REPRODUCIBLE KERNEL HILBERT SPACE BASED GLOBAL AND LOCAL IMAGE SEGMENTATION
REPRODUCIBLE KERNEL HILBERT SPACE BASED GLOBAL AND LOCAL IMAGE SEGMENTATION
复制标题
DOI：
10.3934/ipi.2020048
复制
发表时间：
2021-02-01
影响因子：
1.3
通讯作者：
Torella, Francesco
中科院分区：
文献类型：
--
作者：
Burrows, Liam;Guo, Weihong;Torella, Francesco
图像分割是将图像分割成单个对象的任务，在广泛的领域中具有许多重要的应用。大多数分割方法依赖于图像强度梯度来定义对象之间的边缘。然而，当两个对象之间的对比度较低时，强度梯度无法识别边缘。在本文中，我们的目的是介绍的方法，使这种弱边缘更加突出，以提高低对比度的对象的分割结果。这是针对两种分割模型完成的：全局和局部。我们使用一个再生核希尔伯特空间和近似的Heaviside函数的组合来分解图像，然后展示如何将这种分解应用于分割模型。我们展示了一些结果和对噪声的鲁棒性，以及证明我们可以将重建和分割模型联合收割机结合在一起，使我们能够同时获得分解和分割。
Image segmentation is the task of partitioning an image into individual objects, and has many important applications in a wide range of fields. The majority of segmentation methods rely on image intensity gradient to define edges between objects. However, intensity gradient fails to identify edges when the contrast between two objects is low. In this paper we aim to introduce methods to make such weak edges more prominent in order to improve segmentation results of objects of low contrast. This is done for two kinds of segmentation models: global and local. We use a combination of a reproducing kernel Hilbert space and approximated Heaviside functions to decompose an image and then show how this decomposition can be applied to a segmentation model. We show some results and robustness to noise, as well as demonstrating that we can combine the reconstruction and segmentation model together, allowing us to obtain both the decomposition and segmentation simultaneously.