Machine Learning
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Advances in Large Margin Classifiers
Advances in Large Margin Classifiers
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Biologically inspired Cartesian and non-Cartesian filters for attentional sequences
Pattern Recognition Letters
Learning and Evaluating Visual Features for Pose Estimation
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Robust Localization Using Panoramic View-Based Recognition
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
Context-based vision system for place and object recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Towards a general theory of topological maps
Artificial Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
APES: Attentively Perceiving Robot
Autonomous Robots
Rotation Recovery from Spherical Images without Correspondences
IEEE Transactions on Pattern Analysis and Machine Intelligence
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Cognitive maps for mobile robots-an object based approach
Robotics and Autonomous Systems
From omnidirectional images to hierarchical localization
Robotics and Autonomous Systems
MonoSLAM: Real-Time Single Camera SLAM
IEEE Transactions on Pattern Analysis and Machine Intelligence
A note on Platt's probabilistic outputs for support vector machines
Machine Learning
Scene Classification Using a Hybrid Generative/Discriminative Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance
International Journal of Robotics Research
COLD: The CoSy Localization Database
International Journal of Robotics Research
The New College Vision and Laser Data Set
International Journal of Robotics Research
International Journal of Robotics Research
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Appearance-only SLAM at large scale with FAB-MAP 2.0
International Journal of Robotics Research
An extended-HCT semantic description for visual place recognition
International Journal of Robotics Research
Sampling strategies for bag-of-features image classification
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Histogram of Oriented Uniform Patterns for robust place recognition and categorization
International Journal of Robotics Research
IEEE Transactions on Robotics
Vision-based global localization and mapping for mobile robots
IEEE Transactions on Robotics
Range-Only SLAM With Occupancy Maps: A Set-Membership Approach
IEEE Transactions on Robotics
Integrating cue descriptors in bubble space for place recognition
ICVS'13 Proceedings of the 9th international conference on Computer Vision Systems
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This paper presents bubble space based representation of 芒聙聹places芒聙聺 (nodes) in topological maps. Bubble space simultaneously provides for detailed (bubble surfaces) and holistic (bubble descriptors) representation of places. It is based on bubble memory where visual feature values and their local S2-metric relations from robot's viewpoint are simultaneously encoded on a deformable spherical surface. Bubble surfaces extend bubble memory to accommodate varying robot pose and multiple features. They are transformed into bubble descriptors that are rotationally invariant with respect to heading changes while being computable in an incremental manner as each new set of visual observations is made. We use bubble descriptors for place learning and recognition with support vector machines in both indoor and outdoor environments and provide analysis results on recognition, recall and precision rates and time performance including a comparative study with the state-of-the-art descriptors.