An integrated approach for segmentation and representation of range images

This paper presents an integrated approach for segmentation and representation of objects in range images. The segmentation is based on the association of edge detection with clustering techniques, and it produces a set of labeled regions plus the coefficients of the plane fitted to each region. With this information, the reconstruction error for the whole image is estimated, and the initial number of regions supplied to the clustering algorithm can be updated, based on this error. Once the image reconstruction error is smaller than the desired, the next step is to create a polyhedral representation to the surfaces of the image. For each segmented region, the representation process yields its 3D vertices' coordinates, ordered to form a polygon. The final representation is robust enough to guarantee that there are no cracks on the reconstructed surfaces. The main contributions of this work are: (1) A new approach for determining the optimal number of image regions; (2) A suitable representation that can be applied to both polyhedral and non-polyhedral objects.