Application of Neural Networks for the Classification of Gender from Kick Force Profile – A Small Scale Study

2014 ◽  
pp. 429-438 ◽  
Author(s):  
Dora Lapkova ◽  
Michal Pluhacek ◽  
Zuzana Komínková Oplatková ◽  
Roman Senkerik ◽  
Milan Adamek
Keyword(s):  
Neural Networks ◽  
Small Scale ◽  
Force Profile

scholarly journals Detection and classification of small-scale objects in images obtained by synthetic-aperture radar stations

2018 ◽  
pp. 93-99
Author(s):  
E. A. Kazachkov ◽  
S. N. Matyugin ◽  
I. V. Popov ◽  
V. V. Sharonov
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Considerable Increase ◽  
Simultaneous Detection ◽  
Small Scale ◽  
Learning Approaches ◽  
High Resolution Data ◽  
Radar Images ◽  
Detection And Recognition

The investigation deals with the problem of simultaneous detection and classification (that is, recognition) of several classes of objects in radar images by means of convolutional neural networks. We present a two-stage processing algorithm that detects and recognises objects. It also features an intermediate sub-stage that increases the resolution of those zones where objects have been detected. We show that a considerable increase in detection and recognition probabilities is possible if the recognition module is trained using high-resolution data. We implemented the detection and recognition stages using deep learning approaches for convolutional neural networks.

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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