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Research of Cotton Foreign Fiber Feature Processing Based on Automated Visual Inspection

Author: ZhaoXueHua
Tutor: ChenGuiFen
School: Jilin Agricultural University
Course: Applied Computer Technology
Keywords: Foreign fiber Automatic visual inspection Feature Extraction Ant Colony Algorithm Image Processing Technology
CLC: TP391.41
Type: Master's thesis
Year: 2011
Downloads: 56
Quote: 0
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Abstract


The cotton industry occupies an important position in the national economy of our country, but more foreign fiber content, the cotton quality is generally not high. Foreign fiber, although the proportion of the total amount of cotton is small, but, if not clear, it will seriously affect the quality of cotton yarn and textiles. Currently, foreign fiber detection, eliminate system mainly based on automatic visual technology-based, but the detection rate is generally excluded about 80%. For the foreign fiber line to detect the presence of the problem, the target of foreign fiber lint image as the research object, the use of machine vision technology, image processing technology and pattern recognition technology, research-based automatic visual inspection of foreign fiber target feature processing method. This paper studies the content and innovation as follows: ① extraction method based on building characteristics of cotton the heterosexual fiber combination eigenvectors. Representation of the image features a combination of color, texture and shape-based, combined with foreign fiber types, are not easily distinguishable characteristics, this paper used will be based on color, texture and shape of combination of eigenvectors cotton foreign fiber method. Color-based feature extraction methods, focusing on a different order moments of each component represents the color characteristics of RGB color space and HSV color space, 24 color features were extracted; texture based feature extraction methods, the focus is gray degree of symbiosis of the matrix method, gray - gradient co-occurrence matrix method, gray - smooth symbiotic matrix method, gray-scale differential Statistics Act, 43 texture features are extracted; shape-based feature feature extraction methods, mainly to extract the most suitable said foreign fiber shape features. Use of the above extract color features, texture and shape of a combination of 75-dimensional feature vector of foreign fiber. (2) improved ant colony algorithm foreign fiber-based target feature to select the method of study. Target for foreign fiber combination of high-dimensional feature vector, and each feature has different classification performance problems and the feature space has multiple qualitative problem based on improved ant colony algorithm of optimal feature vector selection by the initial probability pretreatment program go to the processing of redundant features to accelerate the running speed of the algorithm; avoid local convergence segmented variation, improve search efficiency. 24 color characteristics, the method first extracts a target of foreign fibers eight shape features and 43 texture features, constitute a combination of 75-dimensional feature vector; then take advantage of the improved ant colony algorithm selected from the combination of 75-dimensional feature vector classification The strongest feature combination. The experimental results show that the improved ant colony algorithm is faster than traditional ant colony algorithm speed, optimal feature subset selected smaller classification capabilities. ③ foreign fiber-based image processing technology feature extraction and selection system design and realization. In order to verify the validity and correctness of the above-mentioned method, designed to achieve the target feature extraction and selection of foreign fiber. The software system mainly includes the parameter setting, image processing, feature extraction and feature selection of four modules, by the large number of experiments show that the system, the study based on the automatic visual inspection of foreign fiber target feature extraction and selection methods valid.

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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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