Improving Image Processing Systems by Artificial Neural Networks

2004 ◽  
pp. 37-64 ◽  
Author(s):  
R. Rebmann ◽  
B. Michaelis ◽  
G. Krell ◽  
U. Seiffert ◽  
F. Püschel
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Artificial Neural Networks ◽  
Artificial Neural

Detection of Eye Diseases using Image Processing and Artificial Neural Networks

2020 ◽  
pp. 15-20
Author(s):  
K. Sujatha ◽  
V. Srividhya ◽  
V. Karthikeyan ◽  
L. Madheshwaran ◽  
N. P. G. Bhavani
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Artificial Neural Networks ◽  
Eye Diseases ◽  
Artificial Neural

Pig Weight Estimation Using Image Processing and Artificial Neural Networks

2019 ◽  
Vol 6 (4) ◽  
pp. 253-256
Author(s):  
Chanwit Kaewtapee ◽  
◽  
Choawit Rakangtong ◽  
Chaiyapoom Bunchasak
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Artificial Neural Networks ◽  
Weight Estimation ◽  
Artificial Neural

Guest Editorial Applications Of Artificial Neural Networks To Image Processing

1998 ◽  
Vol 7 (8) ◽  
pp. 1093-1096 ◽  
Author(s):  
R. Chellappa ◽  
K. Fukushima ◽  
A.K. Katsaggelos ◽  
Sun-Yuan Kung ◽  
Y. LeCun ◽  
...  
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Artificial Neural Networks ◽  
Guest Editorial ◽  
Artificial Neural

scholarly journals Error Reduction In The Vehicle Wheel Rim Packaging With Image Processing And ANN

2019 ◽  
pp. 80-84
Author(s):  
Gokhan Gokay ◽  
Tulay Yildirim
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Artificial Neural Networks ◽  
Product Quality ◽  
Initial Phase ◽  
Error Reduction ◽  
Wheel Rim ◽  
Artificial Neural ◽  
Product Control

The product quality is provided by the product control companies during the period from the initial phase. Of manufacturing to packaging in many companies. In this study, it is aimed to carry out product control with an image processing based algorithm to prevent model mixes that are loaded on pallets to be packed and cargoed while being controlled by the operators according to the rim models. In the method, a camera located at a height of 5 meters from the ground is used, and the features of the rims in the ground are extracted trained by using artificial neural networks. Attempted to prevent mixing in the backplane.

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