Title
Deep Convolutional Neural Networks with Integrated Quadratic Correlation Filters for Automatic Target Recognition
Abstract
Automatic target recognition involves detecting and recognizing potential targets automatically, which is widely used in civilian and military applications today. Quadratic correlation filters were introduced as two-class recognition classifiers for quickly detecting targets in cluttered scene environments. In this paper, we introduce two methods that integrate the discrimination capability of quadratic correlation filters with the multi-class recognition ability of multilayer neural networks. For mid-wave infrared imagery, the proposed methods are demonstrated to be multi-class target recognition classifiers with very high accuracy.
Year
DOI
Venue
2018
10.1109/CVPRW.2018.00168
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Keywords
Field
DocType
two-class recognition classifiers,multilayer neural networks,multiclass target recognition classifiers,convolutional neural networks,automatic target recognition,military applications,civilian applications,mid-wave infrared imagery,quadratic correlation filters,target detection,cluttered scene environment
Computer vision,Object detection,Pattern recognition,Automatic target recognition,Convolutional neural network,Computer science,Infrared imagery,Quadratic equation,Correlation,Artificial intelligence,Artificial neural network
Conference
ISSN
ISBN
Citations 
2160-7508
978-1-5386-6101-7
0
PageRank 
References 
Authors
0.34
9
3
Name
Order
Citations
PageRank
Brian Millikan120.77
Hassan Foroosh225114.66
Qiyu Sun321.39