Polynomial expansion for range image segmentation and classification of the environment

In this paper we introduce a method that utilizes a high-order polynomial expansion of range imagery for the purposes of image segmentation and classification. The use of polynomial expansion has been quite successful in segmenting and estimating optical flow in 2D imagery, but has not been used extensively in 3D or range imagery. We derive features using the coefficients of the high-order polynomial expansion and use those features for local and global segmentation of the range image. Finally, we classify the segments based on the features within each segment. Promising results are shown on range images from the Odetic lidar database.