A stayed cable inspection system was developed which consists of robot, host computer, cameras and image acquisition system. The robot was driven with single motor and could climb cables of various and variable diameters. Pictures of the cables’ were taken by the robot, and the defects and mars were identified automatically with image recognition. The steps of image recognition includes image de-noising, image enhancement, image segmentation, feature extraction, and recognition with the features of the images’ histogram grayscale distributions and energy distributions.
In this chapter, the authors have described the methodologies to achieve the objectives of veins image enhancement, feature extractions, and matching with other veins images in the cloud IoT-based m-health environment. The initial steps to propose the algorithms for veins image enhance and feature extractions will have five parts. Once the proposed algorithm is written, the hardware architecture designs of the proposed veins image enhancements and feature extraction algorithm will be described by the authors. The hardware designs are presented in subsequent sections of this chapter. Further, the hardware designs are elaborated in detail for each of the techniques. The presented algorithms are implemented in MATLAB 11.0 software, and these algorithms are simulated and integrated with different veins sample images. The hardware designs of veins image enhancements and feature extractions are implemented using Verilog Hardware Language Description (VHLD), and these implemented results are simulated using MSA (Model-Sim-Altera) for sample images of different types of veins.
Image Enhancement and Feature Extraction Based on Low-Resolution Satellite Data
