Multiresolution neural networks for mine detection in side scan sonar images

1998 ◽  
Author(s):  
Weiming Guo ◽  
William G. Szymczak
Keyword(s):  
Neural Networks ◽  
Mine Detection ◽  
Side Scan Sonar ◽  
Sonar Images

scholarly journals Optimizing the Sediment Classification of Small Side-Scan Sonar Images Based on Deep Learning

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 29416-29428
Author(s):  
Xiaoming Qin ◽  
Xiaowen Luo ◽  
Ziyin Wu ◽  
Jihong Shang
Keyword(s):  
Deep Learning ◽  
Side Scan Sonar ◽  
Sediment Classification ◽  
Sonar Images ◽  
Small Side

Enhancing mine signatures in sonar images using nested neural networks

1999 ◽  
Author(s):  
Jeffrey P. Sutton ◽  
David D. Sha ◽  
Stuart W. Perry ◽  
Ling Guan
Keyword(s):  
Neural Networks ◽  
Sonar Images

Deblurring of GLORIA side-scan sonar images

1992 ◽  
Vol 14 (2) ◽  
pp. 125-136 ◽  
Author(s):  
D. C. Mason ◽  
T. P. LeBas ◽  
I. Sewell ◽  
C. Angelikaki
Keyword(s):  
Side Scan Sonar ◽  
Sonar Images

A Probabilistic Neural Network-Based Module for Recognition of Objects from their 3-D Images

2013 ◽  
Vol 2 (2) ◽  
pp. 66-79 ◽  
Author(s):  
Onsy A. Abdel Alim ◽  
Amin Shoukry ◽  
Neamat A. Elboughdadly ◽  
Gehan Abouelseoud
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Decision Making ◽  
Pattern Recognition ◽  
Object Recognition ◽  
Probabilistic Neural Network ◽  
Training Procedure ◽  
Generalization Capability ◽  
Mine Detection ◽  
Recognition Of Objects

In this paper, a pattern recognition module that makes use of 3-D images of objects is presented. The proposed module takes advantage of both the generalization capability of neural networks and the possibility of manipulating 3-D images to generate views at different poses of the object that is to be recognized. This allows the construction of a robust 3-D object recognition module that can find use in various applications including military, biomedical and mine detection applications. The paper proposes an efficient training procedure and decision making strategy for the suggested neural network. Sample results of testing the module on 3-D images of several objects are also included along with an insightful discussion of the implications of the results.

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