A Hierarchical Target Recognition Method Based on Image Processing

2017 ◽  
pp. 91-107
Author(s):  
QingE Wu ◽  
Weidong Yang
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Target Recognition ◽  
Wavelet Packet ◽  
Recognition Rate ◽  
Image Decomposition ◽  
Extraction Process ◽  
Practical Significance ◽  
Recognition Method ◽  
Target Feature

In order to provide an accurate and rapid target recognition method for some military affairs, public security, finance and other departments, this paper studies firstly a variety of fuzzy signal, analyzes the uncertainties classification and their influence, eliminates fuzziness processing, presents some methods and algorithms for fuzzy signal processing, and compares with other methods on image processing. Moreover, this paper uses the wavelet packet analysis to carry out feature extraction of target for the first time, extracts the coefficient feature and energy feature of wavelet transformation, gives the matching and recognition methods, compares with the existing target recognition methods by experiment, and presents the hierarchical recognition method. In target feature extraction process, the more detailed and rich texture feature of target can be obtained by wavelet packet to image decomposition to compare with the wavelet decomposition. In the process of matching and recognition, the hierarchical recognition method is presented to improve the recognition speed and accuracy. The wavelet packet transformation is used to carry out the image decomposition. Through experiment results, the proposed recognition method has the high precision, fast speed, and its correct recognition rate is improved by an average 6.13% than that of existing recognition methods. These researches development in this paper can provide an important theoretical reference and practical significance to improve the real-time and accuracy on fuzzy target recognition.

A Hierarchical Target Recognition Method Based on Image Processing

2018 ◽  
pp. 494-510
Author(s):  
QingE Wu ◽  
Weidong Yang
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Target Recognition ◽  
Wavelet Packet ◽  
Recognition Rate ◽  
Image Decomposition ◽  
Extraction Process ◽  
Practical Significance ◽  
Recognition Method ◽  
Target Feature

In order to provide an accurate and rapid target recognition method for some military affairs, public security, finance and other departments, this paper studies firstly a variety of fuzzy signal, analyzes the uncertainties classification and their influence, eliminates fuzziness processing, presents some methods and algorithms for fuzzy signal processing, and compares with other methods on image processing. Moreover, this paper uses the wavelet packet analysis to carry out feature extraction of target for the first time, extracts the coefficient feature and energy feature of wavelet transformation, gives the matching and recognition methods, compares with the existing target recognition methods by experiment, and presents the hierarchical recognition method. In target feature extraction process, the more detailed and rich texture feature of target can be obtained by wavelet packet to image decomposition to compare with the wavelet decomposition. In the process of matching and recognition, the hierarchical recognition method is presented to improve the recognition speed and accuracy. The wavelet packet transformation is used to carry out the image decomposition. Through experiment results, the proposed recognition method has the high precision, fast speed, and its correct recognition rate is improved by an average 6.13% than that of existing recognition methods. These researches development in this paper can provide an important theoretical reference and practical significance to improve the real-time and accuracy on fuzzy target recognition.

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%.

Research on Adaptive Face Gender Recognition Based on Compressive Sensing

2014 ◽  
Vol 989-994 ◽  
pp. 4187-4190 ◽  
Author(s):  
Lin Zhang
Keyword(s):  
Feature Extraction ◽  
Compressive Sensing ◽  
Recognition Accuracy ◽  
Recognition Rate ◽  
Low Frequency ◽  
Face Image ◽  
Gender Recognition ◽  
Recognition Method ◽  
Frequency Information ◽  
Learning Machine

An adaptive gender recognition method is proposed in this paper. At first, do multiwavlet transform to face image and get its low frequency information, then do feature extraction to the low frequency information using compressive sensing (CS), use extreme learning machine (ELM) to achieve gender recognition finally. In the process of feature extraction, we use genetic algorithm (GA) to get the number of measurements of CS in order to gain the highest recognition rate, so the method can adaptive access optimal performance. Experimental results show that compared with PDA and LDA, the new method improved the recognition accuracy substantially.

The Recognition of Handwritten Digital Based on BP Neural Network

2013 ◽  
Vol 416-417 ◽  
pp. 1239-1243
Author(s):  
Shan Gao
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Recognition Rate ◽  
Practical Significance ◽  
Test Results ◽  
Recognition Method ◽  
Number Recognition ◽  
Recognition Ability ◽  
Fuzzy Recognition ◽  
Zip Code

The article put forward to new recognition method of handwritten digital based on BP neural network. Its recognition process mainly includes ten aspect: incline correction of handwritten number, edge detection and separation of a set number, binarization, denoising, extraction of numerals, window scaling, location standardization, thinning, extraction of numeral feature and fuzzy recognition based on BP neural network. The test results show that the recognition rate of this method can be over 92 percent. The recognition time of characters for character is less than 1.1 second, which means that the method is more effective recognition ability and can better satisfy the real system requirements.It should be widely applied practical significance for Book Number Recognition, zip code recognition sorting.

scholarly journals Badminton motion capture with visual image detection of picking robotics

2020 ◽  
Vol 17 (6) ◽  
pp. 172988142096907
Author(s):  
Changxin Li
Keyword(s):  
Image Processing ◽  
Control System ◽  
Motion Capture ◽  
High Precision ◽  
Target Recognition ◽  
Vision System ◽  
Recognition Rate ◽  
Motion Capture System ◽  
Vision Control ◽  
Edge Features

In the process of strawberry easily broken fruit picking, in order to reduce the damage rate of the fruit, improves accuracy and efficiency of picking robot, field put forward a motion capture system based on international standard badminton edge feature detection and capture automation algorithm process of night picking robot badminton motion capture techniques training methods. The badminton motion capture system can analyze the game video in real time and obtain the accuracy rate of excellent badminton players and the technical characteristics of badminton motion capture through motion capture. The purpose of this article is to apply the high-precision motion capture vision control system to the design of the vision control system of the robot in the night picking process, so as to effectively improve the observation and recognition accuracy of the robot in the night picking process, so as to improve the degree of automation of the operation. This paper tests the reliability of the picking robot vision system. Taking the environment of picking at night as an example, image processing was performed on the edge features of the fruits picked by the picking robot. The results show that smooth and enhanced image processing can successfully extract edge features of fruit images. The accuracy of the target recognition rate and the positioning ability of the vision system of the picking robot were tested by the edge feature test. The results showed that the accuracy of the target recognition rate and the positioning ability of the motion edge of the vision system were far higher than 91%, satisfying the automation demand of the picking robot operation with high precision.

scholarly journals Active Learning for Recognition of Shipwreck Target in Side-Scan Sonar Image

2019 ◽  
Vol 11 (3) ◽  
pp. 243 ◽  
Author(s):  
Bangyan Zhu ◽  
Xiao Wang ◽  
Zhengwei Chu ◽  
Yi Yang ◽  
Juan Shi
Keyword(s):  
Feature Extraction ◽  
Target Recognition ◽  
Recognition Rate ◽  
Correct Recognition ◽  
Matching Model ◽  
Side Scan Sonar ◽  
Recognition Model ◽  
Adaboost Algorithm ◽  
Recognition Process ◽  
Correct Recognition Rate

In order to realize the automatic and accurate recognition of shipwreck targets in side-scan sonar (SSS) waterfall images, a pipeline that contains feature extraction, selection, and shipwreck recognition, an AdaBoost model was constructed by sample images. Shipwreck targets are detected quickly by a nonlinear matching model, and a shipwreck recognition in SSS waterfall images are given, and according to a wide set of combinations of different types of these individual procedures, the model is able to recognize the shipwrecks accurately. Firstly, two feature-extraction methods suitable for recognizing SSS shipwreck targets from natural sea bottom images were studied. In addition to these two typical features, some commonly used features were extracted and combined as comprehensive features to characterize shipwrecks from various feature spaces. Based on Independent Component Analysis (ICA), the preferred features were selected from the comprehensive features, which avoid dimension disaster and improved the correct recognition rate. Then, the Gentle AdaBoost algorithm was studied and used for constructing the shipwreck target recognition model using sample images. Finally, a shipwreck target recognition process for the SSS waterfall image was given, and the process contains shipwreck target fast detection by a nonlinear matching model and accurate recognition by the Gentle AdaBoost recognition model. The results show that the correct recognition rate of the model for the sample image is 97.44%, while the false positive rate is 3.13% and the missing detection rate is 0. This study of a measured SSS waterfall image confirms the correctness of the recognition process and model.

scholarly journals A hierarchical target recognition method based on image processing

2014 ◽  
Vol 3 (3) ◽  
Author(s):  
Anlai Sun ◽  
Wei Hu ◽  
Ying Xiong ◽  
Jian Li ◽  
QingE Wu
Keyword(s):  
Image Processing ◽  
Target Recognition ◽  
Recognition Method

Feature Extraction

2017 ◽  
pp. 48-105
Author(s):  
Anindita Das Bhattacharjee
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Pattern Recognition ◽  
Feature Extraction ◽  
Extraction Process ◽  
Measured Data ◽  
Data Set ◽  
Content Domain ◽  
Accessibility Problem ◽  
The Common

Accessibility problem is relevant for audiovisual information, where enormous data has to be explored and processed. Most of the solutions for this specific type of problems point towards a regular need of extracting applicable information features for a given content domain. And feature extraction process deals with two complicated tasks first deciding and then extracting. There are certain properties expected from good features-Repeatability, Distinctiveness, Locality, Quantity, Accuracy, Efficiency, and Invariance. Different feature extraction techniques are described. The chapter concentrates of taking a survey on the topic of Feature extraction and Image formation. Here both image and video are considered to have their feature extracted. In machine learning, pattern recognition and in image processing has significant contribution. The feature extraction is one of the common mechanisms involved in these two techniques. Extracting feature initiates from an initial data set of measured data and constructs derived informative values which are non redundant in nature.

The Recognition of Smart Car Track and Starting Line

2011 ◽  
Vol 308-310 ◽  
pp. 833-836
Author(s):  
Wen Dai ◽  
Ming Ge Cao ◽  
Xu Ying Ren
Keyword(s):  
Image Processing ◽  
Control System ◽  
Recognition Rate ◽  
Processing Method ◽  
Experimental Results ◽  
Recognition Method ◽  
Image Processing Method ◽  
Main Track ◽  
The Given ◽  
Motor Controls

In this paper, an intelligent car control system based on MCU is proposed. A camera mounted on the car is used to capture path images, in which path is recognized by image processing method. Based on the extracted path information, the car motor controls are applied to keep car running in the given path. The recognition rate and precision of main track and starting line will directly affect the precision of control system. In this article, a recognition method is proposed, and experimental results show that our car can follow the main track and stopped on the starting line.

Motor Imagery EEG Recognition Based on WPD-CSP and KF-SVM in Brain Computer Interfaces

2014 ◽  
Vol 556-562 ◽  
pp. 2829-2833 ◽  
Author(s):  
Bang Hua Yang ◽  
Ting Wu ◽  
Qian Wang ◽  
Zhi Jun Han
Keyword(s):  
Feature Extraction ◽  
Motor Imagery ◽  
Kernel Function ◽  
Feature Vector ◽  
Wavelet Packet ◽  
Support Vector ◽  
Wavelet Packet Decomposition ◽  
Brain Computer Interfaces ◽  
Recognition Method ◽  
Computer Interfaces

A recognition method based on Wavelet Packet Decomposition - Common Spatial Patterns (WPD-CSP) and Kernel Fisher Support Vector Machine (KF-SVM) is developed and used for EEG recognition in motor imagery brain–computer interfaces (BCIs). The WPD-CSP is used for feature extraction and KF-SVM is used for classification. The presented recognition method includes the following steps: (1) some important EEG channels are selected. The 'haar' wavelet basis is used to take wavelet packet decomposition. And some decomposed sub-bands related with motor imagery for each EEG channel are reconstructed to obtain the relevant frequency information. (2) A six-dimensional feature vector is obtained by the CSP feature extraction to the reconstructed signal. And then the within-class scatter is calculated based on the feature vector. (3) The scatter is added into the radical basis function to construct a new kernel function. The obtained new kernel is integrated into the SVM to act as its kernel function. To evaluate effectiveness of the proposed WPD-CSP + KF-SVM method, the data from the 2008 international BCI competition are processed. A preliminary result shows that the proposed classification algorithm can well recognize EEG data and improve the EEG recognition accuracy in motor imagery BCIs.

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