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Iris Segmentation based on Fuzzy Mathematical Morphology, Neural Networks and Ontologies

机译:基于模糊数学形态学,神经网络和本体的虹膜分割

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Segmentation is one of the most time-consuming steps within the whole process of Iris Recognition. By means of Fuzzy Mathematical Morphology and Neural Networks, this new algorithm can fulfill the task of isolating the Iris, not only with an acceptable accuracy, but also with a very high improvement in terms of time. Furthermore, this innovative scheme presents an ontology able to decide whether the features can be extracted, based on previous segmentation. This paper provides a detailed explanation of both the problem to be solved and how this new approach meets the required goals. Current Iris Recognition algorithms may benefit from this new approach, and what is more, the essence of the algorithm can be extended to other biometric segmentation procedures.
机译:分割是虹膜识别过程中最耗时的步骤之一。通过模糊数学形态和神经网络,这种新算法可以满足隔离虹膜的任务,不仅具有可接受的准确性,而且还具有很高的时间改善。此外,这种创新方案提供了一个能够基于先前的分段来决定是否可以提取特征的本体。本文提供了对要解决的问题的详细说明以及这种新方法如何符合所需目标。目前的虹膜识别算法可能会受益于这种新方法,并且更重要的是,算法的本质可以扩展到其他生物识别分段过程。

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