Classification of Dive Profiles: A Comparison of Statistical Clustering Techniques and Unsupervised Artifical Neural Networks

1998 ◽  
Vol 3 (4) ◽  
pp. 383 ◽  
Author(s):  
Jason F. Schreer ◽  
R. J. O'Hara Hines ◽  
Kit M. Kovacs
Keyword(s):  
Neural Networks ◽  
Clustering Techniques ◽  
Artifical Neural Networks ◽  
Statistical Clustering

Comparison of statistical clustering techniques for the classification of modelled atmospheric trajectories

2009 ◽  
Vol 102 (1-2) ◽  
pp. 1-12 ◽  
Author(s):  
P. Kassomenos ◽  
S. Vardoulakis ◽  
R. Borge ◽  
J. Lumbreras ◽  
C. Papaloukas ◽  
...  
Keyword(s):  
Clustering Techniques ◽  
Statistical Clustering

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

CLASSIFICATION OF SOUNDS OF ASTHMATIC BREATHING USING NEURAL NETWORKS

2018 ◽  
Author(s):  
G. К. Berdibaeva ◽  
◽  
O. N. Bodin ◽  
D. S. Firsov ◽  
◽  
...  
Keyword(s):  
Neural Networks

Classification of ground-truth fire debris samples using artificial neural networks

2021 ◽  
Vol 23 ◽  
pp. 100313
Author(s):  
Nicholas A. Thurn ◽  
Taylor Wood ◽  
Mary R. Williams ◽  
Michael E. Sigman
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Ground Truth ◽  
Fire Debris ◽  
Artificial Neural
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