scholarly journals COIFLET-Based Fuzzy-Classifier for Defect Detection in Industrial LNG/LPG Tanks

2014 ◽  
Author(s):  
Uvais Qidwai ◽  
Mohamed Shakir
Keyword(s):  
Defect Detection ◽  
Fuzzy Classifier

Study on Defect Detection Technology for Bridge Deck Pavements

2017 ◽  
Vol 19 (6) ◽  
pp. 38
Author(s):  
Chengchao Guo ◽  
Pengfei Xu ◽  
Can Cui
Keyword(s):  
Defect Detection ◽  
Bridge Deck ◽  
Detection Technology

Model Based Defect Detection in Multi-Dimensional Vector Spaces

2011 ◽  
Vol 131 (9) ◽  
pp. 1633-1641
Author(s):  
Toshifumi Honda ◽  
Kenji Obara ◽  
Minoru Harada ◽  
Hajime Igarashi
Keyword(s):  
Defect Detection ◽  
Vector Spaces ◽  
Dimensional Vector ◽  
Model Based

Automated Characterization of Atheromatous Plaque in Intravascular Ultrasound Images Using Neuro Fuzzy Classifier

2012 ◽  
Vol 58 (4) ◽  
pp. 425-431 ◽  
Author(s):  
D. Selvathi ◽  
N. Emimal ◽  
Henry Selvaraj
Keyword(s):  
Intravascular Ultrasound ◽  
Human Error ◽  
Human Life ◽  
Classification Performance ◽  
Atheromatous Plaque ◽  
Ultrasound Images ◽  
Fuzzy Classifier ◽  
Neuro Fuzzy ◽  
Calcified Tissues

Abstract The medical imaging field has grown significantly in recent years and demands high accuracy since it deals with human life. The idea is to reduce human error as much as possible by assisting physicians and radiologists with some automatic techniques. The use of artificial intelligent techniques has shown great potential in this field. Hence, in this paper the neuro fuzzy classifier is applied for the automated characterization of atheromatous plaque to identify the fibrotic, lipidic and calcified tissues in Intravascular Ultrasound images (IVUS) which is designed using sixteen inputs, corresponds to sixteen pixels of instantaneous scanning matrix, one output that tells whether the pixel under consideration is Fibrotic, Lipidic, Calcified or Normal pixel. The classification performance was evaluated in terms of sensitivity, specificity and accuracy and the results confirmed that the proposed system has potential in detecting the respective plaque with the average accuracy of 98.9%.

New Defect Detection Technology for Tissue Industry

2014 ◽  
Vol 68 (3) ◽  
pp. 311-313
Author(s):  
Jason Zyglis ◽  
Wayne Killmer ◽  
Atsushi Kurosaki
Keyword(s):  
Defect Detection ◽  
Detection Technology
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