Inspired by the human visual perception system, hexagonal image processing in the context of machine learning deals with the development of image processing systems that combine the advantages of evolutionary motivated structures based on biological models. While conventional state-of-the-art image processing systems of recording and output devices almost exclusively utilize square arranged methods, their hexagonal counterparts offer a number of key advantages that can benefit both researchers and users. This contribution serves as a general application-oriented approach the synthesis of the therefore designed hexagonal image processing framework, called Hexnet, the processing steps of hexagonal image transformation, and dependent methods. The results of our created test environment show that the realized framework surpasses current approaches of hexagonal image processing systems, while hexagonal artificial neural networks can benefit from the implemented hexagonal architecture. As hexagonal lattice format based deep neural networks, also called H-DNN, can be compared to their square counterparts by transforming classical square lattice based data sets into their hexagonal representation, they can also result in a reduction of trainable parameters as well as result in increased training and test rates.
翻译:在人类视觉认知系统的启发下,机器学习背景下的六边形图像处理工作涉及开发图像处理系统,这些系统结合了基于生物模型的进化驱动结构的优势。虽然记录和输出装置的常规最先进的图像处理系统几乎完全使用平方排列方法,但其六边形对等系统提供了一系列关键优势,既有利于研究人员,也有利于用户。这一贡献是一种通用的面向应用的方法,综合了因此设计的六边形图像处理框架,称为Hexnet,六边形图像转换的处理步骤和依附方法。我们创建的测试环境结果表明,已实现的框架超过了六边形图像处理系统的现有方法,而六边形人造神经网络可以从已实施的六边形结构中受益。作为基于六边形阵形的深层神经网络,也称为H-DNN,可以通过将基于经典平方阵形的数据集转化为其六边形代表,将其与正方形对等相进行比较,还可以减少可训练的参数,并导致培训和测试率的提高。