基于字典学习的机织物疵点图像等级评定研究
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摘要
针对机织物疵点图像等级自动评定问题,在应用字典学习方法对疵点图像稀疏表达的基础上提出一种等级自动评定方法,采用该方法分别对样本大小和字典原子个数进行优化,首先在机织物纹理图像上截取特定尺寸的疵点纹理图像和正常纹理图像,对正常织物纹理图像进行K-SVD字典学习,然后用学习得到的字典对疵点纹理图像进行重构,最后根据重构效果进行等级评定。实验结果表明:最佳子样本尺寸为128像素×128像素,最佳字典原子个数为256。该方法的自动评定结果相对于人工评定结果准确率达到了83.61%。
In view of automatic evaluation of the image grades of woven fabric defect, an automatic method for evaluating grades was processed based on the application of dictionary learning method to sparse expression of defective images. In this method, the sample size and the number of dictionary atoms were optimized respectively. Firstly, defect texture image and normal texture image of certain size were cropped on the woven fabric texture image, the K-SVD dictionary of the normal fabric texture was studied, and then the defective texture image was reconstructed by the learning dictionary. Finally, the grading was carried out according to the reconstruction results. The experimental results showed that the best subsample size was 128 × 128 pixels and the optimal number of dictionary atoms was 256, the accuracy of this method was 83.61% compared with the accuracy of the manual evaluation.
In view of automatic evaluation of the image grades of woven fabric defect, an automatic method for evaluating grades was processed based on the application of dictionary learning method to sparse expression of defective images. In this method, the sample size and the number of dictionary atoms were optimized respectively. Firstly, defect texture image and normal texture image of certain size were cropped on the woven fabric texture image, the K-SVD dictionary of the normal fabric texture was studied, and then the defective texture image was reconstructed by the learning dictionary. Finally, the grading was carried out according to the reconstruction results. The experimental results showed that the best subsample size was 128 × 128 pixels and the optimal number of dictionary atoms was 256, the accuracy of this method was 83.61% compared with the accuracy of the manual evaluation.
引文
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[7] NAM S, DAVIES M E, ELAD M, et al. The cosparse analysis model and algorithms[J]. Applied and Computational Harmonic Analysis, 2013, 34(1): 30-56.
[8] AHARON M, ELAD M, BRUCKSTEIN A. K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation[J]. IEEE Transactions on Signal Processing, 2006, 54(11): 4311-4322.
[9] 尚丽, 苏品刚. 基于 K-SVD 的偏微分方程模型在毫米波图像恢复中的应用[J]. 计算机应用, 2012, 32(3): 756-758.
[10] BRYT O, ELAD M. Compression of facial images using the K-SVD algorithm[J]. Journal of Visual Communication and Image Representation, 2008, 19(4): 270-282.
[11] TROPP J A, GILBERT A C. Signal recovery from random measurements via orthogonal matching pursuit[J]. IEEE Transactions on information theory, 2007, 53(12): 4655-4666.
[12] 禹建鹰, 郭会清. 美国四分制标准与 GB/T 406—1993 棉本色布国家标准的比较研究[J]. 河南纺织高等专科学校学报, 2007,19(2):29-30.
[2] 李胜旺. 针对坯布疵点的计算机图像模式识别与质量等级划分研究[D]. 北京:北京工业大学, 2007.
[3] 夏冬升, 汪军. 基于小波变换和图像分割的织物等级自动评定[J]. 东华大学学报(自然科学版), 2007, 33(2): 221-226.
[4] 方俊. 纺织面料经柳疵病等级的计算机自动鉴别研究[J]. 丝绸, 2007(7):36-38.
[5] 梁金祥. 织物疵点检测与自动化等级评定系统研究[D]. 苏州:苏州大学, 2011.
[6] RUBINSTEIN R, BRUCKSTEIN A M, ELAD M. Dictionaries for sparse representation modeling[J]. Proceedings of the IEEE, 2010, 98(6): 1045-1057.
[7] NAM S, DAVIES M E, ELAD M, et al. The cosparse analysis model and algorithms[J]. Applied and Computational Harmonic Analysis, 2013, 34(1): 30-56.
[8] AHARON M, ELAD M, BRUCKSTEIN A. K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation[J]. IEEE Transactions on Signal Processing, 2006, 54(11): 4311-4322.
[9] 尚丽, 苏品刚. 基于 K-SVD 的偏微分方程模型在毫米波图像恢复中的应用[J]. 计算机应用, 2012, 32(3): 756-758.
[10] BRYT O, ELAD M. Compression of facial images using the K-SVD algorithm[J]. Journal of Visual Communication and Image Representation, 2008, 19(4): 270-282.
[11] TROPP J A, GILBERT A C. Signal recovery from random measurements via orthogonal matching pursuit[J]. IEEE Transactions on information theory, 2007, 53(12): 4655-4666.
[12] 禹建鹰, 郭会清. 美国四分制标准与 GB/T 406—1993 棉本色布国家标准的比较研究[J]. 河南纺织高等专科学校学报, 2007,19(2):29-30.