. Line Extraction and Target Recognition System is developed. The system is realized under Windows operating system. The tool is Visual C++ 6.0 software. The platform is MFC functions. The system is written in C++ language. The characteristics of the system are the strong pertinence and the simple operation. When the system is applied safely, the results are definite and clear.

Research on Road Target Recognition Method Based on Optical Image

2014 ◽  
Vol 602-605 ◽  
pp. 1968-1971
Author(s):  
Man Zhao ◽  
Jin Jiang Cui ◽  
Fei Guo ◽  
Mei Zhao ◽  
Da Yong Jiang
Keyword(s):  
Target Recognition ◽  
Recognition System ◽  
Optical Image ◽  
Recognition Method ◽  
C Language ◽  
The Road ◽  
Optical Images ◽  
Target Extraction ◽  
Very High ◽  
Detection And Recognition

With the development of science and technology, optical images with very high resolution have been able to provide a large amount of information. Therein the road target is the most widely used in optical image. Road target detection and recognition is extremely important for reducing a lot of practical work and greatly improving the efficiency of the target extraction and identification. Aimed at this problem, we propose a road target recognition method based on optical image.The method is realized by joining human recognize and identify, combining with the intelligence of computer processing and powerful place. So in this work, the method based on edge detection and Hough transform algorithm is exploded. The man-machine interactive recognition system (Road Target Extraction and Recognition System) is developed. The system is realized under Windows operating system. The tool is Visual C++ 6.0 software. The platform is MFC functions. The system is written in C++ language. The characteristics of the system are the strong pertinence and the simple operation. When the system is applied safely, the results are definite and clear.

scholarly journals The Effect of Informing Participants of the Response Bias of an Automated Target Recognition System on Trust and Reliance Behavior

2021 ◽  
pp. 001872082110217
Author(s):  
Shala Knocton ◽  
Aren Hunter ◽  
Warren Connors ◽  
Lori Dithurbide ◽  
Heather F. Neyedli
Keyword(s):  
Response Bias ◽  
Target Recognition ◽  
Rate Sensitivity ◽  
High Sensitivity ◽  
Recognition System ◽  
Decision Criterion ◽  
Detection Task ◽  
Mine Detection ◽  
Rate Condition ◽  
Consistent Response

Objective To determine how changing and informing a user of the false alarm (FA) rate of an automated target recognition (ATR) system affects the user’s trust in and reliance on the system and their performance during an underwater mine detection task. Background ATR systems are designed to operate using a high sensitivity and a liberal decision criterion to reduce the risk of the ATR system missing a target. A high number of FAs in general may lead to a decrease in operator trust and reliance. Methods Participants viewed sonar images and were asked to identify mines in the images. They performed the task without ATR and with ATR at a lower and higher FA rate. The participants were split into two groups—one informed and one uninformed of the changed FA rate. Trust and/or confidence in detecting mines was measured after each block. Results When not informed of the FA rate, the FA rate had a significant effect on the participants’ response bias. Participants had greater trust in the system and a more consistent response bias when informed of the FA rate. Sensitivity and confidence were not influenced by disclosure of the FA rate but were significantly worse for the high FA rate condition compared with performance without the ATR. Conclusion and application Informing a user of the FA rate of automation may positively influence the level of trust in and reliance on the aid.

scholarly journals Underwater Acoustic Target Recognition Based on Depthwise Separable Convolution Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1429
Author(s):  
Gang Hu ◽  
Kejun Wang ◽  
Liangliang Liu
Keyword(s):  
Feature Extraction ◽  
Target Recognition ◽  
Target Feature ◽  
Underwater Acoustic ◽  
Radiated Noise ◽  
Automatic Feature Extraction ◽  
Acoustic Target ◽  
And Cluster Analysis ◽  
Underwater Target ◽  
The One

Facing the complex marine environment, it is extremely challenging to conduct underwater acoustic target feature extraction and recognition using ship-radiated noise. In this paper, firstly, taking the one-dimensional time-domain raw signal of the ship as the input of the model, a new deep neural network model for underwater target recognition is proposed. Depthwise separable convolution and time-dilated convolution are used for passive underwater acoustic target recognition for the first time. The proposed model realizes automatic feature extraction from the raw data of ship radiated noise and temporal attention in the process of underwater target recognition. Secondly, the measured data are used to evaluate the model, and cluster analysis and visualization analysis are performed based on the features extracted from the model. The results show that the features extracted from the model have good characteristics of intra-class aggregation and inter-class separation. Furthermore, the cross-folding model is used to verify that there is no overfitting in the model, which improves the generalization ability of the model. Finally, the model is compared with traditional underwater acoustic target recognition, and its accuracy is significantly improved by 6.8%.

scholarly journals Hierarchical Recognition System for Target Recognition from Sparse Representations

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Zongyong Cui ◽  
Zongjie Cao ◽  
Jianyu Yang ◽  
Hongliang Ren
Keyword(s):  
Deep Learning ◽  
Deep Structure ◽  
Target Recognition ◽  
Recognition System ◽  
Automatic Target Recognition ◽  
Feature Representation ◽  
Small Data ◽  
Learning Method ◽  
Classification Problems ◽  
Restricted Boltzmann Machines

A hierarchical recognition system (HRS) based on constrained Deep Belief Network (DBN) is proposed for SAR Automatic Target Recognition (SAR ATR). As a classical Deep Learning method, DBN has shown great performance on data reconstruction, big data mining, and classification. However, few works have been carried out to solve small data problems (like SAR ATR) by Deep Learning method. In HRS, the deep structure and pattern classifier are combined to solve small data classification problems. After building the DBN with multiple Restricted Boltzmann Machines (RBMs), hierarchical features can be obtained, and then they are fed to classifier directly. To obtain more natural sparse feature representation, the Constrained RBM (CRBM) is proposed with solving a generalized optimization problem. Three RBM variants,L1-RNM,L2-RBM, andL1/2-RBM, are presented and introduced to HRS in this paper. The experiments on MSTAR public dataset show that the performance of the proposed HRS with CRBM outperforms current pattern recognition methods in SAR ATR, like PCA + SVM, LDA + SVM, and NMF + SVM.

Target recognition system of dynamic scene based on artificial intelligence vision

2018 ◽  
Vol 35 (4) ◽  
pp. 4373-4383 ◽  
Author(s):  
Jianzhong Yang ◽  
Xianyang Li ◽  
Yu Jiang ◽  
Guihua Qiu ◽  
S. Buckdahn
Keyword(s):  
Artificial Intelligence ◽  
Target Recognition ◽  
Recognition System ◽  
Dynamic Scene
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