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Wood inspection with non-supervised clustering

机译：无监督聚类的木材检查

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The appearance of sawn timber has huge natural variations that the human inspector easily compensates for mentally when determining the types of defects and the grade of each board. However, for automatic wood inspection systems these variations are a major source for complication. This makes it difficult to use textbook methodologies for visual inspection. These methodologies generally aim at systems that are trained in a supervised manner with samples of defects and good material, but selecting and labeling the samples is an error-prone process that limits the accuracy that can be achieved. We present a non-supervised clustering-based approach for detecting and recognizing defects in lumber boards. A key idea is to employ a self-organizing map (SOM) for discriminating between sound wood and defects. Human involvement needed for training is minimal. The approach has been tested with color images of lumber boards, and the achieved false detection and error escape rates are low. The approach also provides a self-intuitive visual user interface.
机译：锯材的外观具有巨大的自然变化，在确定缺陷的类型和每块木板的等级时，人工检查人员很容易在心理上进行补偿。然而，对于自动木材检查系统，这些变化是复杂化的主要来源。这使得很难使用教科书的方法进行视觉检查。这些方法通常针对以有缺陷和良好材料的样本以监督方式进行训练的系统，但是选择和标记样本是易于出错的过程，这限制了可以实现的准确性。我们提出了一种基于非监督聚类的方法来检测和识别木板中的缺陷。关键思想是采用自组织图（SOM）来区分有声木材和缺陷。培训所需的人员参与很少。该方法已经用木板的彩色图像进行了测试，并且实现的错误检测和错误逃逸率很低。该方法还提供了一种直观的可视用户界面。

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