Hypergraph optimization for multi-structural geometric model fitting

Simple item page
dc.contributor.authorLin, S.
dc.contributor.authorXiao, G.
dc.contributor.authorYan, Y.
dc.contributor.authorSuter, D.
dc.contributor.authorWang, H.
dc.contributor.conferenceAAAI Conference on Artificial Intelligence (27 Jan 2019 - 1 Feb 2019 : Honolulu, HI)
dc.date.issued2019
dc.description.abstractRecently, some hypergraph-based methods have been pro- posed to deal with the problem of model fitting in computer vision, mainly due to the superior capability of hypergraph to represent the complex relationship between data points. How- ever, a hypergraph becomes extremely complicated when the input data include a large number of data points (usually con- taminated with noises and outliers), which will significantly increase the computational burden. In order to overcome the above problem, we propose a novel hypergraph optimization based model fitting (HOMF) method to construct a simple but effective hypergraph. Specifically, HOMF includes two main parts: an adaptive inlier estimation algorithm for ver- tex optimization and an iterative hyperedge optimization al- gorithm for hyperedge optimization. The proposed method is highly efficient, and it can obtain accurate model fitting re- sults within a few iterations. Moreover, HOMF can then di- rectly apply spectral clustering, to achieve good fitting per- formance. Extensive experimental results show that HOMF outperforms several state-of-the-art model fitting methods on both synthetic data and real images, especially in sampling efficiency and in handling data with severe outliers
dc.description.statementofresponsibilityShuyuan Lin, Guobao Xiao, Yan Yan, David Suter, Hanzi Wang
dc.identifier.citationProceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence, 2019, vol.33, iss.01, pp.8730-8737
dc.identifier.doi10.1609/aaai.v33i01.33018730
dc.identifier.isbn9781577358091
dc.identifier.issn2159-5399
dc.identifier.issn2374-3468
dc.identifier.orcidSuter, D. [0000-0001-6306-3023]
dc.identifier.urihttps://hdl.handle.net/2440/132106
dc.language.isoen
dc.publisherAssociation for the Advancement of Artificial Intelligence
dc.relation.granthttp://purl.org/au-research/grants/arc/DP160103490
dc.relation.ispartofseriesAAAI Conference on Artificial Intelligence
dc.rights© 2019, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
dc.source.urihttps://aaai.org/Library/AAAI/aaai19contents.php
dc.titleHypergraph optimization for multi-structural geometric model fitting
dc.typeConference paper
pubs.publication-statusPublished

Files

Collections

Electrical and Electronic Engineering publications