Research output per year
Research output per year
Pilailuck Panphattarasap*, Andrew Calway
Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)
Recent work by Sünderhauf et al. [1] demonstrated improved visual place recognition using proposal regions coupled with features from convolutional neural networks (CNN) to match landmarks between views. In this work we extend the approach by introducing descriptors built from landmark features which also encode the spatial distribution of the landmarks within a view. Matching descriptors then enforces consistency of the relative positions of landmarks between views. This has a significant impact on performance. For example, in experiments on 10 image-pair datasets, each consisting of 200 urban locations with significant differences in viewing positions and conditions, we recorded average precision of around 70% (at 100% recall), compared with 58% obtained using whole image CNN features and 50% for the method in [1].
Original language | English |
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Title of host publication | Computer Vision - ACCV 2016 |
Subtitle of host publication | 13th Asian Conference on Computer Vision, ACCV 2016, Revised Selected Papers |
Publisher | Springer-Verlag Berlin |
Pages | 487-502 |
Number of pages | 16 |
Volume | 10114 LNCS |
ISBN (Print) | 9783319541891 |
DOIs | |
Publication status | Published - 2017 |
Event | 13th Asian Conference on Computer Vision 2016: Workshop on Assistive Vision - Taipei International Convention Center, Taipei, Taiwan Duration: 20 Nov 2016 → 24 Nov 2016 Conference number: 13 http://www.accv2016.org/ |
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 10114 LNCS |
ISSN (Print) | 03029743 |
ISSN (Electronic) | 16113349 |
Conference | 13th Asian Conference on Computer Vision 2016 |
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Abbreviated title | ACCV 16 |
Country/Territory | Taiwan |
City | Taipei |
Period | 20/11/16 → 24/11/16 |
Internet address |
Research output: Working paper
Person: Academic , Member