There are situations when someone finds a certain object or its remains. Particularly the second case is complicated, because having only a part of the element, it is difficult to identify the full object. In the case of archeological excavations, the fragment should be classified in order to know what we are looking at. Unfortunately, such classification may be a difficult task. Hence, it is essential to focus on certain features which define it, and then to classify the complete object. In this paper, we proposed creating a novel soft tree decision structure. The idea is based on soft sets. In addition, we have introduced convolutional networks to the nodes to make decisions based on graphic files. A new archeological item can be photographed and evaluated by the proposed technique. As a result, the object will be classified depending on the amount of information obtained to the appropriate class. If the object cannot be classified, the method will return individual features and possible class.

中文翻译：

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具有神经成分的软树作为考古发掘的图像处理技术

在某些情况下，有人会找到某个物体或其遗物。特别地，第二种情况是复杂的，因为仅具有元素的一部分，因此难以识别整个对象。在考古发掘的情况下，应该对片段进行分类，以了解我们在看什么。不幸的是，这样的分类可能是困难的任务。因此，必须重点关注定义它的某些功能，然后对完整的对象进行分类。在本文中，我们提议创建一种新颖的软树决策结构。这个想法是基于软集合的。此外，我们还向节点引入了卷积网络，以基于图形文件做出决策。可以通过提出的技术对新的考古物品进行拍照和评估。结果是，该对象将根据获得的信息量分类为适当的类别。如果无法对对象进行分类，则该方法将返回单个特征和可能的类。