Abstract
Ocean front is defined as the interface between different water masses and plays a vital role in the evolution of many physical phenomena. Previous detection methods are based on histogram, Lyapunov exponent, gradient and machine learning. These algorithms, however, introduce discontinuity, inaccuracy, use less information or just approaching traditional results. Moreover, automatic front tracking algrorithm is not open source in preceding studies. This paper foucuses on large-scale ocean fronts and proposes an automatic front detection and tracking algorithm based on Bayesian decision and metric space. In this, front merging, filling and ring deletion are put forward to enhance continuity. The distance between fronts in different days is firstly defined and is well-defined in metric space for functional analysis. These technologies can be migrated to other areas of computer vision such as edge detection and tracking.
Abstract (translated)
海洋前沿被定义为不同水体之间的界面，在许多物理现象的演化中起着至关重要的作用。以往的检测方法基于直方图、李雅普诺夫指数、梯度和机器学习等技术，然而这些算法引入了不连续性、不准确性或信息利用不足的问题，甚至有些方法只是接近传统结果。此外，在先前的研究中，自动前沿追踪算法未开源。 本文重点关注大规模海洋前沿，并提出了一种基于贝叶斯决策和度量空间的自动前沿检测与跟踪算法。文中提出了前沿合并、填充和环形删除等技术以增强连续性。首先定义了不同日期间前沿之间的距离，并在度量空间中进行了良好的定义，以便于函数分析。 这些技术可以迁移到计算机视觉领域的其他方面，例如边缘检测和追踪。
URL
https://arxiv.org/abs/2502.15250
