Estimate the Performance of Multi-Model Estimation Algorithms

• Published in: Applied Mechanics and Materials Vol. 427-429; no. Mechanical Engineering, Industrial Electronics and Information Technology Applications in Industry; pp. 1506 - 1509
• Main Author: Yu, Yong Yan
• Format: Journal Article
• Language: English
• Published: Zurich Trans Tech Publications Ltd 01.09.2013
• Subjects: Algorithms; Asymptotic properties; Estimates; Performance measurement; Running; Strategy; Tasks; Performance Metric; Test Dataset; Multiple-Model; Strongest-Intersection
• ISBN: 9783037858905; 3037858907
• ISSN: 1660-9336; 1662-7482; 1662-7482
• DOI: 10.4028/www.scientific.net/AMM.427-429.1506

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.