The Padding of Vein Image Features and Hardware Designs in M-Health Environments

The Padding of Vein Image Features and Hardware Designs in M-Health Environments

DOI: 10.4018/978-1-7998-4537-9.ch005

Abstract

This chapter describes the timing diagrams of padding features and hardware designs of segmentation, controllers, and filters. Further, the authors have described that the hardware design concept of segmentation task can be performed online in a distributed cloud computing m-health environment. The segmentation phase uses two Gaussian filter functions with different sizes of filter masks and standard deviation with a threshold value to make a distinction between veins image patterns and the corresponding backgrounds in the cloud IoT-based m-health environment. In order to design the hardware architecture of the median filter, the superior moving window architecture is used by researchers to accommodate a larger size median filter in the cloud IoT-based m-health environment.
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Timing For The Padding Features For The Vertical Gaussians In Different Models

In the mode zero of the 3x3 and 31x31 Vertical Gaussian processes, the chosen veins’ image input data from re-sampling are kept in MEM_PAD for using padding feature mode1 and mode2. The chosen veins’ image input data are the thirty (pixels from the starting and thirty pixels from last in a column of the veins’ image as presented in figure 1. Figure 2 represents the timing diagram of writing to MEM_PAD in mode zero (mode0) (Mukherjee et al., 2015; Al Ghozali et al., 2016; Lin et al., 2008) .

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