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数学形态学图象

于 2022-08-06 发布 文件大小:119.51 kB
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资源描述  随着对海洋的开发和勘探需求的增长,用于民用和军用等各领域的水下目标探测技术得到了突飞猛进的发展。其中,图象信息作为主要信息来源成为水下探测技术研究的焦点之一。成像声纳由于具有更大的成像范围和抗干扰能力,成为水下目标搜索和识别的主要工具。 本论文的工作是智能水下机器人“声视觉系统”中的一部分,进行水声图象的预处理和识别算法的研究。在众多的算法中,基于频域分割原理的线性处理方法在去除噪声的同时会模糊图象的边缘,很难满足针对水声图象特点的“保边去噪”的要求;而一些非线性方法又缺乏统一的理论基础,使其在系统整体的应用中受到限制。 本文针对水声图象预处理和识别的特殊性,把数学形态学的形态变换应用到“声视觉系统”的预处理和识别中,实现了以数学形态学为理论基础的非线性处理方法,使得图象的去噪、边缘提取和物体识别等处理较传统方法优越和快速。 文中介绍了二值形态学基本变换与其改进算法,灰度形态变换与彩色形态变换,并详细分析了它们的性质以及用于图象预处理和分析识别时的滤波性能。另外对一些经典的图象处理和分析算法进行了充分的仿真研究和比较,得出了有用的结论,同时给出了大量水声图象的处理结果。最后实现了一些常用线性和非线性算法,与数学形态学算法一起形成一个适用于水声图象处理和分析的软件。

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