Abstract
In this paper, we propose a gradient-based local affine invariant feature extraction algorithm (G-LAIFE), using affine moment invariants for robot localization in real indoor environments. The proposed algorithm is an effective feature extraction algorithm that is invariant to image translation and to 3D rotation, and it is within a partial range of the image scale. Representative performance analysis confirms that the proposed G-LAIFE algorithm significantly enhances the recognition rate and is more efficient than the scale invariant feature transform (SIFT), especially in terms of 3D rotation change and computational time.
Original language | English |
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Pages (from-to) | 1934-1940 |
Number of pages | 7 |
Journal | Pattern Recognition Letters |
Volume | 29 |
Issue number | 14 |
DOIs | |
Publication status | Published - 2008 Oct 15 |
Keywords
- 3D rotation
- Affine invariant
- Local feature extraction
- Translation
ASJC Scopus subject areas
- Software
- Signal Processing
- Computer Vision and Pattern Recognition
- Artificial Intelligence