基于图像亮度均衡的海底矿产资源自动分析方法

Xinliang Ma, Zhiwei He, Jiye Huang, Yanhui Dong, ChuFeng you
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引用次数: 2

摘要

进入21世纪以来,人类对海洋资源的勘探日益频繁,人们日益认识到海洋资源对人类发展的重要作用。但是,目前海底矿产资源深度分析还存在实时性、准确性、有效性等问题,很多地方值得探索。本文的主要目的是应用图像处理和滤波技术,然后分析海底图像的清晰度,准确统计海底矿产资源的覆盖指标,从而实现对该区域海底资源分布的预测。本文的重点是通过调整亮度均衡算法,设置ROI区域的设置区域和窗口直方图均衡(HE)来解决海底黑色连通域的覆盖精度问题。为了达到评价海域资源的目的,采用一系列如色彩校正、双边滤波、窗口HE和二值化处理等图像预处理算法,准确统计覆盖海底矿产资源。本文基于qt环境对视频图像进行处理,包括对视频流和指标数据进行导出处理,生成清晰度评价和黑块连通域覆盖率曲线,可以实现更加准确稳定的海底图像检测指标,实现对海底矿石图像覆盖率的准确统计预测,为今后深度学习的探索奠定基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Automatic Analysis Method for Seabed Mineral Resources Based on Image Brightness Equalization
Since the beginning of the 21st century, the exploration of marine resources has become increasingly frequent, it is increasingly recognized that marine resources play a vital role in human development. However, there are still some problems such as real-time, accurancy and validity, and many places worth exploring in depth analysis of seabed mineral resources. The main purpose of this paper is to apply image process and filter technology, and then analysis of seabed image clarity, accurate statistical coverage indicators seabed mineral resources, so as to realize forecasting undersea resources distribution in the area. The focus of this paper is to solve the problem of the coverage accuracy of seabed black connected domain by adjusting the brightness equalization algorithm and setting the Setting Region Of(ROI) area and the window Histogram Equalization(HE). In order to achieve the purpose of evaluation of sea area resources, a series of such as color correction, bilater filter, window HE and binarization processing such as image preprocessing algorithm, accurate statistical coverage of seabed mineral resources. In this article, video image processing based on the qt environment, including export processing of video streams and index data, generate clarity evaluation and black pieces connected domain coverage rate curve, can achieve more accurate and stable the indicators of seabed image detection the prediction of the accurate statistics of image coverage of seabed ore is achieved in the paper, which lays a foundation for the exploration of deep learning in the future.
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