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MSFD: Multi-scale segmentation based feature detection for wide-baseline scene reconstruction

MSFD: Multi-scale segmentation based feature detection for wide-baseline scene reconstruction
MSFD: Multi-scale segmentation based feature detection for wide-baseline scene reconstruction
A common problem in wide-baseline matching is the sparse and non-uniform distribution of correspondences when using conventional detectors, such as SIFT, SURF, FAST, A-KAZE, and MSER. In this paper, we introduce a novel segmentation-based feature detector (SFD) that produces an increased number of accurate features for wide-baseline matching. A multi-scale SFD is proposed using bilateral image decomposition to produce a large number of scale-invariant features for wide-baseline reconstruction. All input images are over-segmented into regions using any existing segmentation technique, such as Watershed, Mean-shift, and simple linear iterative clustering. Feature points are then detected at the intersection of the boundaries of three or more regions. The detected feature points are local maxima of the image function. The key advantage of feature detection based on segmentation is that it does not require global threshold setting and can, therefore, detect features throughout the image. A comprehensive evaluation demonstrates that SFD gives an increased number of features that are accurately localized and matched between wide-baseline camera views; the number of features for a given matching error increases by a factor of 3-5 compared with SIFT; feature detection and matching performance are maintained with increasing baseline between views; multi-scale SFD improves matching performance at varying scales. Application of SFD to sparse multi-view wide-baseline reconstruction demonstrates a factor of 10 increases in the number of reconstructed points with improved scene coverage compared with SIFT/MSER/A-KAZE. Evaluation against ground-truth shows that SFD produces an increased number of wide-baseline matches with a reduced error.
1057-7149
1118-1132
Mustafa, Armin
29037014-ab45-4368-81e3-6b698e9bbbd0
Kim, Hansung
2c7c135c-f00b-4409-acb2-85b3a9e8225f
Hilton, Adrian
12782a55-4c4d-4dfb-a690-62505f6665db
Mustafa, Armin
29037014-ab45-4368-81e3-6b698e9bbbd0
Kim, Hansung
2c7c135c-f00b-4409-acb2-85b3a9e8225f
Hilton, Adrian
12782a55-4c4d-4dfb-a690-62505f6665db

Mustafa, Armin, Kim, Hansung and Hilton, Adrian (2019) MSFD: Multi-scale segmentation based feature detection for wide-baseline scene reconstruction. IEEE Transactions on Image Processing, 28 (3), 1118-1132. (doi:10.1109/TIP.2018.2872906).

Record type: Article

Abstract

A common problem in wide-baseline matching is the sparse and non-uniform distribution of correspondences when using conventional detectors, such as SIFT, SURF, FAST, A-KAZE, and MSER. In this paper, we introduce a novel segmentation-based feature detector (SFD) that produces an increased number of accurate features for wide-baseline matching. A multi-scale SFD is proposed using bilateral image decomposition to produce a large number of scale-invariant features for wide-baseline reconstruction. All input images are over-segmented into regions using any existing segmentation technique, such as Watershed, Mean-shift, and simple linear iterative clustering. Feature points are then detected at the intersection of the boundaries of three or more regions. The detected feature points are local maxima of the image function. The key advantage of feature detection based on segmentation is that it does not require global threshold setting and can, therefore, detect features throughout the image. A comprehensive evaluation demonstrates that SFD gives an increased number of features that are accurately localized and matched between wide-baseline camera views; the number of features for a given matching error increases by a factor of 3-5 compared with SIFT; feature detection and matching performance are maintained with increasing baseline between views; multi-scale SFD improves matching performance at varying scales. Application of SFD to sparse multi-view wide-baseline reconstruction demonstrates a factor of 10 increases in the number of reconstructed points with improved scene coverage compared with SIFT/MSER/A-KAZE. Evaluation against ground-truth shows that SFD produces an increased number of wide-baseline matches with a reduced error.

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More information

Accepted/In Press date: 23 September 2018
e-pub ahead of print date: 28 September 2018
Published date: March 2019

Identifiers

Local EPrints ID: 438837
URI: http://eprints.soton.ac.uk/id/eprint/438837
ISSN: 1057-7149
PURE UUID: 94a28109-2476-443c-acda-a3a88541ec84
ORCID for Hansung Kim: ORCID iD orcid.org/0000-0003-4907-0491

Catalogue record

Date deposited: 25 Mar 2020 17:31
Last modified: 07 Oct 2020 02:27

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