Abstract

This paper introduces a topology based affine invariant descriptor for maximally stable extremal regions (MSERs). The popular SIFT descriptor computes the texture information on a grey-scale patch. Instead our descriptor use only the topology and geometric information among MSERs so that features can be rapidly matched regardless of the texture in the image patch. Based on the ellipses fitting for the detected MSERs, geometric affine invariants between ellipses pair are extracted as the descriptors. Finally topology based voting selector is designed to achieve the best correspondences. Experiment shows that our descriptor is not only computational faster than SIFT descriptor, but also has better performance on wide angle of view and nonlinear illumination change. In addition, our descriptor shows a good result on multi sensor images registration.

Keywords:
Scale-invariant feature transform Ellipse Affine transformation Topology (electrical circuits) Invariant (physics) Artificial intelligence Mathematics Computer science Pattern recognition (psychology) Computer vision Affine shape adaptation Image (mathematics) Affine combination Pure mathematics Geometry Combinatorics

Metrics

6
Cited By
0.64
FWCI (Field Weighted Citation Impact)
14
Refs
0.70
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Advanced Image and Video Retrieval Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Robotics and Sensor-Based Localization
Physical Sciences →  Engineering →  Aerospace Engineering
Image Retrieval and Classification Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition

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