Deep Neural Networks (DNNs) are generated by sequentially performing linear and non-linear processes. Using a combination of linear and non-linear procedures is critical for generating a sufficiently deep feature space. The majority of non-linear operators are derivations of activation functions or pooling functions. Mathematical morphology is a branch of mathematics that provides non-linear operators for a variety of image processing problems. We investigate the utility of integrating these operations in an end-to-end deep learning framework in this paper. DNNs are designed to acquire a realistic representation for a particular job. Morphological operators give topological descriptors that convey salient information about the shapes of objects depicted in images. We propose a method based on meta-learning to incorporate morphological operators into DNNs. The learned architecture demonstrates how our novel morphological operations significantly increase DNN performance on various tasks, including picture classification and edge detection.
翻译:深神经网络(DNN) 是由连续运行的线性和非线性进程生成的。 使用线性和非线性程序相结合对于生成足够深的地貌空间至关重要。 大多数非线性操作员是激活功能或集合功能的衍生物。 数学形态学是数学的一个分支, 为各种图像处理问题提供非线性操作员。 我们调查了将这些操作纳入本文件中一个端至端深层学习框架的效用。 DNS 旨在为某一特定工作获取现实的描述物。 感官学操作员提供表层描述符, 传达图像所描绘的物体形状的突出信息。 我们提出了一个基于元学习的方法, 将形态操作员纳入 DNNS 。 学习的架构表明我们新型的形态操作如何大大提高 DNN 在各种任务中的性能, 包括图片分类和边缘探测。