Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/86799
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Type: Journal article
Title: Interacting geometric priors for robust multimodel fitting
Author: Pham, T.
Chin, T.
Schindler, K.
Suter, D.
Citation: IEEE Transactions on Image Processing, 2014; 23(10):4601-4610
Publisher: Institute of Electrical and Electronics Engineers
Issue Date: 2014
ISSN: 1057-7149
1941-0042
Statement of
Responsibility: 
Trung Thanh Pham, Tat-Jun Chin, Konrad Schindler, and David Suter
Abstract: 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
Rights: © 2014 IEEE.
RMID: 0030011704
DOI: 10.1109/TIP.2014.2346025
Appears in Collections:Computer Science publications

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