Kernel methods offer a flexible toolbox for pattern analysis and machine learning. A general class of kernel functions which incorporates known pattern invariances are invariant d...
Abstract. By combining algorithmic learning, decision procedures, predicate abstraction, and simple templates, we present an automated technique for finding quantified loop invaria...
In this paper, invariant sonar texture characterization for seabed classification is addressed from the spatial distribution of image keypoints using log-Gaussian Cox processes. ...
We present here an approach for applying the technique of modeling data transformation manifolds for invariant learning with kernel methods. The approach is based on building a ke...
This paper presents robust click-point linking: a novel localized registration framework that allows users to interactively prescribe where the accuracy has to be high. By emphasi...