We present a novel variational and statistical approach for shape registration. Shapes of interest are implicitly embedded in a higher dimensional space of distance transforms. In...
Xiaolei Huang, Nikos Paragios, Dimitris N. Metaxas
This paper presents a new deformable modeling strategy aimed at integrating shape and appearance in a unified space. If we think traditional deformable models as "active cont...
Computer-aided diagnosis is often based on comparing a structure of interest with prior models. Such a comparison requires automatic techniques in determining prior models from a s...
Xiaolei Huang, Nikos Paragios, Dimitris N. Metaxas
We present a new class of deformable models, MetaMorphs, whose formulation integrates both shape and interior texture. The model deformations are derived from both boundary and re...
We present a novel framework for learning a joint shape and appearance model from a large set of un-labelled training examples in arbitrary positions and orientations. The shape an...