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引用次数: 0

摘要

众所周知,当相机或其他成像系统捕获图像时,通常,被捕获的视觉系统不能直接实现它。这一事实背后可能有几个原因,例如图像中可能存在随机强度变化。图像中也可能存在光照变化或对比度差。这些缺点必须在原始阶段解决,以获得最佳的视觉处理。本章将讨论用于此目的的不同过滤方法。本章从高斯滤波器开始,然后简要回顾不同的常用方法。此外,本章还将介绍不同的过滤方法,包括它们的硬件架构。
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Approaches for M-Health Environment
It is a well-known fact that when a camera or other imaging system captures an image, often, the vision system for which it is captured cannot implement it directly. There may be several reasons behind this fact such as there can exist random intensity variation in the image. There can also be illumination variation in the image or poor contrast. These drawbacks must be tackled at the primitive stages for optimum vision processing. This chapter will discuss different filtering approaches for this purpose. The chapter begins with the Gaussian filter, followed by a brief review of different often used approaches. Moreover, this chapter will also render different filtering approaches including their hardware architectures.
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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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