Butterflies have a prominent role in the agro-ecological ecosystems. Some butterfly populations can injure wildlife, vegetation, and even humans in addition to causing harm to flora and fauna. By contrast, the presence of some other ones can help in improving agricultural productivity and preserving the agro-ecological ecosystems. Butterfly segmentation is therefore an initial process that precedes species recognition. In this paper, we propose a new segmentation process that adapts quantum mechanics to be deployed on a multi-layered graph. To achieve a proper butterfly segmentation, we implement efficiently the Schrödinger equation in a propagation process across the different layers of the graph. Furthermore, It is supported by both background and foreground priors guidance coupled to local contrast information. Comparative evaluation suggests that our method has higher resistance than competing methods to artefacts that are inherent to agro-ecological photographs. Our algorithm shows a considerable advantage over single-layered graph based versions when dealing with some image details. It also outperforms some deep learning based methods that achieve high segmentation performance. Unlike these methods, ours does not involve any training step. Thus, it doesn't require high performance equipments or supplementary human labelling operation and does not fall in the problem of generalization.

中文翻译：

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农业生态环境中多尺度量子切割的蝴蝶分割

蝴蝶在农业生态系统中发挥着重要作用。有些蝴蝶种群除了对动植物造成伤害外，还会伤害野生动物、植被，甚至人类。相比之下，其他一些因素的存在有助于提高农业生产力和保护农业生态系统。因此，蝴蝶分割是物种识别之前的初始过程。在本文中，我们提出了一种新的分割过程，该过程适应量子力学以部署在多层图上。为了实现正确的蝴蝶分割，我们在图的不同层的传播过程中有效地实现了薛定谔方程。此外，它还得到背景和前景先验指导以及局部对比度信息的支持。比较评估表明，我们的方法比竞争方法对农业生态照片固有的伪影具有更高的抵抗力。在处理某些图像细节时，我们的算法比基于单层图的版本显示出相当大的优势。它还优于一些基于深度学习的方法，可实现高分割性能。与这些方法不同，我们的方法不涉及任何训练步骤。因此，它不需要高性能的设备或辅助的人工标记操作，并且不存在通用化的问题。