Research on temperature image archive segmentation based on the combination of Canny and Bernsen algorithm

In order to extract target information from massive temperature self-recording paper archive images, improve the utilization rate of self-recording paper archives, and solve the problem of poor continuity of target edge feature points and difficulty in forming closed areas after image segmentation using traditional Canny operators, the Bernsen algorithm addresses the problems of noise or uneven lighting in the image, as well as the loss of target image details caused by manually recorded handwriting, pencil scratches, and ink pollution marks in self-recording paper archive images. Based on the characteristics of self-recording paper archive images, this paper proposes a method that combines Gaussian filtering Canny operator and Bernsen algorithm to segment the distribution of feature images, achieving effective segmentation of temperature self-recording paper archives. The experimental results show that the method proposed in the paper can accurately achieve target segmentation of coordinates and temperature curves in temperature self-recording paper archive images, avoiding the impact of edge features such as handwriting, pencil scratches, and ink contamination marks on the image, and achieving good segmentation results.