Generalized tube model: recognizing 3D elongated objects from 2D intensity images

This research addresses the issue of recognizing 3D elongated objects from 2D intensity images. A tube model, locally similar to generalized cones, is developed for the class of elongated objects. A recognition strategy that combines 2D contour properties and surface shading information is used to eeectively exploit the power of the 3D model. Reliable contours provide constraints for localizing the objects of interest. The theory of optimal lters is adopted in verifying the shading of hypothesized objects. Object recognition is achieved through optimizing the Signal-to-Noise response with respect to model parameters. A sweeping operation is proposed as a further stage of identifying objects so that the overall performance of the system does not heavily rely on the quality of local feature detection. A smooth sweeping is controlled by a set of adaptive constraints computed dynamically from an on-line sweeping history. We apply the proposed approach to real world data from diierent application domains.