Isometric Deformation Modelling for Object Recognition

Authors:
Dirk Smeets;Thomas Fabry;Jeroen Hermans;Dirk Vandermeulen;Paul Suetens
Affiliations:
K.U. Leuven,Faculty of Engineering, Department of Electrical Engineering, Center for Processing Speech and Images, Medical Imaging Research Center, Universitair Ziekenhuis Gasthuisberg, Leuven, Be ...;K.U. Leuven,Faculty of Engineering, Department of Electrical Engineering, Center for Processing Speech and Images, Medical Imaging Research Center, Universitair Ziekenhuis Gasthuisberg, Leuven, Be ...;K.U. Leuven,Faculty of Engineering, Department of Electrical Engineering, Center for Processing Speech and Images, Medical Imaging Research Center, Universitair Ziekenhuis Gasthuisberg, Leuven, Be ...;K.U. Leuven,Faculty of Engineering, Department of Electrical Engineering, Center for Processing Speech and Images, Medical Imaging Research Center, Universitair Ziekenhuis Gasthuisberg, Leuven, Be ...;K.U. Leuven,Faculty of Engineering, Department of Electrical Engineering, Center for Processing Speech and Images, Medical Imaging Research Center, Universitair Ziekenhuis Gasthuisberg, Leuven, Be ...
Venue:
CAIP '09 Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns
Year:
2009

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Abstract

We present two methods for isometrically deformable object recognition. The methods are built upon the use of geodesic distance matrices (GDM) as an object representation. The first method compares these matrices by using histogram comparisons. The second method is a modal approach. The largest singular values or eigenvalues appear to be an excellent shape descriptor, based on the comparison with other methods also using the isometric deformation model and a general baseline algorithm. The methods are validated using the TOSCA database of non-rigid objects and a rank 1 recognition rate of 100% is reported for the modal representation method using the 50 largest eigenvalues. This is clearly higher than other methods using an isometric deformation model.