An experimental comparison of RGB, YIQ, LAB, HSV, and opponent color models
ACM Transactions on Graphics (TOG)
The nature of statistical learning theory
The nature of statistical learning theory
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
A Stochastic Optimization Approach for Parameter Tuning of Support Vector Machines
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Output-Sensitive Algorithms for Computing Nearest-Neighbour Decision Boundaries
Discrete & Computational Geometry
Monocular Vision for Mobile Robot Localization and Autonomous Navigation
International Journal of Computer Vision
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Advances in Unmanned Aerial Vehicles: State of the Art and the Road to Autonomy
Advances in Unmanned Aerial Vehicles: State of the Art and the Road to Autonomy
Image Classification in CBIR Systems with Color Histogram Features
ARTCOM '09 Proceedings of the 2009 International Conference on Advances in Recent Technologies in Communication and Computing
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A new set of attributes combining color and SURF-based histograms coupled with a SVM classifier to enhance visual based autonomous aerial navigation is proposed. These new features are used for region classification with aerial images in order to speed up the UAV (Unmanned Aerial Vehicles) localization performed by image matching using only reference images according to the region classification. Experimental results comparing the proposal with color or SURF only attributes are presented. In the experiments the UAV localization task can be performed four times faster using the proposed approach, however the performance gain can be still bigger for large datasets of reference images.