Performance comparison of neural network and statistical pattern recognition approaches to automatic target recognition of ground vehicles using SAR imagery

1997 ◽  
Author(s):  
Kevin M. Olson ◽  
Gary A. Ybarra
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Target Recognition ◽  
Automatic Target Recognition ◽  
Performance Comparison ◽  
Statistical Pattern Recognition ◽  
Ground Vehicles ◽  
Statistical Pattern ◽  
Sar Imagery

Research and Application on Bionic Pattern Recognition

2011 ◽  
Vol 341-342 ◽  
pp. 355-358
Author(s):  
Xiao Jia Wu ◽  
Qi Chuan Tian
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Target Recognition ◽  
Statistical Pattern Recognition ◽  
Human Being ◽  
Main Principle ◽  
New Model ◽  
Statistical Pattern ◽  
Cognitive Pattern ◽  
Selection Operator

Studies on biology basis from human being or other animals have attracted an ever increasing attention in pattern recognition. This paper describe a new model of pattern recognition principles, witch is based on “matter cognition” instead of “matter classification” in traditional statistical pattern recognition. This new model is better closer to the function of human being, rather than traditional statistical pattern recognition using “optimal separating” as its main principle. So the new model of pattern recognition is called the Bionic（or cognitive） Pattern Recognition. In the support of this theory, the pulse coupled neural network (PCNN), an entirely different neural network from traditional artificial ones, is used for image target recognition. Through the contrast of the image, the linking strength of each pixel can be chosen adaptively. After the processing of PCNN with the adaptive linking strength, new fire mapping images are obtained for each image from sensor. The clear objects of each original image are decided by the compare-selection operator with the fire mapping images pixel by pixel and then all of them are merged into a new clear image. As a result, the target which was polluted by noise was recognized correctly.

Automated feature extraction for the classification of humanin vivo13C NMR spectra using statistical pattern recognition and wavelets

1996 ◽  
Vol 35 (6) ◽  
pp. 834-840 ◽  
Author(s):  
A. Rosemary Tate ◽  
Des Watson ◽  
Stephen Eglen ◽  
Theodores N. Arvanitis ◽  
E. Louise Thomas ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Nmr Spectra ◽  
Statistical Pattern Recognition ◽  
Statistical Pattern ◽  
Automated Feature Extraction
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