Modal matching for correspondence and recognition

Citations Over TimeTop 1% of 1995 papers

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 2D contour and point feature data are shown, and recognition experiments with 2D images of hand tools and airplanes are described.>

Related Papers

• → Is there a well-founded solution to the generality problem?(2014)13 cited
• → Generality of treatment effects with parents as therapists: A review of assessment and implementation procedures(1977)160 cited
• → How to Make the Generality Problem Work for You(2009)7 cited
• → Contextual Learning: Towards Using Contexts to Achieve Generality(2000)
• → Exploring the Generality of Norms in Multi-Agent Systems(2023)