Please use this identifier to cite or link to this item:
Scopus Web of Science® Altmetric
Type: Conference paper
Title: Mode-Seeking on Hypergraphs for Robust Geometric Model Fitting
Author: Wang, H.
Xiao, G.
Yan, Y.
Suter, D.
Citation: Proceedings / IEEE International Conference on Computer Vision. IEEE International Conference on Computer Vision, 2015, vol.Proceedings 2015 IEEE International Conference on Computer Vision, pp.2902-2910
Publisher: IEEE
Issue Date: 2015
Series/Report no.: IEEE International Conference on Computer Vision
ISBN: 9781467383912
ISSN: 1550-5499
Conference Name: IEEE International Conference on Computer Vision (ICCV) (11 Dec 2015 - 18 Dec 2015 : Santiago, Chile)
Statement of
Hanzi Wang, Guobao Xiao, Yan Yan, David Suter
Abstract: In this paper, we propose a novel geometric model fitting method, called Mode-Seeking on Hypergraphs (MSH), to deal with multi-structure data even in the presence of severe outliers. The proposed method formulates geometric model fitting as a mode seeking problem on a hypergraph in which vertices represent model hypotheses and hyperedges denote data points. MSH intuitively detects model instances by a simple and effective mode seeking algorithm. In addition to the mode seeking algorithm, MSH includes a similarity measure between vertices on the hypergraph and a “weight-aware sampling” technique. The proposed method not only alleviates sensitivity to the data distribution, but also is scalable to large scale problems. Experimental results further demonstrate that the proposed method has significant superiority over the state-of-the-art fitting methods on both synthetic data and real images.
Rights: © 2015 IEEE
DOI: 10.1109/ICCV.2015.332
Grant ID:
Published version:
Appears in Collections:Aurora harvest 3
Computer Science publications

Files in This Item:
File Description SizeFormat 
  Restricted Access
Restricted Access1.27 MBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.