A systematic literature review on hardware implementation of image processing

Abstract

Image processing has become under the spotlight recently and leads to a significant shift in various fields such as biomedical, satellite images, and graphical applications. Nevertheless, the poor quality of an image is one of the noticeable limitations of image processing as it restricts efficient data extraction to be conducted. Conventionally, the image was processed via software applications such as MATLAB. In spite of the software's ability to cater to the data extraction of low-quality image issues, it still suffers from the time-consuming issue. As the ability to obtain a rapid outcome is a favorable feature of efficient image processing, the use of hardware in image processing is deemed to keep the addressed issue at bay. Thus, the image enhancement techniques using hardware have gradually rising interest among researchers with numerous approaches such as field programmable gate array (FPGA). In this study, 25 different research papers published from 2016 to 2021 are studied and analyzed to focus on the performance of FPGA as hardware implementation in image processing techniques.

Implementing hue-saturation-value filter and circle hough transform for object tracking on ball-wheeled robot

“…The field of view refers to the area that the camera can capture, and it is influenced by various factors such as the camera's focal length, sensor size, and resolution. In this design stage, the camera capture on the robot is modeled and parameters used for object tracking using HSV colour filtering are specified [25], [26]. Figure 3 illustrates the preprocessing steps for object detection, which include blurring, red-green-blue (RGB) to HSV conversion, binarization, and morphology.…”