Effectiveness of edge detection of color images

Abstract

Digital image processing is an ever-expanding and dynamic field, and its applications have involved our daily lives, such as medicine, space exploration, surveillance, identity verification, automatic industrial inspection, and intelligent transportation systems, self-driving cars and self-driving cars, Guided weapons, etc. Applications such as these involve different processes, such as image enhancement and object detection. The edge detection process can simplify image analysis by greatly reducing the amount of data to be processed, while retaining useful structural information about object boundaries. Efficient and accurate edge detection will ensure the performance of the further image processing stage. Edge detection uses different methods, such as Sobel, Prewitt, Canny and other operators. Edge detection based on Sobel operator has been widely used in edge detection, becoming a real-time gray image solution. Nowadays, color image processing has attracted more and more attention, so more research has been done in this field, because edge detection in color images is more complicated than edge detection in gray images. In this project, Very-high-speed integrated circuit Hardware Description Language (VHDL) and Python language are used to study the Sobel edge detection technology for color image segmentation. VHDL is a general-purpose parallel programming language used to describe digital and mixed-signal systems and used in real-time embedded system automation for documentation, simulation, verification, and synthesis. On the other hand, Python provides highly optimized libraries from a large developer community, and it is used in all fields from scientific computing to image processing and machine learning. Therefore, this research focuses on the effectiveness of color image edge detection based on Sobel operator using these two methods.