Piet W. Verbeek

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In artificial intelligence, a number of search algorithms is available for finding shortest paths in graphs. The uniform cost algorithm is a special case of one of those algorithms, the A*-algorithm. In the uniform cost algorithm, nodes are expanded in order of increasing cost. We have developed an efficient version of this algorithm for inManuscript(More)
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)
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)
In this paper we develop four measures to describe the distribution of nuclear chromatin. These measures attempt to describe in an objective and meaningful way the heterogeneity, granularity, condensation, and margination of chromatin in cell nuclei. Starting with a high-resolution digitized image of a cell where the nuclear pixels have been identified, the(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)
We measure the sharpness of natural (complex) images using Gaussian models. We first locate lines and edges in the image. We apply Gaussian derivatives at different scales to the lines and edges. This yields a response function, to which we can fit the response function of model lines and edges. We can thus estimate the width and amplitude of the line or(More)