Efficient One-to-One Pair Matching for 2-D and 3-D Edge Detection Evaluation

This paper introduces a novel efficient method of obtaining one to one correspondence matching for fast, accurate, performance evaluation of edge detectors. The proposed Efficient Pairing Strategy (EPS) overcomes the computational cost limitations of the Hungarian algorithm, enabling a fast and accurate evaluation of 3-D data and large 2-D data sets. In this work, the accuracy of the EPS method is measured against the optimal Hungarian method across a data set of 124240 images, and is shown to produce accurate results with a Pearson Pairwise Correlation coefficient of 0.99 . Additionally the efficiency of the EPS method is compared against the fast Closest Distance Match (CDM), the Cost Scaling Assignment (CSA), and the commonly applied Pratt figure of Merit (PFOM) methods. Analysis shows the EPS and CSA methods both produce cost scaling accuracy comparable to the Hungarian algorithm. However the EPS method outperforms the CSA method in computational efficiency, achieving linear comp utation time comparable to the efficient sub-optimal methods. More generally, we make recommendations for using one to one correspondence matching over other methods in order to produce reliable performance scores across 2-D and 3-D image data.