A new boundary detection approach for shape modeling is presented. It detects the global minimum of an active contour model’s energy between two end points. Initialization is made easier and the curve is not trapped at a local minimum by spurious edges. We modify the “snake” energy by including the internal regularization term in the external potential term. Our method is based on finding a path of minimal length in a Riemannian metric. We then make use of a new efficient numerical method to find this shortest path. It is shown that the proposed energy, though based only on a potential integrated along the curve, imposes a regularization effect like snakes. We explore the relation between the maximum curvature along the resulting contour and the potential generated from the image. The method is capable to close contours, given only one point on the objects’ boundary by using a topology-based saddle search routine. We show examples of our method applied to real aerial and medical images.

The central component in the RADIUS modelsupported image exploitation paradigm, is the creation of a 3-dimensional model that captures the basic geometry of the site under examination. While many fully automated methods for site model construction show promising results, none are robust or general enough to replace interactive and semiautomatic techniques. In this paper, we outline the suite of site-model construction tools being assembled for the RADIUS program, a sample scenario for their use, and the results of a study to characterize the potential saving of effort they offer. 1

Even though methods based on the use of deformable models have become prevalent, the quality of their output depends critically on the model's initial state. The issue of initializing such models, however, has not received much attention even though it is often key to the implementation of a truly useful system. We therefore present a new approach to segmentation of 3-Dimensional shapes that initializes and then optimizes a 3-D surface model given only the data and a very small number of 3-D seed points and corresponding surface normals. This is a valuable capability for medical, robotic and cartographic applications where such seed points can be naturally supplied. In effect, the surface model is clamped onto the object boundary in manner reminiscent of a Velcro being closed. We develop the method's mathematic framework and show results using volumetric medical data.

Model-Based Optimization (MBO) is a paradigm in which an objective function is used to express both geometric and photometric constraints on features of interest. A parametric model of a feature, such as a road, a building, a river or the underlying terrain, is extracted from one or more images by automatically adjusting the model's state variables until a minimum value of the objective function is obtained. The optimization procedure yields a description that simultaneously satis es (or nearly satis es) all constraints, and, as a result, is likely to be a good model of the feature. Using this approach, a rough initial sketch of a 3-D object can automatically be re ned, resulting in an accurate model. The paper presents experimental results on road delineation, showing the speedup obtained when compared to totally manual road delineation. Furthermore, because objects are all modeled in the same fashion, we can re ne the models simultaneously and enforce geometric and semantic constraints between objects, thus increasing

We present a new approach to segmentation of 3Dimensional shapes that initializes and then optimizes a 3-D surface model given only the data and a very small number of 3-D seed points and corresponding surface normals. This is a valuable capability for medical, robotic and cartographic applications where such seed points can be naturally supplied. In effect, the surface model is clamped onto the object boundary in manner reminiscent of a Velcro being closed. We develop the method's mathematic framework and show preliminary results using volumetric medical data.