Interacting Geometric Priors For Robust Multimodel Fitting

Trung Pham

doi:10.1109/TIP.2014.2346025

Interacting Geometric Priors For Robust Multimodel Fitting

2014-10-01

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AI

DOI：10.1109/TIP.2014.2346025

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Recent works on multimodel fitting are often formulated as an energy minimization task, where the energy function includes fitting error and regularization terms, such as low-level spatial smoothness and model complexity. In this paper, we introduce a novel energy with high-level geometric priors that consider interactions between geometric models, such that certain preferred model configurations may be induced. We argue that in many applications, such prior geometric properties are available and should be fruitfully exploited. For example, in surface fitting to point clouds, the building walls are usually either orthogonal or parallel to each other. Our proposed energy function is useful in dealing with unknown distributions of data errors and outliers, which are often the factors leading to biased estimation. Furthermore, the energy can be efficiently minimized using the expansion move method. We evaluate the performance on several vision applications using real data sets. Experimental results show that our method outperforms the state-of-the-art methods without significant increase in computation.

Keywords:

Robust statistics

multi-model fitting

model selection

global constraints

geometric priors

Interacting Geometric Priors For Robust Multimodel Fitting

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