The Experts below are selected from a list of 678 Experts worldwide ranked by ideXlab platform
Gao Yanfeng - One of the best experts on this subject based on the ideXlab platform.
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Detection of Watermelon Seeds Exterior Quality based on Machine Vision
TELKOMNIKA Indonesian Journal of Electrical Engineering, 2013Co-Authors: Xiai Chen, Ling Wang, Wenquan Chen, Gao YanfengAbstract:To investigate the detection of watermelon seeds exterior quality, a machine vision system based on least square support vector machine was developed. Appearance characteristics of watermelon seeds included area, perimeter, roughness, minimum Enclosing Rectangle and solidity were calculated by image analysis after image preprocess.The broken seeds, normal seeds and high-quality seeds were distinguished by least square support vector machine optimized by genetic algorithm. Compared to the grid search algorithm, the classification results of watermelon seeds exterior quality achieved by genetic algorithm were analyzed in detail. Meanwhile machine vision grid laser was applied to detect the surface irregularities defects of watermelon seeds. This study demonstrated the feasible of detecting the watermelon seeds exterior quality by machine vision. DOI: http://dx.doi.org/10.11591/telkomnika.v11i6.2605 Full Text: PDF
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Detection of Watermelon Seeds Exterior Quality based on Machine Vision
Institute of Advanced Engineering and Science, 2013Co-Authors: Chen Xiai, Wang Ling, Chen Wenquan, Gao YanfengAbstract:To investigate the detection of watermelon seeds exterior quality, a machine vision system based on least square support vector machine was developed. Appearance characteristics of watermelon seeds included area, perimeter, roughness, minimum Enclosing Rectangle and solidity were calculated by image analysis after image preprocess.The broken seeds, normal seeds and high-quality seeds were distinguished by least square support vector machine optimized by genetic algorithm. Compared to the grid search algorithm, the classification results of watermelon seeds exterior quality achieved by genetic algorithm were analyzed in detail. Meanwhile machine vision grid laser was applied to detect the surface irregularities defects of watermelon seeds. This study demonstrated the feasible of detecting the watermelon seeds exterior quality by machine vision. DOI: http://dx.doi.org/10.11591/telkomnika.v11i6.260
Ling Wang - One of the best experts on this subject based on the ideXlab platform.
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Detection of Watermelon Seeds Exterior Quality based on Machine Vision
TELKOMNIKA Indonesian Journal of Electrical Engineering, 2013Co-Authors: Xiai Chen, Ling Wang, Wenquan Chen, Gao YanfengAbstract:To investigate the detection of watermelon seeds exterior quality, a machine vision system based on least square support vector machine was developed. Appearance characteristics of watermelon seeds included area, perimeter, roughness, minimum Enclosing Rectangle and solidity were calculated by image analysis after image preprocess.The broken seeds, normal seeds and high-quality seeds were distinguished by least square support vector machine optimized by genetic algorithm. Compared to the grid search algorithm, the classification results of watermelon seeds exterior quality achieved by genetic algorithm were analyzed in detail. Meanwhile machine vision grid laser was applied to detect the surface irregularities defects of watermelon seeds. This study demonstrated the feasible of detecting the watermelon seeds exterior quality by machine vision. DOI: http://dx.doi.org/10.11591/telkomnika.v11i6.2605 Full Text: PDF
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Detection and Classification of Watermelon Seeds Exterior Quality Based on LS-SVM Using Machine Vision
Applied Mechanics and Materials, 2013Co-Authors: Xiai Chen, Ling WangAbstract:A machine vision system was developed to investigate the detection of watermelon seeds exterior quality. The main characteristics of watermelon seeds appearance including area, perimeter, roughness and minimum Enclosing Rectangle were calculated by image analysis. Least square support vector machine optimized by genetic algorithm was applied for the classification of watermelon seeds exterior quality, and the broken seeds, normal seeds and high-quality seeds were distinguished finally. The surface irregularities defects of watermelon seeds were detected by machine vision grid laser. The experimental results show that the watermelon seeds exterior quality could be well detected and classified by machine vision based on least squares support vector machine.
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ICICA (1) - Classification of Rice Appearance Quality Based on LS-SVM Using Machine Vision
Communications in Computer and Information Science, 2012Co-Authors: Xiai Chen, Ling Wang, Wenquan ChenAbstract:A machine vision system was developed for rice quality detection in this paper. The main characteristics of rice appearance including area, perimeter, roughness and minimum Enclosing Rectangle were calculated by image analysis. The Least Squares Support Vector Machines was applied for the classification of head rice and broken rice. Genetic algorithm was used to optimize the parameters values of Least Squares Support Vector Machines. The robustness of this classification method was testified, and the experiment result shows that the head rice and broken rice can be effectively identified by Least Squares Support Vector Machines using machine vision.
Ping-Mian Kou - One of the best experts on this subject based on the ideXlab platform.
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Study on the detection of yarn hairiness morphology based on image processing technique
2010 International Conference on Machine Learning and Cybernetics, 2010Co-Authors: Xiao Hong Wang, Jing-Yang Wang, Ji-Lei Zhang, Hong-Wei Liang, Ping-Mian KouAbstract:Yarn hairiness is an important factor that affects the textile appearance, feel and usability, which is also very crucial to the quality assessment of the yarns. This paper presents a new detection method of yarn hairiness based on image procession technique to solve the disadvantages of low efficiency and low precision existed in current detection methods. This method firstly obtains the yarn image using digital camera, then extracts the yarn skeleton by image processing technology, calculates the yarn length by the minimum Enclosing Rectangle (MER) and calculates the hairiness length by pixel search method, finally gets the Hairiness Index by the proportional relationship between the actual length and pixel length of the reference object. The experiment result indicates that the efficiency and precision of yarn hairiness detection is improved.
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ICMLC - Study on the detection of yarn hairiness morphology based on image processing technique
2010 International Conference on Machine Learning and Cybernetics, 2010Co-Authors: Xiao Hong Wang, Jing-Yang Wang, Ji-Lei Zhang, Hong-Wei Liang, Ping-Mian KouAbstract:Yarn hairiness is an important factor that affects the textile appearance, feel and usability, which is also very crucial to the quality assessment of the yarns. This paper presents a new detection method of yarn hairiness based on image procession technique to solve the disadvantages of low efficiency and low precision existed in current detection methods. This method firstly obtains the yarn image using digital camera, then extracts the yarn skeleton by image processing technology, calculates the yarn length by the minimum Enclosing Rectangle (MER) and calculates the hairiness length by pixel search method, finally gets the Hairiness Index by the proportional relationship between the actual length and pixel length of the reference object. The experiment result indicates that the efficiency and precision of yarn hairiness detection is improved.
Xiai Chen - One of the best experts on this subject based on the ideXlab platform.
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Detection of Watermelon Seeds Exterior Quality based on Machine Vision
TELKOMNIKA Indonesian Journal of Electrical Engineering, 2013Co-Authors: Xiai Chen, Ling Wang, Wenquan Chen, Gao YanfengAbstract:To investigate the detection of watermelon seeds exterior quality, a machine vision system based on least square support vector machine was developed. Appearance characteristics of watermelon seeds included area, perimeter, roughness, minimum Enclosing Rectangle and solidity were calculated by image analysis after image preprocess.The broken seeds, normal seeds and high-quality seeds were distinguished by least square support vector machine optimized by genetic algorithm. Compared to the grid search algorithm, the classification results of watermelon seeds exterior quality achieved by genetic algorithm were analyzed in detail. Meanwhile machine vision grid laser was applied to detect the surface irregularities defects of watermelon seeds. This study demonstrated the feasible of detecting the watermelon seeds exterior quality by machine vision. DOI: http://dx.doi.org/10.11591/telkomnika.v11i6.2605 Full Text: PDF
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Detection and Classification of Watermelon Seeds Exterior Quality Based on LS-SVM Using Machine Vision
Applied Mechanics and Materials, 2013Co-Authors: Xiai Chen, Ling WangAbstract:A machine vision system was developed to investigate the detection of watermelon seeds exterior quality. The main characteristics of watermelon seeds appearance including area, perimeter, roughness and minimum Enclosing Rectangle were calculated by image analysis. Least square support vector machine optimized by genetic algorithm was applied for the classification of watermelon seeds exterior quality, and the broken seeds, normal seeds and high-quality seeds were distinguished finally. The surface irregularities defects of watermelon seeds were detected by machine vision grid laser. The experimental results show that the watermelon seeds exterior quality could be well detected and classified by machine vision based on least squares support vector machine.
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ICICA (1) - Classification of Rice Appearance Quality Based on LS-SVM Using Machine Vision
Communications in Computer and Information Science, 2012Co-Authors: Xiai Chen, Ling Wang, Wenquan ChenAbstract:A machine vision system was developed for rice quality detection in this paper. The main characteristics of rice appearance including area, perimeter, roughness and minimum Enclosing Rectangle were calculated by image analysis. The Least Squares Support Vector Machines was applied for the classification of head rice and broken rice. Genetic algorithm was used to optimize the parameters values of Least Squares Support Vector Machines. The robustness of this classification method was testified, and the experiment result shows that the head rice and broken rice can be effectively identified by Least Squares Support Vector Machines using machine vision.
Timothy M. Chan - One of the best experts on this subject based on the ideXlab platform.
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Smallest k-Enclosing Rectangle Revisited
Discrete & Computational Geometry, 2020Co-Authors: Timothy M. Chan, Sariel Har-peledAbstract:Given a set of n points in the plane, and a parameter $$k$$ k , we consider the problem of computing the minimum (perimeter or area) axis-aligned Rectangle Enclosing $$k$$ k points. We present the first near quadratic time algorithm for this problem, improving over the previous near- $$O(n^{5/2})$$ O ( n 5 / 2 ) -time algorithm by Kaplan et al. (25th European Symposium on Algorithms. Leibniz Int Proc Inform, vol. 87, # 52. Leibniz-Zent Inform, Wadern, 2017). We provide an almost matching conditional lower bound, under the assumption that $$(\min ,+)$$ ( min , + ) -convolution cannot be solved in truly subquadratic time. Furthermore, we present a new reduction (for both perimeter and area) that can make the time bound sensitive to $$k$$ k , giving near $$O(nk)$$ O ( n k ) time. We also present a near linear time $$(1+\varepsilon )$$ ( 1 + ε ) -approximation algorithm to the minimum area of the optimal Rectangle containing $$k$$ k points. In addition, we study related problems including the 3-sided, arbitrarily oriented, weighted, and subset sum versions of the problem.
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Smallest k-Enclosing Rectangle Revisited.
arXiv: Computational Geometry, 2019Co-Authors: Timothy M. Chan, Sariel Har-peledAbstract:Given a set of $n$ points in the plane, and a parameter $k$, we consider the problem of computing the minimum (perimeter or area) axis-aligned Rectangle Enclosing $k$ points. We present the first near quadratic time algorithm for this problem, improving over the previous near-$O(n^{5/2})$-time algorithm by Kaplan etal [KRS17]. We provide an almost matching conditional lower bound, under the assumption that $(\min,+)$-convolution cannot be solved in truly subquadratic time. Furthermore, we present a new reduction (for either perimeter or area) that can make the time bound sensitive to $k$, giving near $O(n k) $ time. We also present a near linear time $(1+\varepsilon)$-approximation algorithm to the minimum area of the optimal Rectangle containing $k$ points. In addition, we study related problems including the $3$-sided, arbitrarily oriented, weighted, and subset sum versions of the problem.
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Semi-Online Maintenance of Geometric Optima and Measures
SIAM Journal on Computing, 2003Co-Authors: Timothy M. ChanAbstract:We give the first nontrivial worst-case results for dynamic versions of various basic geometric optimization and measure problems under the semi-online model, where during the insertion of an object we are told when the object is to be deleted. Problems that we can solve with sublinear update time include the Hausdorff distance of two point sets, discrete 1-center, largest empty circle, convex hull volume in three dimensions, volume of the union of axis-parallel cubes, and minimum Enclosing Rectangle. The decision versions of the Hausdorff distance and discrete 1-center problems can be solved fully dynamically. Some applications are mentioned.
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SODA - Semi-online maintenance of geometric optima and measures
2002Co-Authors: Timothy M. ChanAbstract:We give the first nontrivial worst-case results for dynamic versions of various basic geometric optimization and measure problems under the semi-online model, where during the insertion of an object we are told when the object is to be deleted. Problems that we can solve with sublinear update time include the Hausdorff distance of two point sets, discrete 1-center, largest empty circle, convex hull volume in three dimensions, volume of the union of axis-parallel cubes, and minimum Enclosing Rectangle. The decision versions of the Hausdorff distance and discrete 1-center problems can be solved fully dynamically. Some applications are mentioned.