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Cross - sectional image segmentation method based on genetic algorithm and support vector machine blended yarn

Author: LinSen
Tutor: ZhouLingKe
School: Nanjing University of Technology and Engineering
Course: Control Theory and Control Engineering
Keywords: Blended yarn Wool Viscose Image processing Genetic Algorithms Support Vector Machine Parameter optimization
CLC: TP391.41
Type: Master's thesis
Year: 2010
Downloads: 84
Quote: 0
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Abstract


The textile industry is a traditional pillar industries, textile testing is an integral part of textile industry . Most traditional detection methods rely on manual and subjective appraisal . How to improve the objectivity and the degree of automation of the detection method is a critical issue facing the sustainable development of the industry . With the rapid development of computer image processing techniques and machine learning theory , and many other fields glow with a new vitality . How to integrate the computer image processing technology and machine learning theory , access to good textile testing results , and is one of the key technical difficulties . This paper focuses on the use of support vector machine cross-sectional images of the wool and viscose blended yarn , wool and viscose are two categories split . The main work done as follows : ( 1) for the split object of a more generic image preprocessing process . (2) two different materials blended yarn wool and viscose for feature extraction . Proceed from both differences in morphology , to compare them with the rules of the circle , oval , rectangle , bounding polygon need for SVM training sample set . (3) study the basic framework of the theory of genetic algorithms and support vector machine . The help of both the framework of the genetic algorithm selection operator improvements , select the type of kernel function of support vector machine . And an application example , made ??improvements and selected rationality . (4) An Empirical Analysis of image segmentation method based on genetic algorithm and support vector machine . Wool and viscose blended yarn cross-section image segmentation application background , the proposed method is validated , the results indicate that the feasibility of the proposed method . ( 5 ) comparative analysis of the non- heuristic grid search parameter optimization and heuristic genetic algorithm parameter optimization . Mainly on the speed of the classifier model are compared and analyzed , indicating a genetic algorithm to combine the advantages of support vector machine method . Finally, combining support vector machines using genetic algorithms , of single wool and viscose blended yarn cross-section image segmentation , and the results of calculation and analysis of the yarn quality parameters .

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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