Prediction Intervals for Surface Growing Range Segmentation

The surface growing framework presented by Besl and Jain [2] has served as the basis for many range segmentation techniques. It has been augmented with alternative fitting techniques [17], model selection criteria [11, 15], and solid modelling components [6]. All of these surface growing approaches, however, require global thresholds. Range scenes typically cannotsatisfy the globalthreshold assumption since it requires data noise characteristics to be constant throughout the scene. Furthermore, these approaches can only be applied to range scenes where large seed regions can be isolated. As scene complexity increases, the number of surfaces, discontinuities, and outliers increase, hindering the identification of large seed regions. We present statistical criteria based on multivariate regression to replace the traditional decision criteria used in surface growing. We use local estimates and their uncertainties to construct criteria which capture the uncertainty associated with extra...

• Type: Conference
• Year: 1997
• Where: CVPR
• Authors: James V. Miller, Charles V. Stewart
• Keywords: Computer Vision, CVPR 1997, Global Thresholds, Large Seed Regions, Seed Regions