We present a new surface smoothing method. The method enhances sharp variation points of surface normals and, therefore, is good for stable detection of salient surface creases and natural shape segmentation. The method is based on local weighted averaging (diffusion) of surface normals where weights depend nonlinearly on surface curvatures.
Surface creases, ridges and ravines, provide us with important information about the shapes of 3D objects and can be intuitively defined as curves on a surface along which the surface bends sharply. Our mathematical description of the ridges and ravines is based on the study of sharp variation points of the surface normals or equivalently, extrema of the principal curvatures along their curvature lines. We explore similarity between image intensity edges (sharp variation points of an image intensity) and curvature extrema of a 3D surface. It allows us to adopt a basic edge detection technique for detection of the ridges and ravines on range images and smooth surfaces approximated by polygonal meshes. Because the ridges and ravines are of high-order differential nature, careful smoothing is required in order to achieve stable detection of perceptually salient ridges and ravines. To detect the ridges and ravines on a range image we use a nonlinear diffusion process acting on the image intensity surface normals. To detect the ridges and ravines on a triangular mesh we use a coupled nonlinear diffusion of mesh normals and vertices. We demonstrate feasibility of the ridges and ravines for segmentation and shape recognition purposes.
On a smooth generic surface we define ridges to be the local positive maxima of the maximal principal curvature along its associated curvature line and ravines. We investigate relationships between the ridges and ravines, singularities of the caustic generated by the surface normals, and singularities of the distance function from the surface. Stable numerical extraction of the ridges and ravines from range data is achieved via adaptive smoothing that preserves sharp ridges and ravines. We demonstrate applicability of the ridges and ravines for range image segmentation purposes.
We consider deformations of a silhouette while its boundary evolves according to a function of the curvature. The functions assumed to satisfy some general conditions of monotonicity and positiveness. For all such deformations we prove the following qualitative properties: convexity preservation, reduction of the number of the curvature extrema, and finite time disappearing. For some curvature- driven deformations we investigate the limiting shapes of the shrinking parts of the silhouette. A discrete polygon evolution scheme is used to demonstrate our theoretical.
Analyzing the 3D images of a given surface as viewed from different positions naturally leads to investigation of coordinate-independent geometric surface features reflecting its essential properties. In the present paper we study surface point features related to ridge and ravine lines on a surface. These lines introduced in our previous works are defined as curves corresponding to the boundary points of the skeleton of the distance transform of the surface. We show that the ridges and ravines can be extracted via the directional derivatives of the principal curvatures along the associated principal directions. However, even after this local description the direct extraction of the ridges and ravines is a time-consuming procedure. It turns out that the ridge and ravine lines contain some remarkable points (end points and others) that can be extracted relatively easily. After finding such points the procedure of ridge and ravine extraction becomes much simpler. Moreover, these points are closely connected with some singularities of caustics and wavefronts, and have an independent interest in image analysis as visual invariants. The paper is devoted to the investigation of such points and the accompanying geometry of singularities of wavefronts and caustics. We also demonstrate applicability of the ridges and ravines defined as above and related point features in image understanding.