Research on Classification of Watermelon Ripeness Based on Neural Network Pattern Recognition

2021 ◽  
Author(s):  
Yuchan Chen
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Network Pattern

Classification of catchments for nitrogen using Artificial Neural Network Pattern Recognition and spatial data

2021 ◽  
pp. 151139
Author(s):  
Cherie M. O'Sullivan ◽  
Afshin Ghahramani ◽  
Ravinesh C. Deo ◽  
Keith Pembleton ◽  
Urooj Khan ◽  
...  
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Spatial Data ◽  
Artificial Neural ◽  
Network Pattern

Detection of odorants using an array of piezoelectric crystals and neural-network pattern recognition

1991 ◽  
Vol 249 (2) ◽  
pp. 323-329 ◽  
Author(s):  
S.-M. Chang ◽  
Y. Iwasaki ◽  
M. Suzuki ◽  
E. Tamiya ◽  
I. Karube ◽  
...  
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Piezoelectric Crystals ◽  
Network Pattern

scholarly journals Pattern Recognition by Hierarchical Feature Extraction

2003 ◽  
Vol 15 (3) ◽  
pp. 278-285
Author(s):  
Daigo Misaki ◽  
◽  
Shigeru Aomura ◽  
Noriyuki Aoyama
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Feature Extraction ◽  
Input Data ◽  
Local Features ◽  
Detailed Classification

We discuss effective pattern recognition for contour images by hierarchical feature extraction. When pattern recognition is done for an unlimited object, it is effective to see the object in a perspective manner at the beginning and next to see in detail. General features are used for rough classification and local features are used for a more detailed classification. D-P matching is applied for classification of a typical contour image of individual class, which contains selected points called ""landmark""s, and rough classification is done. Features between these landmarks are analyzed and used as input data of neural networks for more detailed classification. We apply this to an illustrated referenced book of insects in which much information is classified hierarchically to verify the proposed method. By introducing landmarks, a neural network can be used effectively for pattern recognition of contour images.

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