CORE
🇺🇦
make metadata, not war
Services
Services overview
Explore all CORE services
Access to raw data
API
Dataset
FastSync
Content discovery
Recommender
Discovery
OAI identifiers
OAI Resolver
Managing content
Dashboard
Bespoke contracts
Consultancy services
Support us
Support us
Membership
Sponsorship
Community governance
Advisory Board
Board of supporters
Research network
About
About us
Our mission
Team
Blog
FAQs
Contact us
Cluster-based point set saliency
Authors
N Dodgson
J Kosinka
FP Tasse
Publication date
1 January 2015
Publisher
Proceedings of the IEEE International Conference on Computer Vision
Doi
Cite
Abstract
© 2015 IEEE. We propose a cluster-based approach to point set saliency detection, a challenge since point sets lack topological information. A point set is first decomposed into small clusters, using fuzzy clustering. We evaluate cluster uniqueness and spatial distribution of each cluster and combine these values into a cluster saliency function. Finally, the probabilities of points belonging to each cluster are used to assign a saliency to each point. Our approach detects fine-scale salient features and uninteresting regions consistently have lower saliency values. We evaluate the proposed saliency model by testing our saliency-based keypoint detection against a 3D interest point detection benchmark. The evaluation shows that our method achieves a good balance between false positive and false negative error rates, without using any topological information
Similar works
Full text
Open in the Core reader
Download PDF
Available Versions
Sustaining member
Apollo (Cambridge)
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:www.repository.cam.ac.uk:1...
Last time updated on 03/06/2019
University of Groningen Research Database
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:pure.rug.nl:publications/3...
Last time updated on 10/02/2018
NARCIS
See this paper in CORE
Go to the repository landing page
Download from data provider
Last time updated on 09/03/2017