Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/86799
Citations | ||
Scopus | Web of Science® | Altmetric |
---|---|---|
?
|
?
|
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. |
DOI: | 10.1109/TIP.2014.2346025 |
Published version: | http://dx.doi.org/10.1109/tip.2014.2346025 |
Appears in Collections: | Aurora harvest 7 Computer Science publications |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.