A high speed roller dung beetles clustering algorithm and its architecture for real-time image segmentation

Design and Implementation of Local Threshold Segmentation Based on FPGA

In the process of the development of image processing technology, image segmentation is a very important image processing technology in the field of machine vision, pedestrian detection, medical imaging, and so on. However, the traditional image segmentation technology cannot solve the problems of reflection and uneven illumination. This paper presents a local threshold segmentation method based on FPGA, which can automatically select the optimal threshold according to different gray levels of images. First, the image is processed by mean filtering to remove noise interference in the image. Then, the idea of the mean value of the local neighborhood block and the Gaussian weighted sum in the local neighborhood is used to deal with the reflective and uneven light on the image. The process is designed and realized on FPGA. Finally, the design algorithm is verified by ModelSim simulation software and QT5 software. The experimental results show that the algorithm can effectively solve the problems of reflection and uneven illumination on the image surface, and the segmentation effect is significantly improved compared with the fixed threshold algorithm and Otsu algorithm. It also has certain reference value in medicine, agriculture, engineering, and other fields.