Research on Adaptive Threshold Segmentation Technology in Machine Vision

In order to enhance the robustness of image segmentation under complex background and non-uniform illumination conditions, this paper conducts in-depth research on the adaptive threshold segmentation technology in machine vision. The study uses the adaptiveThreshold () function in the OpenCV library. Based on the PASCAL VOC 2012 image dataset, a complete algorithm flow including grayscale, median filtering, local threshold calculation and binarization is designed. The Gaussian weighted model is introduced to dynamically obtain the local threshold of each pixel. The experimental results show that this method performs extremely well in multiple dimensions such as image white pixel ratio, PSNR and edge retention rate. Compared with the traditional Otsu algorithm, the average accuracy rate is improved by more than 7%, and it also shows good performance in retaining edge structures. The adaptive threshold segmentation technology shows extremely high adaptability and stability in various images, which can provide more accurate image segmentation support for intelligent manufacturing, medical image analysis and other fields.