Statistical edge detection of concealed weapons using artificial neural networks

2008 ◽  
Author(s):  
Ian Williams ◽  
David Svoboda ◽  
Nicholas Bowring ◽  
Elizabeth Guest
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Edge Detection ◽  
Concealed Weapons ◽  
Artificial Neural

scholarly journals Experimental applications of artificial neural networks in Engineering Processing System

2014 ◽  
Vol 8 (2) ◽  
pp. 28-33
Author(s):  
Samad Dadvandipour
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Artificial Neural Networks ◽  
Edge Detection ◽  
Moving Objects ◽  
Processing System ◽  
Conveyor Belt ◽  
Proposed Model ◽  
Artificial Neural ◽  
Scientific Environment

Artificial Neural Networks along with Image Processing Systems have proven to be successful, particularly in the domains of mathematics, science and technology. They have gained quite notable advantages beyond classical learning, as their usable engagement are observable in many fields of scientific environment related to the relevant systems. This paper presents a model for identifying the small components parts. The model may be significant in various industries mainly in engineering processing system areas. The objective of the study is to apply Artificial Neural Networks (ANN) in Image Processing System (IPS) with feed forward structure to detect, and recognize different parts or any other environment products on a moving conveyor bel. In the proposed model, we have used appropriate method of edge detection. The edge detection realizes artificial neural network with noise. The paper emphasizes the implementation of the model considering functionality, parts images, accurate detection and identifying the different components. The result shows that the model can detect moving objects (products of many kinds) accurately on the conveyor belt with very high success rate and sort them accordingly for further processes.

Identification of pulpitis at dental X-ray periapical radiography based on edge detection, texture description and artificial neural networks

2014 ◽  
Vol 4 (3) ◽  
pp. 115 ◽  
Author(s):  
BernardY Tumbelaka ◽  
FaisalNur Baihaki ◽  
Fahmi Oscandar ◽  
Mandojo Rukmo ◽  
Suhardjo Sitam
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Edge Detection ◽  
X Ray ◽  
Artificial Neural ◽  
Periapical Radiography

scholarly journals Edge Detection through Artificial Neural Networks

2014 ◽  
Vol 4 (2) ◽  
pp. 22-25
Author(s):  
P. S. K. Rohit Varma ◽  
◽  
Prathik S.M ◽  
R. Rohit
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Edge Detection ◽  
Artificial Neural

scholarly journals Color Extraction and Edge Detection of Nutrient Deficiencies in Cucumber Leaves Using Artificial Neural Networks

2020 ◽  
Vol 14 (1) ◽  
pp. 23
Author(s):  
Arie Qur'ania ◽  
Prihastuti Harsani ◽  
Triastinurmiatiningsih Triastinurmiatiningsih ◽  
Lili Ayu Wulandhari ◽  
Alexander Agung Santoso Gunawan
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Edge Detection ◽  
Leaf Shape ◽  
Training Data ◽  
Nutrient Deficiencies ◽  
Testing Data ◽  
Artificial Neural ◽  
Sobel Edge Detection ◽  
Phosphorus And Potassium

The research aims to detect the combined deficiency of two nutrients. Those are nitrogen (N) and phosphorus (P), and phosphorus and potassium (K). Here, it is referred to as nutrient deficiencies of N and Pand P and K. The r esearchers use the characteristics of Red, Green, Blue (RGB) color and Sobel edge detection for leaf shape detection and Artificial Neural Networks (ANN) for the identification process to make the application of nutrient differentiation identification in cucumber. The data of plant images consist of 450 training data and 150 testing data. The results of identifying nutrient deficiencies in plants using backpropagation neural networks are carried out in three tests. First, using RGB color extraction and Sobel edge detection, the researchers show 65.36% accuracy. Second, using RGB color extraction, it has 70.25% accuracy. Last, with Sobel edge detection, it has 59.52% accuracy.

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