Issue No. 11 - November (2008 vol. 30)
Brent C. Munsell , University of South Carolina, Columbia
Pahal Dalal , University of South Carolina, Columbia
Song Wang , University of South Carolina, Columbia
This paper introduces a new benchmark study to evaluate the performance of landmark-based shape correspondence used for statistical shape analysis. Different from previous shape-correspondence evaluation methods, the proposed benchmark first generates a large set of synthetic shape instances by randomly sampling a given statistical shape model that defines a ground-truth shape space. We then run a test shape-correspondence algorithm on these synthetic shape instances to identify a set of corresponded landmarks. According to the identified corresponded landmarks, we construct a new statistical shape model, which defines a new shape space. We finally compare this new shape space against the ground-truth shape space to determine the performance of the test shape-correspondence algorithm. In this paper, we introduce three new performance measures that are landmark independent to quantify the difference between the ground-truth and the newly derived shape spaces. By introducing a ground-truth shape space that is defined by a statistical shape model and three new landmark-independent performance measures, we believe the proposed benchmark allows for a more objective evaluation of shape correspondence than previous methods. In this paper, we focus on developing the proposed benchmark for $2$D shape correspondence. However it can be easily extended to $3$D cases.
Statistical shape analysis, shape correspondence, point distribution model, benchmark study
S. Wang, P. Dalal and B. C. Munsell, "Evaluating Shape Correspondence for Statistical Shape Analysis: A Benchmark Study," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 30, no. , pp. 2023-2039, 2007.