The brightness clustering transform and locally contrasting keypoints
The brightness clustering transform and 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.
Lomeli-R., Jaime
26137049-8361-4a68-a7d9-734befbc5e58
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12
2 September 2015
Lomeli-R., Jaime
26137049-8361-4a68-a7d9-734befbc5e58
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Lomeli-R., Jaime and Nixon, Mark
(2015)
The brightness clustering transform and locally contrasting keypoints.
16th International Conference on Computer Analysis of Images and Patterns, , Valleta, Malta.
02 - 04 Sep 2015.
12 pp
.
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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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Published date: 2 September 2015
Venue - Dates:
16th International Conference on Computer Analysis of Images and Patterns, , Valleta, Malta, 2015-09-02 - 2015-09-04
Organisations:
Vision, Learning and Control
Identifiers
Local EPrints ID: 388509
URI: http://eprints.soton.ac.uk/id/eprint/388509
PURE UUID: f3133331-581b-40a6-aadd-230979d76d70
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Date deposited: 02 Mar 2016 14:55
Last modified: 15 Mar 2024 02:35
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Contributors
Author:
Jaime Lomeli-R.
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