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The brightness clustering transformand locally contrasting keypoints

The brightness clustering transformand locally contrasting keypoints
The brightness clustering transformand locally contrasting keypoints
In recent years a new wave of feature descriptors has been presented to the computer vision community, ORB, BRISK and FREAK amongst others. These new descriptors allow reduced time and memory consumption on the processing and storage stages of tasks such as image matching or visual odometry, enabling real time applications. The problem is now the lack of fast interest point detectors with good repeatability to use with these new descriptors. We present a new blob-detector which can be implemented in real time and is faster than most of the currently used feature-detectors. The detection is achieved with an innovative non-deterministic low-level operator called the Brightness Clustering Transform (BCT). The BCT can be thought as a coarse-to-fine search through scale spaces for the true derivative of the image; it also mimics trans-saccadic perception of human vision. We call the new algorithm Locally Contrasting Keypoints detector or LOCKY. Showing good repeatability and robustness to image transformations included in the Oxford dataset, LOCKY is amongst the fastest affine-covariant feature detectors.
362
Lomeli Rodriguez, Jaime
26137049-8361-4a68-a7d9-734befbc5e58
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Lomeli Rodriguez, Jaime
26137049-8361-4a68-a7d9-734befbc5e58
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12

Lomeli Rodriguez, Jaime and Nixon, Mark (2015) The brightness clustering transformand locally contrasting keypoints. International Conference on Computer Analysis of Images and Patterns, Malta. 02 - 04 Sep 2015. p. 362 . (doi:10.1007/978-3-319-23192-1_30).

Record type: Conference or Workshop Item (Paper)

Abstract

In recent years a new wave of feature descriptors has been presented to the computer vision community, ORB, BRISK and FREAK amongst others. These new descriptors allow reduced time and memory consumption on the processing and storage stages of tasks such as image matching or visual odometry, enabling real time applications. The problem is now the lack of fast interest point detectors with good repeatability to use with these new descriptors. We present a new blob-detector which can be implemented in real time and is faster than most of the currently used feature-detectors. The detection is achieved with an innovative non-deterministic low-level operator called the Brightness Clustering Transform (BCT). The BCT can be thought as a coarse-to-fine search through scale spaces for the true derivative of the image; it also mimics trans-saccadic perception of human vision. We call the new algorithm Locally Contrasting Keypoints detector or LOCKY. Showing good repeatability and robustness to image transformations included in the Oxford dataset, LOCKY is amongst the fastest affine-covariant feature detectors.

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More information

Published date: 2015
Venue - Dates: International Conference on Computer Analysis of Images and Patterns, Malta, 2015-09-02 - 2015-09-04

Identifiers

Local EPrints ID: 426229
URI: http://eprints.soton.ac.uk/id/eprint/426229
PURE UUID: 4efc6cab-6f5a-4d4a-9995-d9e00887002a
ORCID for Mark Nixon: ORCID iD orcid.org/0000-0002-9174-5934

Catalogue record

Date deposited: 20 Nov 2018 17:30
Last modified: 07 Oct 2020 02:36

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