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Segmentation based features for wide-baseline multi-view reconstruction

Segmentation based features for wide-baseline multi-view reconstruction
Segmentation based features for wide-baseline multi-view reconstruction
A common problem in wide-baseline stereo is the sparse and non-uniform distribution of correspondences when using conventional detectors such as SIFT, SURF, FAST and MSER. In this paper we introduce a novel segmentation based feature detector SFD that produces an increased number of 'good' features for accurate wide-baseline reconstruction. Each image is segmented into regions by over-segmentation and feature points are detected at the intersection of the boundaries for three or more regions. Segmentation-based feature detection locates features at local maxima giving a relatively large number of feature points which are consistently detected across wide-baseline views and accurately localised. A comprehensive comparative performance evaluation with previous feature detection approaches demonstrates that: SFD produces a large number of features with increased scene coverage, detected features are consistent across wide-baseline views for images of a variety of indoor and outdoor scenes, and the number of wide-baseline matches is increased by an order of magnitude compared to alternative detector-descriptor combinations. Sparse scene reconstruction from multiple wide-baseline stereo views using the SFD feature detector demonstrates at least a factor six increase in the number of reconstructed points with reduced error distribution compared to SIFT when evaluated against ground-truth and similar computational cost to SURF/FAST. © 2015 IEEE.
Image segmentation, Stereo image processing, Comparative performance, Conventional detectors, Descriptor combinations, Feature detection, Multi-view reconstruction, Non-uniform distribution, Segementation, Wide baseline stereo, Feature extraction
282-290
Mustafa, A.
29037014-ab45-4368-81e3-6b698e9bbbd0
Kim, H.
2c7c135c-f00b-4409-acb2-85b3a9e8225f
Imre, E.
d5e10a85-873f-4e03-80b4-d6c3f4c6ca3d
Hilton, Adrian
12782a55-4c4d-4dfb-a690-62505f6665db
Mustafa, A.
29037014-ab45-4368-81e3-6b698e9bbbd0
Kim, H.
2c7c135c-f00b-4409-acb2-85b3a9e8225f
Imre, E.
d5e10a85-873f-4e03-80b4-d6c3f4c6ca3d
Hilton, Adrian
12782a55-4c4d-4dfb-a690-62505f6665db

Mustafa, A., Kim, H., Imre, E. and Hilton, Adrian (2015) Segmentation based features for wide-baseline multi-view reconstruction. International Conference on 3D Vision, France. 19 - 22 Oct 2015. pp. 282-290 . (doi:10.1109/3DV.2015.39).

Record type: Conference or Workshop Item (Paper)

Abstract

A common problem in wide-baseline stereo is the sparse and non-uniform distribution of correspondences when using conventional detectors such as SIFT, SURF, FAST and MSER. In this paper we introduce a novel segmentation based feature detector SFD that produces an increased number of 'good' features for accurate wide-baseline reconstruction. Each image is segmented into regions by over-segmentation and feature points are detected at the intersection of the boundaries for three or more regions. Segmentation-based feature detection locates features at local maxima giving a relatively large number of feature points which are consistently detected across wide-baseline views and accurately localised. A comprehensive comparative performance evaluation with previous feature detection approaches demonstrates that: SFD produces a large number of features with increased scene coverage, detected features are consistent across wide-baseline views for images of a variety of indoor and outdoor scenes, and the number of wide-baseline matches is increased by an order of magnitude compared to alternative detector-descriptor combinations. Sparse scene reconstruction from multiple wide-baseline stereo views using the SFD feature detector demonstrates at least a factor six increase in the number of reconstructed points with reduced error distribution compared to SIFT when evaluated against ground-truth and similar computational cost to SURF/FAST. © 2015 IEEE.

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More information

Published date: 19 October 2015
Venue - Dates: International Conference on 3D Vision, France, 2015-10-19 - 2015-10-22
Keywords: Image segmentation, Stereo image processing, Comparative performance, Conventional detectors, Descriptor combinations, Feature detection, Multi-view reconstruction, Non-uniform distribution, Segementation, Wide baseline stereo, Feature extraction

Identifiers

Local EPrints ID: 440921
URI: http://eprints.soton.ac.uk/id/eprint/440921
PURE UUID: cec1f3ce-b30e-4522-ad97-08d9c9be21f7
ORCID for H. Kim: ORCID iD orcid.org/0000-0003-4907-0491

Catalogue record

Date deposited: 22 May 2020 16:38
Last modified: 23 May 2020 00:47

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