Edge detection has a wide range of applications, including object recognition, image segmentation, and scene analysis. It is crucial in autonomous vehicles, robotics, medical imaging, surveillance systems, and various industrial automation processes. Traditional edge detection methods, like the Sobel and Canny operators, have been widely employed due to their simplicity and effectiveness. The computation of gradients and thresholds in conventional edge detectors requires extensive processing, resulting in high computational costs. The proposed VLSI implementation of the Sobel edge detectors utilizes hybrid accumulations to address the limitations of traditional methods. By using hybrid accumulations, the hardware resource requirements are optimized, making it feasible to implement the edge detectors on resource-constrained platforms.

Image Processing,MATLAB, Pixel Analysis, Image Recognition, Sub-window, Very Large-Scale Integration.