Abstract:

In the real image， when the target object is occluded by other objects or environmental elements， the noise near the occluded area will confuse the contour features of the background and the target object. It is particularly difficult to infer the contour of the occluded area with continuity and smoothness by analyzing only the contour features of the visible part. This results in a deviation between the extracted foreground target contour and the original target boundary. Therefore， a full-contour extraction algorithm for local occluding object in real image is proposed under the influence of background noise. The background model of the real image is constructed by the mixed Gaussian background difference method， which is used to distinguish the background image and the foreground target image in the real image， remove the background information， and obtain the foreground target image. Multi-resolution method is combined with ACM and GVF field is introduced to extract the contour of the foreground target image. The double-arc interpolation algorithm is used to obtain the full contour of the foreground target continuously and smoothly through smooth contour repair and corner contour repair， aiming at the blocked area in the foreground target contour. The repaired contour is visually consistent with the original contour， and the difference between the boundary and the original target is minimized. The experimental results show that the misjudgment rate for non-background elements is always below 0.1%； The contour curves generated in the process of contour extraction are highly consistent with the target boundary， and the contour lines are smooth and continuous. The contour restoration results are extremely natural and realistic， with an error value of only 0.02%.

Key words: background noise, realistic images, partial occlusion, double arc interpolation, full contour extraction, contour repair