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In this paper, we present a novel method to estimate curvature of iso gray-level surfaces in gray-value images. Our method succeeds where standard isophote curvature estimation methods fail. There is neither a segmentation of the surface needed nor a parametric model assumed. Our estimator works on the orientation field of the surface. This orientation(More)
An algorithm is proposed for skeletonization of 3-D images. The criterion to preserve connectivity is given in two versions: global and local. The latter allows local decisions in the erosion process. A table of the decisions for all possible configurations is given in this paper. The algorithm using this table can be directly implemented both on general(More)
In this paper we describe a new strategy for combining orientation adaptive filtering and edge preserving filtering. The filter adapts to the local orientation and avoids filtering across borders. The local orientation for steering the filter will be estimated in a fixed sized window which never contains two orientation fields. This can be achieved using(More)
Filtering of an image with rotated versions of an orientation selective filter yields a set of images which can be stacked to form an orientation space. Orientation space provides a means of analyzing overlapping and touching patterns, characterized by their orientation. In this paper we extend previous work and show that curved patterns may also be(More)
abstract In this paper we present a new method for estimating confidence and curvature of 3-D curvilinear structures. The gradient structure tensor (GST) models shift-invariance. The eigenstructure of the tensor allows estimation of local dimensionality, orientation, and the corresponding confidence value. Local rotational invariance, which occurs often in(More)
A systematic framework is given that accommodates existing max-min filter methods and suggests new ones. Putting the upper and lower envelopes UPP = MIN(MAX) and LOW = MAX(MIN) in the roles that MAX, MIN or original play in existing filters we can distinguish edges in ramp edges and texture (or noise) edges; all methods presented come in three versions: for(More)