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首页> 外文期刊>Neurocomputing >Maximal similarity based region classification method through local image region descriptors and Bhattacharyya coefficient-based distance: Application to horizon line detection using wide-angle camera
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Maximal similarity based region classification method through local image region descriptors and Bhattacharyya coefficient-based distance: Application to horizon line detection using wide-angle camera

机译：基于局部图像区域描述符和基于Bhattacharyya系数的距离的基于最大相似度的区域分类方法：在广角相机的视线检测中的应用

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摘要

In recent years, many approaches have been proposed to compensate the lack of performance of GNSS (Global Navigation Satellites Systems) occurring when operating in constrained environments. One of these approaches consists in characterizing the environment of reception of GNSS signals using a wide-angle (fisheye) camera oriented to the sky. The content of acquired images is classified into two regions (sky and not-sky) in order to determine LOS (Line-Of-Sight) satellites and NLOS (Nonline-Of-Sight) satellites. This paper is aimed at proposing an image-content classification method to make this approach more effective. The proposed method is composed of four major steps. The first one consists of simplifying the acquired image with an appropriate couple of colorimetric invariant and exponential transform. In the second step, the simplified image is segmented using Statistical Region Merging method. The third step consists of characterizing the segmented regions with a number of local image region descriptors providing more statistically meaningful and discriminatory features. In order to classify the characterized regions into sky and non sky regions, we propose the supervised MSRC (Maximal Similarity Based Region Classification) method by using Bhattacharyya coefficient-based distance. Comparative and extensive experiments have been conducted to investigate the effectiveness of the proposed MSRC method according to the proposed groups of local image region descriptors. Furthermore, we clearly validate the feasibility of MSRC method by comparing its results with those presented in the state of the art. (C) 2017 Elsevier B.V. All rights reserved.
机译：近年来，已经提出了许多方法来补偿在受限环境中运行时发生的GNSS（全球导航卫星系统）性能不足的问题。这些方法之一是使用面向天空的广角（鱼眼镜头）摄像机表征GNSS信号的接收环境。为了确定LOS（视线）卫星和NLOS（非视线）卫星，将获取的图像的内容分为两个区域（天空和非天空）。本文旨在提出一种图像内容分类方法，以使这种方法更加有效。所提出的方法包括四个主要步骤。第一个包括通过适当的比色不变和指数变换来简化采集的图像。第二步，使用统计区域合并方法对简化图像进行分割。第三步包括用多个局部图像区域描述符对分割的区域进行特征化，以提供更具统计意义和区分性的特征。为了将特征区域分为天空区域和非天空区域，我们提出了一种使用基于Bhattacharyya系数的距离进行监督的MSRC（基于最大相似度的区域分类）方法。根据提议的局部图像区域描述符组，已经进行了比较和广泛的实验来研究提议的MSRC方法的有效性。此外，我们通过将MSRC方法的结果与现有技术进行比较，清楚地验证了MSRC方法的可行性。 （C）2017 Elsevier B.V.保留所有权利。

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  • 来源
    《Neurocomputing》 |2017年第22期|28-41|共14页
  • 作者

    El Merabet Y.; Ruichek Y.; Ghaffarian S.; Samir Z.; Boujiha T.; Messoussi R.; Touahni R.; Sbihi A.;

  • 作者单位

    Univ Ibn Tofail, Fac Sci, Dept Phys, Lab LASTID, BP 133, Kenitra 14000, Morocco;

    UTBM, Univ Bourgogne Franche Comte, CNRS, Le2i UMR 6306, F-90010 Belfort, France;

    Univ Abdelmalek Essadi, ENSA, Lab LABTIC, Km 10,BP 1818, Tanger, Morocco;

    Univ Ibn Tofail, Fac Sci, Dept Phys, Lab LASTID, BP 133, Kenitra 14000, Morocco;

    Univ Ibn Tofail, Fac Sci, Dept Phys, Lab LASTID, BP 133, Kenitra 14000, Morocco;

    Univ Ibn Tofail, Fac Sci, Dept Phys, Lab LASTID, BP 133, Kenitra 14000, Morocco;

    Univ Ibn Tofail, Fac Sci, Dept Phys, Lab LASTID, BP 133, Kenitra 14000, Morocco;

    Univ Twente, Fac Geoinformat Sci & Earth Observat ITC, NL-7500 AE Enschede, Netherlands;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    GNSS; Region classification; Image segmentation; Color invariance; Color texture feature; Hybrid descriptor; Maximal similarity;

    机译：GNSS;区域分类;图像分割;颜色不变性;颜色纹理特征;混合描述符;最大相似度;

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