Regular texture removal for video stabilization

In this paper we propose a novel fast fuzzy classifier able to find regular and low distorted near regular texture taking into account the constraints of video stabilization applications. Digital video stabilization allows to acquire video sequences without disturbing jerkiness, removing unwanted camera movements. In presence of regular or near regular texture, video stabilization approaches typically fail. These kind of patterns, due to their periodicity, create multiple matching that degrade motion estimation performances. The proposed classifier has been used as a filtering module in a block based video stabilization approach. Experiments on real sequences with (and without) regular texture confirm the effectiveness of the proposed approach.