Optical feature extraction using the Radon transform and angular correlation

1993 ◽  
Author(s):  
Oodaye B. Shukla ◽  
Bradley G. Boone
Keyword(s):  
Feature Extraction ◽  
Angular Correlation ◽  
Radon Transform ◽  
Optical Feature

Optical Feature Extraction Via The Radon Transform

1984 ◽  
Vol 23 (5) ◽  
Author(s):  
Gene R. Gindi ◽  
Arthur F. Gmitro
Keyword(s):  
Feature Extraction ◽  
Radon Transform ◽  
Optical Feature

Parallel optical-feature extraction by use of rotationally multiplexed holograms

1996 ◽  
Vol 21 (19) ◽  
pp. 1612 ◽  
Author(s):  
Ju-Seog Jang ◽  
Dong-Hak Shin
Keyword(s):  
Feature Extraction ◽  
Optical Feature

scholarly journals Specific Emitter Identification via Bispectrum-Radon Transform and Hybrid Deep Model

2020 ◽  
Vol 2020 ◽  
pp. 1-17 ◽  
Author(s):  
Yipeng Zhou ◽  
Xing Wang ◽  
You Chen ◽  
Yuanrong Tian
Keyword(s):  
Feature Extraction ◽  
Radon Transform ◽  
Signal Acquisition ◽  
Factors Affecting ◽  
Classifier Selection ◽  
Deep Model ◽  
Combination Methods ◽  
Nonlinear Features ◽  
Specific Emitter Identification ◽  
Deep Feature Extraction

Specific emitter identification is a technique that distinguishes different emitters using radio fingerprints. Feature extraction and classifier selection are critical factors affecting SEI performance. In this paper, we propose an SEI method using the Bispectrum-Radon transform (BRT) and a hybrid deep model. We propose BRT to characterize the unintentional modulation of pulses due to the superiority of bispectrum distributions in characterizing nonlinear features of signals. We then apply a hybrid deep model based on denoising autoencoders and a deep belief network to perform further deep feature extraction and discriminative identification. We design an automatic dependent surveillance-broadcast signal acquisition system to capture signals and to build dataset for validating our proposed SEI method. Theoretical analysis and experimental results show that the BRT feature outperformed traditional features in characterizing UMOP, and our proposed SEI method outperformed other feature and classifier combination methods.

Feature extraction for different distances of visible reflection iris using multiscale sparse representation of local Radon transform

2013 ◽  
Vol 46 (10) ◽  
pp. 2622-2633 ◽  
Author(s):  
Anis Farihan Mat Raffei ◽  
Hishammuddin Asmuni ◽  
Rohayanti Hassan ◽  
Razib M. Othman
Keyword(s):  
Feature Extraction ◽  
Sparse Representation ◽  
Radon Transform
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