Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/107660
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Type: | Conference paper |
Title: | Superpixel-based two-view deterministic fitting for multiple-structure data |
Author: | Xiao, G. Wang, H. Yan, Y. Suter, D. |
Citation: | Lecture Notes in Artificial Intelligence, 2016 / Leibe, B., Matas, J., Sebe, N., Welling, M. (ed./s), vol.9910, pp.517-533 |
Publisher: | Springer International Publishing AG |
Issue Date: | 2016 |
Series/Report no.: | Lecture Notes in Computer Science |
ISBN: | 9783319464657 |
ISSN: | 0302-9743 1611-3349 |
Conference Name: | 14th European Conference on Computer Vision (ECCV) (8 Oct 2016 - 16 Oct 2016 : Amsterdam, Netherlands) |
Editor: | Leibe, B. Matas, J. Sebe, N. Welling, M. |
Statement of Responsibility: | Guobao Xiao, Hanzi Wang, B, Yan Yan, and David Suter |
Abstract: | This paper proposes a two-view deterministic geometric model fitting method, termed Superpixel-based Deterministic Fitting (SDF), for multiple-structure data. SDF starts from superpixel segmentation, which effectively captures prior information of feature appearances. The feature appearances are beneficial to reduce the computational complexity for deterministic fitting methods. SDF also includes two original elements, i.e., a deterministic sampling algorithm and a novel model selection algorithm. The two algorithms are tightly coupled to boost the performance of SDF in both speed and accuracy. The key characteristic of SDF is that it can efficiently and deterministically estimate the parameters of model instances in multi-structure data. Experimental results demonstrate that the proposed SDF shows superiority over several state-of-the-art fitting methods for real images with singlestructure and multiple-structure data. |
Keywords: | Deterministic algorithm; Superpixel; Model fitting; Feature appearances |
Rights: | © Springer International Publishing AG 2016 |
DOI: | 10.1007/978-3-319-46466-4_31 |
Grant ID: | http://purl.org/au-research/grants/arc/DP130102524 |
Published version: | http://dx.doi.org/10.1007/978-3-319-46466-4_31 |
Appears in Collections: | Aurora harvest 3 Computer Science publications |
Files in This Item:
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RA_hdl_107660.pdf Restricted Access | Restricted Access | 4.6 MB | Adobe PDF | View/Open |
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