1995
@article{ScP1995,
vgclass = {refpap},
vgproject = {cbir},
author = {S. Sclaroff and A. Pentland},
title = {Modal Matching for Correspondence and Recognition},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
month = {June},
year = {1995},
abstract = {Modal matching is a new method for establishing
correspondences and computing canonical descriptions. The method is
based on the idea of describing objects in terms of generalized
symmetries, as defined by each object's eigenmodes. The resulting modal
description is used for object recognition and categorization, where
shape similarities are expressed as the amounts of modal deformation
energy needed to align the two objects. In general, modes provide a
global-to-local ordering of shape deformation and thus allow for
selecting which types of deformations are used in object alignment and
comparison. In contrast to previous techniques, which required
correspondence to be computed with an initial or prototype shape, modal
matching utilizes a new type of finite element formulation that allows
for an object's eigenmodes to be computed directly from available image
information. This improved formulation provides greater generality and
accuracy, and is applicable to data of any dimensionality.
Correspondence results with 2-D contour and point feature data are
shown, and recognition experiments with 2-D images of hand tools and
airplanes are described.},
}