Classification and identification of foreign fibers in cotton on the basis of a support vector machine

Ji, Ronghua; Li, Bingbing; Lai-rong, Chen; Yang, Wenzhu

doi:10.1016/j.mcm.2009.10.007

Cited by 29 publications

(13 citation statements)

References 3 publications

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“…Ronghua Ji et al [4] have presented a paper on classification and identification of foreign fibers in cotton on the basis of a support vector machine. The paper uses the cotton foreign fibers as the research object and acquires the images under simulated actual cotton processing environment.…”

Section: Related Workmentioning

confidence: 99%

Comparative Study of Certain Classifiers for Variety Classification of Certain Thin and Thick Fabric Images

Anami¹,

Elemmi²

2019

IJIGSP

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The proposed work gives a comparative study of three different classifiers, namely, decision tree (DT), support vector machine (SVM) and artificial neural network (ANN) for variety classification of certain thin and thick fabric images. The textural features are used in the work. The overall classification rates of 85%, 86% and 94% are obtained for DT, SVM and ANN classifiers respectively. Better results for varieties of thick fabric images are obtained compared to the varieties of thin fabric images. Further, the ANN classifier has given good classification rate than DT and SVM classifiers. But, it is also observed that, DT classifier gives better results in case of varieties of thick fabric images. The work finds applications in apparel industry, cost estimation, setting the washing time, fashion design etc.

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Section: Related Workmentioning

confidence: 99%

Comparative Study of Certain Classifiers for Variety Classification of Certain Thin and Thick Fabric Images

Anami¹,

Elemmi²

2019

IJIGSP

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“…The detection speed was increased by reducing the amount of computation. Ji et al 3 distinguished defects based on determining the lowest rectangular defect images and calculating the ratio of the defect area to the rectangular area. In addition to using a morphological image, Chiou 4 used the parameters of a histogram image to set the decision node and end node of a decision tree.…”

Section: Introductionmentioning

confidence: 99%

Machine vision based in-process light-emitting diode chip mounting system

Chen

2018

Measurement and Control

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Background: In this study, a machine vision–based method was developed for automated in-process light-emitting diode chip mounting lines with position uncertainty. In order to place the tiny light-emitting diode chips on the pattern of a printed circuit board, a highly accurate mounting process is achieved with online feedback of the visual assistance. Methods: The system consists of a charge-coupled device camera, a six-axis robot arm, and a delta robot. The lighting system is a critical point for the in-process machine vision problem. Hence, designing the optimal lighting solution is one of the most difficult parts of a machine vision system, and several lighting techniques and experiments are examined in this study. In order to commence the mounting process, the light-emitting diode chip targets inside the camera field were identified and used to guide the delta robot to the grabbing zone based on the calibrated homography transformation. Efforts have been focused on the field of machine vision–based feature extraction of the chip pins and the holes on the printed circuit board. The correspondence of each other is determined by the position of the chip pins and the printed circuit board circuit pattern. The image acquisition is achieved directly online in real time. The image analysis algorithm must be sufficiently fast to follow the production rate. In order to compensate for the uncertainty of the light-emitting diode chip mounting process, a visual feedback strategy in conjunction with an uncertainty compensation strategy is employed. Results: Finally, the light-emitting diode chip was automatically grabbed and accurately placed at the desired positions. Conclusion: On-line and off-line experiments were conducted to investigate the performance of the vision system with respect to detecting and mounting light-emitting diode chips.

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“…Some methods are put forward based on optoelectronic technology which is nondestructive, and non-contacting, and speedy [3][4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Research on influence on luminance of machine vision to measure cotton defects

Zhang

Yusheng

Qiao

et al. 2015

2015 International Conference on Optoelectronics and Microelectronics (ICOM)

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Ginning cotton qualities influence seriously the quality of textiles. Ginning cotton is always mixed with various defects during the picking, transporting, storing, and processing. This paper proposed one novel method to solve the problems of intensity changes of light source. Firstly, the properties of cotton and defects are studied. Secondly, the setup of measuring system is introduced and image processing algorithm is proposed. Finally, experimental results are discussed and the optimal drive circuit of light sources is designed. The results show the measuring system can meet with the requirement of nondestructive measurement of ginning cotton quality. The experimental results show that the accuracy of the cotton defect's measuring system is above 90%, and the measuring time is within 3 second. This system can meet with the requirement of fast measurement of cotton ginning qualities in field.

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Classification and identification of foreign fibers in cotton on the basis of a support vector machine

Cited by 29 publications

References 3 publications

Comparative Study of Certain Classifiers for Variety Classification of Certain Thin and Thick Fabric Images

Comparative Study of Certain Classifiers for Variety Classification of Certain Thin and Thick Fabric Images

Machine vision based in-process light-emitting diode chip mounting system

Research on influence on luminance of machine vision to measure cotton defects

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