Stable Symmetry Feature Detection and Classification in Panoramic Robot Vision Systems

We propose a novel approach to detect sparse and stable image features by symmetric properties extracted from the visual data. The regional features are formed by a fast qualitative symmetry operator in combination with quantitative symmetry range information. We apply a simple color histogram descriptor to match pre-selected features to those features acquired by our omnidirectional vision system at run time. The complete algorithm produces regional symmetry-based features that are sparse and highly robust to scale change and panoramic image warp, in particular. We present the algorithms of symmetry and feature processing and show their application in an object classification experiment using our platform, the Bremen autonomous wheelchair "Holland III"