Fuzzy logic classification in image processing

Fageth, Reiner; Allen, William G.; Jäger, Uwe

doi:10.1016/0165-0114(95)00235-9

Cited by 13 publications

(3 citation statements)

References 6 publications

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“…This method has been used for detecting defects after imaging in the industry [23]. Also, this attitude is a developed intelligence-supportive system for decision-making and classification [24].…”

Section: Fuzzy Operatormentioning

confidence: 99%

Experimental Study on A Reinforced Concrete Element to Extract the Durability Index with the Automated Visualization

Nejad¹,

Lajevardi²,

Ravanshadnia³

2022

Preprint

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Reinforced Concrete (RC) durability is a crucial feature to estimate the long-term quality and structural performance. Since life span estimation is vital for maintenance resource planning, a degradation model of RC component extracts by updating the status of structures and trending the components’ state over time in terms of durability. Surface erosion, spalling, cracks, and other expose defects on the RC component lead to increase factors adversely affecting concrete durability in structures. This research presents an approach based on automated visualization for extracting quantitative indexes beside or instead of visual inspection without subjective interspersion of humans or probable human errors during the inspection. The durability index (D_i) will extract based on damage probability and its growth in order to extract the severity of failure and risk. Measurement operation by automated software has been double-checked by manual measurement tools, and data will verify randomly in this method. The result shows damage growth in this load-bearing component by 24 percentages over the definite time. According to degradation models, it shows this component may pass the relative thresholds as a limit state of operation to fail. This significant difference between expected time and designing time determines the D_i equal to 5 out of 10.

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Section: Fuzzy Operatormentioning

confidence: 99%

Experimental Study on A Reinforced Concrete Element to Extract the Durability Index with the Automated Visualization

Nejad¹,

Lajevardi²,

Ravanshadnia³

2022

Preprint

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“…Actually the human reasoning, for this work, does not present a mathematical model, while there is an ambiguity component, that conforms to the fuzzy inference system. Fuzzy logic has been often used for image classification [2], [14].…”

Section: Introductionmentioning

confidence: 99%

“…Computer Vision allowed to increase production, reduce costs and ensure reliability. Among industrial applications, the vision systems are used for robots positioning and driving [1], code reading, classification [2], quality control and monitoring, including defect detection [3].…”

Section: Introductionmentioning

confidence: 99%

A fuzzy inference system applied to defect detection in flat steel production

Borselli¹,

Colla²,

Vannucci³

et al. 2010

International Conference on Fuzzy Systems

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Recently in many industrial fields the exploitation of vision systems for quality control had a considerable increase, which is mainly due to the technological progress experienced by such systems, that, with respect to the past, made their performance more appealing and more reliable while the associated costs are decreased. The advantages of these kind of systems in terms of savings in human resources and improved quality monitoring have become far more evident, by encouraging their adoption in a wide variety of production cycles.The present paper deals with the elaboration and information extraction from images, that represent portions of the surface of flat steel products, and describes an algorithm for defect detection and classification. The overall classification procedure is composed of a preliminary part that is mostly related to image processing and analysis, which aims at pointing out the defect (independently on the class it belongs to), as well as to the extraction of relevant features of the detected defect; the second part exploits a fuzzy inference system in order to analyze the type of defect and solves a classification problem that presently can be addressed only with the support of a human operator. Fuzzy inference systems are suitable to this application because they are able to mimic and reproduce the human reasoning.

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The Importance of Variable Selection for Neural Networks-Based Classification in an Industrial Context

Cateni

Colla

2016

Advances in Neural Networks

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Fuzzy logic classification in image processing

Cited by 13 publications

References 6 publications

Experimental Study on A Reinforced Concrete Element to Extract the Durability Index with the Automated Visualization

Experimental Study on A Reinforced Concrete Element to Extract the Durability Index with the Automated Visualization

A fuzzy inference system applied to defect detection in flat steel production

The Importance of Variable Selection for Neural Networks-Based Classification in an Industrial Context

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