belief propagation in computer vision

Belief propagation is a computational method used in computer vision to interpret complex images. It works by passing information, or "messages," between different parts of an image—such as pixels or regions—to estimate the most likely features or labels (like objects or textures). Each part updates its beliefs based on neighboring parts' information, gradually refining the overall understanding of the image. This iterative process helps in tasks like image segmentation or depth estimation, enabling the system to make consistent, informed decisions about the visual data.