Recursive Neural Networks for the Classification of Vehicles in Image Sequences

2009 ◽  
pp. 359-366
Author(s):  
Gabriele Monfardini
Keyword(s):  
Neural Networks ◽  
Image Sequences ◽  
Recursive Neural Networks

scholarly journals P622Automatic classification of CMR image sequences with convolutional neural networks

2019 ◽  
Vol 20 (Supplement_2) ◽  
Author(s):  
N Aung ◽  
A M Lee ◽  
M M Sanghvi ◽  
K Fung ◽  
J M Paiva ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Image Sequences

Time Delay Neural Networks for the Classification of Flow Regimes

1997 ◽  
Vol 21 (1-2) ◽  
pp. S367-S371
Author(s):  
Y Yamashita
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Flow Regimes

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.

Deep Learning through Convolutional Neural Networks for Classification of Image A Novel Approach Using Hyper Filter

2019 ◽  
Vol 7 (6) ◽  
pp. 164-168
Author(s):  
Kshitij Tripathi ◽  
Rajendra G. Vyas ◽  
Anil K. Gupta
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Novel Approach

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
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