Infrared image target recognition based on multiple matching methods

2021 ◽  
Author(s):  
Dan Chen ◽  
Weihua Han ◽  
Yuquan Li ◽  
Chunyan Fan
Keyword(s):  
Target Recognition ◽  
Infrared Image ◽  
Matching Methods

The Preprocessing for Infrared Sea-Surface Target Image

2012 ◽  
Vol 433-440 ◽  
pp. 4512-4515
Author(s):  
Shu Li Lou ◽  
Jian Cun Ren ◽  
Yan Li Han ◽  
Xiao Hu Yuan ◽  
Xiao Dong Zhou
Keyword(s):  
Feature Extraction ◽  
Target Recognition ◽  
Infrared Image ◽  
Automatic Target Recognition ◽  
Experimental Results ◽  
Sea Surface ◽  
Practical Application ◽  
Target Image

The preprocessing for infrared sea-surface target image is very important to automatic target recognition and tracking. The preprocessing can reduce noise and enhance target, and it is the base of feature extraction and target recognition. The scene model of infrared sea-surface target image was established. The characteristics of infrared image are analyzed, and several methods of preprocessing nowadays were analyzed and compared. According to the different characteristic of infrared image, a preprocessing scheme is proposed. The experimental results indicate that in practical application appropriate methods should be chosen for different purpose. In order to get good preprocessing effects, these methods can be assembled into multi- process.

Target recognition in infrared image using a new neural network model

1995 ◽  
Author(s):  
Yun Hu ◽  
Guan Hua ◽  
Zhenkang Shen ◽  
Zhongkang Sun
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Target Recognition ◽  
Infrared Image

Moving Target Recognition and Tracking Techniques Based on Infrared Image

2014 ◽  
Vol 608-609 ◽  
pp. 473-477 ◽  
Author(s):  
Ze Gang Yang ◽  
Xiang Jun Liu ◽  
Qi Wen Zhang
Keyword(s):  
Coordinate System ◽  
Target Recognition ◽  
Tracking System ◽  
Median Filter ◽  
Infrared Image ◽  
Combination Method ◽  
Polar Coordinate System ◽  
Moving Target ◽  
Polar Coordinate ◽  
Search Speed

Infrared equipment has good concealment, strong anti-interference ability, far operating range and fast search speed. These features make it more and more used in military or civilian fields. This paper aims to realize the moving target recognition and tracking system based on Infrared scanning image. According to the actual situation, choose a combination method of Butterworth filter and Median filter as the image preprocessing algorithm, selected the segmentation algorithm based on the target local energy for continuous multi frames, and using Kalman tracking algorithm with our orientation algorithm to track the target, get the target specific position. Using GDI in VC++ to establish Radar Scanning polar coordinate system, show the target trajectory in the coordinate system.

scholarly journals Complexity Estimation of Infrared Image Sequence for Automatic Target Track

2019 ◽  
Vol 37 (4) ◽  
pp. 664-672
Author(s):  
Xiaotian Wang ◽  
Wanchao Ma ◽  
Kai Zhang ◽  
Shaoyi Li ◽  
Jie Yan
Keyword(s):  
Target Recognition ◽  
Infrared Image ◽  
Feature Space ◽  
Image Sequence ◽  
Low Complexity ◽  
Automatic Target Recognition ◽  
High Complexity ◽  
Image Complexity ◽  
Single Frame ◽  
Target Track

Infrared image complexity metrics are an important task of automatic target recognition and track performance assessment. Traditional metrics, such as statistical variance and signal-to-noise ratio, targeted to single frame infrared image. However, there are some studies on the complexity of infrared image sequences. For this problem, a method to measure the complexity of infrared image sequence for automatic target recognition and track is proposed. Firstly, based on the analysis of the factors affecting the target recognition and track, the specific reasons which background influences target recognition and track are clarified, and the method introduces the feature space into confusion degree of target and occultation degree of target respectively. Secondly, the feature selection is carried out by using the grey relational method, and the feature space is optimized, so that confusion degree of target and occultation degree of target are more reasonable, and statistical formula F1-Score is used to establish the relationship between the complexity of single-frame image and the two indexes. Finally, the complexity of image sequence is not a linear sum of the single-frame image complexity. Target recognition errors often occur in high-complexity images and the target of low-complexity images can be correctly recognized. So the neural network Sigmoid function is used to intensify the high-complexity weights and weaken the low-complexity weights for constructing the complexity of image sequence. The experimental results show that the present metric is more valid than the other, such as sequence correlation and inter-frame change degree, has a strong correlation with the automatic target track algorithm, and which is an effective complexity evaluation metric for image sequence.

Infrared Target Recognition Based on Multi-Features Fusion

2012 ◽  
Vol 198-199 ◽  
pp. 249-255
Author(s):  
Wei Wu
Keyword(s):  
Image Recognition ◽  
Infrared Imaging ◽  
Target Recognition ◽  
Infrared Image ◽  
Recognition System ◽  
Guidance System ◽  
Features Fusion ◽  
Dempster Shafer Theory ◽  
Calculation Results ◽  
Shafer Theory

Considering the uncertainty of calculation results by using single feature as measurement of target recognition and identification, this paper discussed the multi-features fusion technology in infrared image recognition classification. The invariant of the singular value and invariant moment feature of infrared target image were used to make fusion. According to Dempster-Shafer Theory, the basic probability assignment was calculated first, and the fusion data was used to make specification decision based on the corresponding rules in the decision-making level. The test result shows that the multi-features fusion method has a better stability, accuracy and reliability in target recognition applications. It can raise the accuracy and fault tolerance ability of infrared image recognition system. So it will have great application value to raise the guidance accuracy of infrared imaging terminal guidance system.

Sign in / Sign up

Export Citation Format

Share Document