Title :
Building Better Formlet Codes for Planar Shape
Author :
Yakubovich, Alex ; Elder, James H.
Author_Institution :
Centre for Vision Res., York Univ., Toronto, ON, Canada
Abstract :
The GRID/formlet representation of planar shape has a number of nice properties [4], [10], [3], but there are also limitations: it is slow to converge for shapes with elongated parts, and it can be sensitive to parameterization as well as grossly ill-conditioned. Here we describe a number of innovations on the GRID/formlet model that address these problems: 1) By generalizing the formlet basis to include oriented deformations we achieve faster convergence for elongated parts. 2) By introducing a modest regularizing term that penalizes the total energy of each deformation we limit redundancy in formlet parameters and improve identifiability of the model. 3) By applying a recent contour remapping method [9] we eliminate problems due to drift of the model parameterization during matching pursuit. These innovations are shown to both speed convergence and to improve performance on a shape completion task.
Keywords :
image matching; image representation; shape recognition; GRID representation; contour remapping method; deformation energy; formlet basis; formlet codes; formlet representation; growth by random iterated diffeomorphisms; matching pursuit; model parameterization; modest regularizing term; oriented deformations; planar shape; shape completion task; Computational modeling; Convergence; Deformable models; Measurement uncertainty; Shape; Shape measurement; Topology; Contour completion; Deformation; Diffeomorphisms; Formlets; GRID; Generative models; Planar shape;
Conference_Titel :
Computer and Robot Vision (CRV), 2014 Canadian Conference on
Conference_Location :
Montreal, QC
Print_ISBN :
978-1-4799-4338-8
DOI :
10.1109/CRV.2014.19