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

Mustafa, Armin, Kim, Hansung and Hilton, Adrian (2018) MSFD: Multi-scale segmentation based feature detection for wide-baseline scene reconstruction IEEE Transactions on Image Processing, 28 (3). pp. 1118-1132.

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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 like Watershed, Mean-shift, and SLIC. 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 which are accurately localised and matched between wide-baseline camera views; the number of features for a given matching error increases by a factor of 3-5 compared to SIFT; feature detection and matching performance is 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 ten increase in the number of reconstructed points with improved scene coverage compared to SIFT/MSER/A-KAZE. Evaluation against ground-truth shows that SFD produces an increased number of wide-baseline matches with reduced error.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Mustafa, Arminarmin.mustafa@surrey.ac.uk
Kim, HansungH.Kim@surrey.ac.uk
Hilton, AdrianA.Hilton@surrey.ac.uk
Date : 28 September 2018
Funders : Engineering and Physical Sciences Research Council (EPSRC)
DOI : 10.1109/TIP.2018.2872906
Grant Title : Platform Grant on Audio-Visual Media Research
Copyright Disclaimer : © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords : Feature detection; Segmentation; Matching; Sparse Reconstruction
Depositing User : Clive Harris
Date Deposited : 03 Oct 2018 08:56
Last Modified : 11 Dec 2018 11:24
URI: http://epubs.surrey.ac.uk/id/eprint/849509

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