Estimate the Performance of Multi-Model Estimation Algorithms

Abstract:

A robust estimation procedure is necessary to estimate the true model parameters in computer vision. Evaluating the multiple-model in the presence of outliers-robust is a fundamentally different task than the single-model problem.Despite there are many diversity multi-model estimation algorithms, it is difficult to pick an effective and advisably approach.So we present a novel quantitative evaluation of multi-model estimation algorithms, efficiency may be evaluated by either examining the asymptotic efficiency of the algorithms or by running them for a series of data sets of increasing size.Thus we create a specifical testing dataset,and introduce a performance metric, Strongest-Intersection.and using the model-aware correctness criterion. Finally, well show the validity of estimation strategy by the Experimention of line-fitting.

• Periodical: Applied Mechanics and Materials (Volumes 427-429)
• Pages: 1506-1509
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.427-429.1506
• Online since: September 2013
• Authors: Yong Yan Yu*
• Keywords: Multiple-Model, Performance Metric, Strongest-Intersection, Test Dataset

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