Contribution of fuzzy reasoning method to knowledge integration in a defect recognition system

2007 ◽  
Vol 58 (4) ◽  
pp. 355-366 ◽  
Author(s):  
Vincent Bombardier ◽  
Cyril Mazaud ◽  
Pascal Lhoste ◽  
Raphaël Vogrig
Keyword(s):  
Knowledge Integration ◽  
Fuzzy Reasoning ◽  
Recognition System ◽  
Defect Recognition

scholarly journals The investigation on defect recognition system using gaussian smoothing and template matching approach

2020 ◽  
Vol 18 (2) ◽  
pp. 812
Author(s):  
M. H. Harun ◽  
M. F. Yaakub ◽  
A. F. Z. Abidin ◽  
A. H. Azahar ◽  
M. S. M. Aras ◽  
...  
Keyword(s):  
Defect Detection ◽  
Template Matching ◽  
Recognition Rate ◽  
Region Of Interest ◽  
Recognition System ◽  
Automated Inspection ◽  
Gaussian Smoothing ◽  
Important Region ◽  
Detection Approach ◽  
Defect Recognition

<p>This paper investigates various approaches for automated inspection of gluing process using shape-based matching application. A new supervised defect detection approach to detect a class of defects in gluing application is proposed. Creating of region of interest in important region of object is discussed. Gaussian smoothing features is proposed in determining better image processing. Template matching in differentiates between reference and tested image are proposed. This scheme provides high computational savings and results in high defect detection recognition rate. The defects are broadly classified into three classes: 1) gap defect; 2) bumper defect; 3) bubble defect. This system does lessen execution time, yet additionally produce high precision in deformity location rate. It is discovered that the proposed framework can give precision at 95.77% recognition rate in recognizing imperfection for gluing application.</p>

Recognition System for Awakening and Sleeping by Fuzzy Reasoning

1996 ◽  
Vol 8 (2) ◽  
pp. 167-170
Author(s):  
Takashi Oyabu ◽  
Keyword(s):  
Gas Sensor ◽  
Tin Oxide ◽  
Fuzzy Reasoning ◽  
Recognition System ◽  
Sensor Output ◽  
Human Behaviors ◽  
Compositional Method

Awakening and sleeping are recognized as human behaviors by a gas-sensor output using a fuzzy reasoning. The gas sensor is made from a tin oxide and is sensitive for various gases. The sensor output has a daily periodical pattern. The pattern descends gradually during sleeping and ascends suddenly as of awakening. Recognition is performed using the characteristics of the pattern. At that time, the min-max compositional method is adopted. There are only six rules in this system.

Film Defects of Lithium Battery Recognition Based on Brightness and One-against-All Support Vector Machine

2013 ◽  
Vol 462-463 ◽  
pp. 155-158
Author(s):  
Gong Chen ◽  
Xi Fang Zhu ◽  
Qing Quan Xu ◽  
An Cheng Xu ◽  
Hui Yang
Keyword(s):  
Support Vector Machine ◽  
Traditional Method ◽  
Lithium Battery ◽  
Recognition System ◽  
Support Vector ◽  
Improve Quality ◽  
Line Defect ◽  
Charge Coupled Device ◽  
On Line ◽  
Defect Recognition

Lithium battery film on-line defect recognition system is realized based on industrial charge-coupled device (CCD) to improve quality. Otsu algorithm is adopted for threshold instead of traditional method. Area of defect is sorted to get the largest defect and geometry and projective is extracted from image. Film defects of lithium battery recognition is realized based on Brightness Judgment and One-against-all support vector machine (OAA-SVM). Experiment results show that these methods are effective and feasible, the accuracy can reach 90%.

Automated inspection of micro-defect recognition system for color filter

2015 ◽  
Vol 70 ◽  
pp. 6-17 ◽  
Author(s):  
Chung-Feng Jeffrey Kuo ◽  
Kai-Ching Peng ◽  
Han-Cheng Wu ◽  
Ching-Chin Wang
Keyword(s):  
Recognition System ◽  
Color Filter ◽  
Automated Inspection ◽  
Defect Recognition

Research on Image Scene Semantics Recognition System

2013 ◽  
Vol 433-435 ◽  
pp. 330-333
Author(s):  
Li Jie Yin ◽  
Ya Zhen ◽  
Qi Li Fan
Keyword(s):  
False Positive ◽  
Fuzzy Inference ◽  
Target Recognition ◽  
False Negative ◽  
Fuzzy Reasoning ◽  
Recognition System ◽  
Image Preprocessing ◽  
Image Semantics ◽  
Fuzzy Automata

In order to get better image Semantics recognition, in this paper, a scene Semantics recognition system based on fuzzy reasoning is presented. The system contains three parts: image preprocessing, target recognition, and fuzzy reasoning machine. Compared with other methods, the outputs of pattern classifiers are fuzzed, the fuzzy relationships between targets are extracted, and fuzzy inference is performed using fuzzy automata. The experiment indicates that this method could overcome the problems of false positive and false negative of pattern classifiers, and perform relatively more accurate image semantics recognition than other existing methods.

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