A new benchmark for shape correspondence evaluation.
This paper introduces a new benchmark study of evaluating 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 specified ground-truth statistical shape model. We then run the test shape-correspondence algorithms on these synthetic shape instances to construct a new statistical shape model. We finally introduce a new measure to describe the difference between this newly constructed statistical shape model and the ground truth. This new measure is then used to evaluate the performance of the test shape-correspondence algorithm. By introducing the ground-truth statistical shape model, we believe the proposed benchmark allows for a more objective evaluation of the shape correspondence than those that do not specify any ground truth.
Munsell
Brent C
BC
Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, USA. munsell@engr.sc.edu
Dalal
Pahal
P
Wang
Song
S
D000465:Algorithms; D019985:Benchmarking; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D007089:Image Enhancement / Q000379:methods* / Q000592:standards; D007090:Image Interpretation, Computer-Assisted / Q000379:methods* / Q000592:standards; D008954:Models, Biological; D015233:Models, Statistical; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity