A comparative study on multivariate mathematical morphology
A comparative study on multivariate mathematical morphology
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DOI：
10.1016/j.patcog.2007.02.004
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发表时间：
2007-11-01
影响因子：
8
通讯作者：
Lefevre, S.
中科院分区：
文献类型：
--
作者：
Aptoula, E.;Lefevre, S.
单变量形态算子在多个领域的成功应用，沿着处理过多可用多值图像的需求不断增加，是将数学形态学框架扩展到多变量数据的主要动机。这个扩展的几个理论要求，主要包括一个排名方案以及向量数据的极值算子，导致了许多具有不同属性的建议。然而，它们中没有一个被广泛接受。此外，在目前的文献中的比较研究工作，评估从这些方法中获得的结果，要么是过时的或仅限于一个特定的应用领域。在本文中，提出了一个全面的审查多元形态框架。特别是，他们主要是在其数据排序方法方面进行审查。此外，一个简短的系列说明性的面向应用的测试选定的矢量排序的颜色和多光谱遥感数据的结果也进行了讨论。(c)2007年由Elsevier Ltd代表模式识别协会出版。
The successful application of univariate morphological operators on several domains, along with the increasing need for processing the plethora of available multivalued images, have been the main motives behind the efforts concentrated on extending the mathematical morphology framework to multivariate data. The few theoretical requirements of this extension, consisting primarily of a ranking scheme as well as extrema operators for vectorial data, have led to numerous suggestions with diverse properties. However, none of them has yet been widely accepted. Furthermore, the comparison research work in the current literature, evaluating the results obtained from these approaches, is either outdated or limited to a particular application domain. In this paper, a comprehensive review of the proposed multivariate morphological frameworks is provided. In particular, they are examined mainly with respect to their data ordering methodologies. Additionally, the results of a brief series of illustrative application oriented tests of selected vector orderings on colour and multispectral remote sensing data are also discussed. (c) 2007 Published by Elsevier Ltd on behalf of Pattern Recognition Society.