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摘要: 针对棉花纤维中混杂的色度不明显、细微等异类杂质难踢除的问题, 本文给出一种新的伪彩色方法以增强原始纤维图像, 突出异类杂质, 从而达到成功分离出其中所含的各类杂质的目的. 本伪彩色方法基于图像的梯度、灰度、局部熵及局部尺度形态滤波器特征信息, 并利用各个特征之间的相互关系, 自适应地标识出不同类型的杂质. 实验表明, 本方法在棉花纤维检测方面优于经典伪彩色方法, 适用于高精度的提纯这一类纤维的自动化检测系统.Abstract: In this paper, we propose a novel pseudo-color approach which can effectively mark all different particulars while it has the ability to divide foreign pieces with indiscernible brightness in fibre masses. Hence, it yields highly separate color images for this kind of industrial images. Our approach aims at constructing an adaptive fuzzy rule based on a feature space of gradients, gray values, local entropies, and local features from scalable morphological filters. Due to the fact that the method is based on a full contextual information, it can handle small changes in images more adaptively than classical pseudo-color methods. And it is perfectly suitable to distinguish impurities mixed in fibre masses, as tested in a simulating cotton inspection system and demonstrated by several cotton sample images.
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Key words:
- Pseudo-color coding /
- false color /
- visual inspection /
- cotton fibres
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