Learning Probabilistic Relational Models
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Probabilistic visual learning for object detection
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Learning to Detect Objects in Images via a Sparse, Part-Based Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shared Features for Scalable Appearance-Based Object Recognition
WACV-MOTION '05 Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION'05) - Volume 1 - Volume 01
Case-Based Collective Inference for Maritime Object Classification
ICCBR '09 Proceedings of the 8th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
The cooperative conceptualization of urban spaces in AI-assisted environmental planning
CDVE'09 Proceedings of the 6th international conference on Cooperative design, visualization, and engineering
Memory and creativity in cooperative vs. non-cooperative spatial planning and architecture
CDVE'10 Proceedings of the 7th international conference on Cooperative design, visualization, and engineering
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A robot's ability to assist humans in a variety of tasks, e.g. in search and rescue or in a household, heavily depends on the robot's reliable recognition of the objects in the environment. Numerous approaches attempt to recognize objects based only on the robot's vision. However, the same type of object can have very different visual appearances, such as shape, size, pose, and color. Although such approaches are widely studied with relative success, the general object recognition task still remains very challenging. We build our work upon the fact that robots can observe humans interacting with the objects in their environment, and thus providing numerous non-visual cues to those objects' identities. We research on a flexible object recognition approach which can use any multiple cues, whether they are visual cues intrinsic to the object or provided by observation of a human. We realize the challenging issue that multiple cues can have different weight in their association with an object definition and need to be taken into account during recognition. In this paper, we contribute a probabilistic relational representation of the cue weights and an object recognition algorithm that can flexibly combine multiple cues of any type to robustly recognize objects. We show illustrative results of our implemented approach using visual, activity, gesture, and speech cues, provided by machine or human, to recognize objects more robustly than when using only a single cue.