Using diffusion distances for flexible molecular shape comparison

Shape distances, shape spaces and the comparison of morphometric methods.

On the use of Gromov-Hausdorff Distances for Shape Comparison

It is the purpose of this paper to propose and discuss certain modifications of the ideas concerning GromovHausdorff distances in order to tackle the problems of shape matching and comparison. These reformulations render these distances more amenable to practical computations without sacrificing theoretical underpinnings. A second goal of this paper is to establish links to several other practi...

Molecular shape and effective diffusion radius.

tion. If the researcher offers to assist the Eye Bank in tissue procurement, a partnership is beginning. This might include going with an eye banker to present inservice programs to a group of doctors, nurses, hospital administrators, or others who are close to the supply of tissue. It may be appearing with the eye banker on a radio talk show to explain the need of eye donations. It may be that...

Shape Classification Using a Flexible Graph Kernel

The medial axis being an homotopic transformation, the skeleton of a 2D shape corresponds to a planar graph having one face for each hole of the shape and one node for each junction or extremity of the branches. This graph is non simple since it can be composed of loops and multiple-edges. Within the shape comparison framework, such a graph is usually transformed into a simpler structure such a...

Learning a discriminative classifier using shape context distances

For purpose of object recognition, we learn one discriminative classifier based on one prototype, using shape context distances as the feature vector. From multiple prototypes, the outputs of the classifiers are combined using the method called “error correcting output codes”. The overall classifier is tested on benchmark dataset and is shown to outperform existing methods with far fewer protot...