Learning variability of image feature appearance using statistical methods
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
Lattice independent component analysis for mobile robot localization
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part II
| Hi-index | 0.00 |
In this paper we present a method to recognize images features with a wide base line between learning and recognition phases. The method is based in feature descriptors derived from independent component analysis (ICA). This technique is inspired by the problems of mobile robot mapping and localization using single camera. In the learning phase the descriptors are created to capture the variations in the appearance of each feature across a small base line tracking and stored in a database. The recognition phase proceeds to match descriptors created from the incoming video (with a wide base line respect to the learning phase) in the database. The implementation shows good computational performance.