移动健康环境中硬件设计、细化和分割算法的性能评估

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引用次数: 0

摘要

在本章中,作者描述了通过在基于云物联网的移动健康环境中应用重新采样、分割、滤波和细化算法,将原始静脉图像转换为稀释静脉图像所需的实验分析步骤。在静脉模式和周围环境之间做出区分有点困难,特别是在静脉图像不清晰和细的情况下。然而,在基于云物联网的移动健康环境中应用采样、分割、中值滤波和细化算法后,获得了单线的高质量静脉图像模式。
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Performance Evaluation of Hardware Designs, Thinning, and Segmentation Algorithms in M-Health Environments
In this chapter, the authors have described the experimental analysis steps required for converting original veins images into thinned veins images by applying resample, segmentation, filtering, and thinning algorithms in the cloud IoT-based m-health environments. It is a little bit difficult to make a distinction between the vein pattern and the surroundings particularly in the cases of unclear and thin veins images. However, after applying the resample, segmentation, median filters, and thinning algorithms in the cloud IoT-based m-health environment, the superior quality veins image patterns of a single line are obtained.
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Approaches for M-Health Environment Results and Discussions of Palm-Dorsa-Veins-Based Systems in the Cloud IoT-Based M-Health Environment The Panoramic Views of Cloud IoT-Based M-Health Biometrics A Glimpse of Hardware Design Approaches Future Generation Computing in M-Health
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