Search

The desired solution to many labelling problems in computer vision is a spatially smooth result where label changes are aligned with the edges of the guidance image. It can be obtained traditionally by smoothing the label costs using edge‐aware filters. However, local filters incorporate the information in a local support region to obtain locally‐optimal and non‐local tree‐based filters, which often overuse piece‐wise constant assumptions. In this paper, we propose a spatial‐tree filter for cost aggregation. The tree model incorporates the spatial affinity into the tree structure. The tree distance between two pixels on our spatial tree is an approximated geodesic distance, which acts as a pixel similarity metric. The filtering process was implemented by recursively techniques in four directions: Top‐to‐bottom, left‐to‐right, and vice‐versa. Thus, the complexity of our approach is linear to the number of image pixels. Extensive experiments demonstrate the effectiveness and efficiency of our spatial‐tree filter in image smoothing and stereo matching. Our method performs better than the existing tree‐based non‐local method in cost aggregation. [ABSTRACT FROM AUTHOR]