A 3D Steel Coils’ Recognition Method Based on Multi-Scale Features and Pointnet

2020 ◽  
Author(s):  
Zixuan Liu ◽  
Dan Niu ◽  
Qi Li ◽  
Xisong Chen ◽  
Li Ding ◽  
...  
Keyword(s):  
Recognition Method ◽  
Multi Scale ◽  
Steel Coils

Iris Recognition Method Based on Feature Discrimination and Category Correlation

2014 ◽  
Vol 602-605 ◽  
pp. 1610-1613
Author(s):  
Ming Hai Yao ◽  
Na Wang ◽  
Jin Song Li
Keyword(s):  
Iris Recognition ◽  
Predictive Accuracy ◽  
Gabor Filter ◽  
Recognition Algorithm ◽  
Recognition Method ◽  
Texture Characteristic ◽  
Multi Scale ◽  
Laplacian Score ◽  
Circular Gabor Filter ◽  
Academy Of Sciences

With the increasing number of internet user, the authentication technology is more and more important. Iris recognition as an important method for identification, which has been attention by researchers. In order to improve the predictive accuracy of iris recognition algorithm, the iris recognition method is proposed based feature discrimination and category correlation. The feature discrimination and category correlation are calculated by laplacian score and mutual information. The formula about feature discrimination and category correlation are built. Aiming at texture characteristic of iris image, the multi-scale circular Gabor filter is used to feature extraction. The computational efficiency of algorithm is improved. In order to verify the validity of the algorithm, the CASIA iris database of Chinese Academy of Sciences is used to do the experiment. The experimental results show that our method has high predictive accuracy.

scholarly journals FMnet: Iris Segmentation and Recognition by Using Fully and Multi-Scale CNN for Biometric Security

2019 ◽  
Vol 9 (10) ◽  
pp. 2042 ◽  
Author(s):  
Rachida Tobji ◽  
Wu Di ◽  
Naeem Ayoub
Keyword(s):  
Neural Networks ◽  
Iris Recognition ◽  
State Of The Art ◽  
Features Extraction ◽  
Iris Segmentation ◽  
Recognition Method ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Multi Scale ◽  
Biometric Security

In Deep Learning, recent works show that neural networks have a high potential in the field of biometric security. The advantage of using this type of architecture, in addition to being robust, is that the network learns the characteristic vectors by creating intelligent filters in an automatic way, grace to the layers of convolution. In this paper, we propose an algorithm “FMnet” for iris recognition by using Fully Convolutional Network (FCN) and Multi-scale Convolutional Neural Network (MCNN). By taking into considerations the property of Convolutional Neural Networks to learn and work at different resolutions, our proposed iris recognition method overcomes the existing issues in the classical methods which only use handcrafted features extraction, by performing features extraction and classification together. Our proposed algorithm shows better classification results as compared to the other state-of-the-art iris recognition approaches.

scholarly journals Spacecraft Anomaly Recognition Based on Morphological Variational Mode Decomposition and JRD

2018 ◽  
Vol 36 (1) ◽  
pp. 20-27
Author(s):  
Haixu Jiang ◽  
Ke Zhang ◽  
Jingyu Wang ◽  
Meibo Lü
Keyword(s):  
Measured Data ◽  
Variational Mode Decomposition ◽  
Telemetry Data ◽  
Recognition Method ◽  
State Recognition ◽  
Multi Scale ◽  
Mode Decomposition ◽  
Sample Data ◽  
Orbital Spacecraft ◽  
Weak Anomaly

Considering the difficulty in identifying the in-orbital spacecraft weak anomaly, a spacecraft anomaly state recognition method based on Morphological variational mode decomposition and JRD distance is proposed. First of all, the telemetry data of the spacecraft is decomposed into multi-scale modal functions with different frequencies via morphological variational modal decomposition. Then the Rényi entropy of each modal function is extracted, which is regarded as the feature of telemetry data. Finally, the recognition of spacecraft anomaly state is realized by comparing the JRD distance between the sample data and the measured data. The proposed method is verified by means of the telemetry data of the weak anomaly speed of a satellite reaction wheel. The simulation results demonstrate that the proposed method can effectively identify the anomaly of the spacecraft and has obvious advantage in recognition speed.

MULTI-SCALE PERIODICITY RECOGNITION METHOD OF WIND SPEED BASED ON MORLET WAVELET WITH TIME-FREQUENCY FACTORS

2021 ◽  
Author(s):  
Ziyi Guo ◽  
Shuang Han ◽  
Han Wang ◽  
Yongqian Liu ◽  
Jie Yan ◽  
...  
Keyword(s):  
Wind Speed ◽  
Morlet Wavelet ◽  
Recognition Method ◽  
Time Frequency ◽  
Multi Scale

A recognition method for time-frequency overlapped waveform-agile radar signals based on matrix transformation and multi-scale center point detection

2021 ◽  
Vol 175 ◽  
pp. 107855
Author(s):  
Zhipeng Liu ◽  
Liandong Wang ◽  
Yuntian Feng ◽  
Zhisheng Qian ◽  
Xiong Xu ◽  
...  
Keyword(s):  
Matrix Transformation ◽  
Recognition Method ◽  
Time Frequency ◽  
Multi Scale ◽  
Center Point ◽  
Radar Signals ◽  
Point Detection

A Multi-Scale Image Matching Recognition Algorithm Based on Phase Correlation

2011 ◽  
Vol 301-303 ◽  
pp. 1438-1443
Author(s):  
Yong Gang Tian ◽  
Min Gang Wang ◽  
Ying Ping Fan
Keyword(s):  
Image Matching ◽  
Recognition Algorithm ◽  
Phase Correlation ◽  
Reference Image ◽  
Recognition Method ◽  
Multi Scale ◽  
Template Image ◽  
The Fourier Transform ◽  
Template Size ◽  
Traditional Image

The image matching recognition method of phase correlation is based on the shift characteristics of the Fourier transform. The traditional image matching recognition algorithm has significant influence of the template size upon its matching accuracy and has weak resilience to noises except for the gauss noise. Addressing these shortcomings, we proposed a multi-scale matching recognition method based on phase correlation, combined with wavelet transform and edge detection. The algorithm, processed the reference image and the template image in different scales with such steps: decomposition, denoising, reconstruction, edge detection and the Fourier transform, phase correlation. Hence, it overcome the dependence upon template size effectively and improve the reliability and the resilience of various noises. Finally, we verified the algorithm with a real ground shooting image as the reference image and an intercepted part as the template image. The results have shown that the proposed approach is better than the traditional image matching method.

Paper Currency Image Features Extraction and Recognition Based on Gabor Filter

2014 ◽  
Vol 635-637 ◽  
pp. 1030-1034 ◽  
Author(s):  
Xi Wen Liu ◽  
Chao Ying Liu
Keyword(s):  
Gabor Filter ◽  
Recognition Rate ◽  
Image Features ◽  
Recognition Method ◽  
Match Method ◽  
Template Match ◽  
Multi Scale ◽  
Paper Currency ◽  
Texture Characteristics ◽  
Directional Texture

The paper currency image recognition method based on Gabor filter set is discussed in this paper. According to the paper currency image features, the suitable parameters of Gabor filter set are selected for the extraction of paper currency characteristics, the multi-scale and multi-directional texture characteristics of paper currency image are gotten; then the texture images are meshed, and the row and column projection sum of grid pixels' average grey are calculated, finally, the template match method based on grid projection characteristics is used for paper currency recognition. Experiments show that, this method has strong anti-interference ability, it can raise the recognition rate of old or dirty paper currency greatly, and it costs little time.

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