Multiple Resolution Segmentation of Textured Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Original Contribution: Stacked generalization
Neural Networks
A review of recent texture segmentation and feature extraction techniques
CVGIP: Image Understanding
The nature of statistical learning theory
The nature of statistical learning theory
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
WACV '98 Proceedings of the 4th IEEE Workshop on Applications of Computer Vision (WACV'98)
Bayesian Fusion of Color and Texture Segmentations
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Enhanced Real-time Stereo Using Bilateral Filtering
3DPVT '04 Proceedings of the 3D Data Processing, Visualization, and Transmission, 2nd International Symposium
Toward Reliable Off Road Autonomous Vehicles Operating in Challenging Environments
International Journal of Robotics Research
Vibration-based terrain classification for planetary exploration rovers
IEEE Transactions on Robotics
Visual detection of novel terrain via two-class classification
Proceedings of the 2009 ACM symposium on Applied Computing
A spherical hopping robot for exploration in complex environments
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
Intelligent Service Robotics
Information Sciences: an International Journal
Self-supervised terrain classification for planetary surface exploration rovers
Journal of Field Robotics
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Knowledge of the physical properties of terrain surrounding a planetary exploration rover can be used to allow a rover system to fully exploit its mobility capabilities. Terrain classification methods provide semantic descriptions of the physical nature of a given terrain region. These descriptions can be associated with nominal numerical physical parameters, and/or nominal traversability estimates, to improve mobility prediction accuracy. Here we study the performance of multisensor classification methods in the context of Mars surface exploration. The performance of two classification algorithms for color, texture, and range features are presented based on maximum likelihood estimation and support vector machines. In addition, a classification method based on vibration features derived from rover wheel–terrain interaction is briefly described. Two techniques for merging the results of these “low-level” classifiers are presented that rely on Bayesian fusion and meta-classifier fusion. The performance of these algorithms is studied using images from NASA's Mars Exploration Rover mission and through experiments on a four-wheeled test-bed rover operating in Mars-analog terrain. Also a novel approach to terrain sensing based on fused tactile and visual features is presented. It is shown that accurate terrain classification can be achieved via classifier fusion from visual and tactile features.