CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2013 vol.35 Issue No.09 - Sept.
Issue No.09 - Sept. (2013 vol.35)
E. Konukoglu , Med. Sch., Athinoula A. Martinos Center for Biomed. Imaging, Harvard Univ., Cambridge, MA, USA
B. Glocker , Microsoft Res. Cambridge, Cambridge, UK
A. Criminisi , Microsoft Res. Cambridge, Cambridge, UK
K. M. Pohl , Univ. of Pennsylvania, Philadelphia, PA, USA
This paper presents a new distance for measuring shape dissimilarity between objects. Recent publications introduced the use of eigenvalues of the Laplace operator as compact shape descriptors. Here, we revisit the eigenvalues to define a proper distance, called Weighted Spectral Distance (WESD), for quantifying shape dissimilarity. The definition of WESD is derived through analyzing the heat trace. This analysis provides the proposed distance with an intuitive meaning and mathematically links it to the intrinsic geometry of objects. We analyze the resulting distance definition, present and prove its important theoretical properties. Some of these properties include: 1) WESD is defined over the entire sequence of eigenvalues yet it is guaranteed to converge, 2) it is a pseudometric, 3) it is accurately approximated with a finite number of eigenvalues, and 4) it can be mapped to the ([0,1)) interval. Last, experiments conducted on synthetic and real objects are presented. These experiments highlight the practical benefits of WESD for applications in vision and medical image analysis.
Shape, Eigenvalues and eigenfunctions, Geometry, Laplace equations, Heating, Equations, Global Positioning System,medical images, Shape distance, spectral distance, Laplace operator, Laplace spectrum, segmentations, label maps
E. Konukoglu, B. Glocker, A. Criminisi, K. M. Pohl, "WESD--Weighted Spectral Distance for Measuring Shape Dissimilarity", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.35, no. 9, pp. 2284-2297, Sept. 2013, doi:10.1109/TPAMI.2012.275