Filtering for Texture Classification: A Comparative Study
IEEE Transactions on Pattern Analysis and Machine Intelligence
Selecting Landmarks for Localization in Natural Terrain
Autonomous Robots
Feature selection for reliable tracking using template matching
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
The Selection of Critical Subsets for Signal, Image, and Scene Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Image Processing
Vision-based unmanned aerial vehicle navigation using geo-referenced information
EURASIP Journal on Advances in Signal Processing - Special issue on signal processing advances in robots and autonomy
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To enhance the reliability of path planning in scenery guidance system, it's very important to select reliable or high matching probability areas from the navigation reference images for performing unmanned aerial vehicles localization. This paper applies three measures and proposes a new selection scheme base on a simplified Mumford-Shah model. The proposed method artfully avoids selecting thresholds to separate the feature images and optimally selects robust-matching areas by evolving the level set function. Experiments of the selection show that the proposed method is efficient.