Modeling Shapes with Higher-Order Graphs: Methodology and Applications

Chaohui Wang (1) , Yun Zeng (2) , Dimitris Samaras (2) , Nikos Paragios (3, 4, 5)

1 UCLA - Computer Science Department [Los Angeles]
2 SBU - Stony Brook University [SUNY]
3 CVN - Centre de vision numérique
4 GALEN - Organ Modeling through Extraction, Representation and Understanding of Medical Image Content
5 LIGM - Laboratoire d'Informatique Gaspard-Monge

Extrinsic factors such as object pose and camera parameters are a main source of shape variability and pose an obstacle to efficiently solving shape matching and inference. Most existing methods address the influence of extrinsic factors by decomposing the transformation of the source shape (model) into two parts: one corresponding to the extrinsic factors and the other accounting for intra-class variability and noise, which are solved in a successive or alternating manner. In this chapter, we consider a methodology to circumvent the influence of extrinsic factors by exploiting shape properties that are invariant to them. Based on higher-order graph-based models, we implement such a methodology to address various important vision problems, such as non-rigid 3D surface matching and knowledge-based 3D segmentation, in a one-shot optimization scheme. Experimental results demonstrate the superior performance and potential of this type of approach.

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]

Chaohui Wang, Yun Zeng, Dimitris Samaras, Nikos Paragios. Modeling Shapes with Higher-Order Graphs: Methodology and Applications. Shape Perception in Human and Computer Vision: An Interdisciplinary Perspective, Sven J. Dickinson and Zygmunt Pizlo, pp.459-471, 2013. ⟨hal-00858417⟩