基于通用字典的局部机织物纹理稳定表征

为改善固定字典表征织物纹理的效果,提出基于通用字典表征织物纹理的方法。首先,对字典学习法进行优选,分别选取8种任意正常织物样本和20种不同组织结构样本,采用优选后的字典学习法得到了1种普通通用字典、4种组织结构通用字典和1种联合组织结构通用字典。最后对通用字典的有效性进行试验验证,发现普通通用字典较固定字典能更好地适应织物纹理;在此基础上,比较了3种通用字典的性能。结果表明:在同等试验条件下,与固定字典相比,通用字典能更好地表征织物纹理;不同类型的通用字典可具有较好的通用或专用性。

Local fabric texture stability characterization using general dictionary

In order to improve the effect of fixed dictionary on fabric texture, a method for characterizing fabric texture based on general dictionary was proposed. Firstly, the dictionary learning method was preferred. Secondly, eight kinds of arbitrary normal fabric samples and 20 samples with different weave patterns were selected respectively, and then a general dictionary, a general dictionary of four organizational structures and a general dictionary of joint organizational structure were obtained by the preferred dictionary learning method. Finally, the validity of the general dictionary was verified by experiments. The results show that the common general dictionary can adapt to the fabric texture better than the fixed dictionary. On this basis, the performance of three general dictionaries was compared. The test results show that compared with fixed dictionaries, general dictionaries can better characterize fabric texture under the same experiment conditions; and different types of general dictionaries can have better universality or specificity.