Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Control of selective perception using Bayes nets and decision theory
International Journal of Computer Vision - Special issue on active vision II
Visual semantics: extracting visual information from text accompanying pictures
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Bucket elimination: a unifying framework for probabilistic inference
Learning in graphical models
Integrated Recognition and Interpretation of Speech for a Construction Task Domain
Proceedings of HCI International (the 8th International Conference on Human-Computer Interaction) on Human-Computer Interaction: Ergonomics and User Interfaces-Volume I - Volume I
Visual recognition of multiagent action
Visual recognition of multiagent action
Helping Computer Vision by Verbal and Nonverbal Communication
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Context-specific independence in Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Evaluating Integrated Speech- and Image Understanding
ICMI '02 Proceedings of the 4th IEEE International Conference on Multimodal Interfaces
Combining speech and haptics for intuitive and efficient navigation through image databases
Proceedings of the 5th international conference on Multimodal interfaces
Vision systems with the human in the loop
EURASIP Journal on Applied Signal Processing
On the integration of grounding language and learning objects
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
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The realization of natural human-computer interfaces suffers from a wide range of restrictions concerning noisy data, vague meanings, and context dependence. An essential aspect of everyday communication is the ability of humans to ground verbal interpretations in visual perception. Thus, the system has to be able to solve the correspondence problem of relating verbal and visual descriptions of the same object. This contribution proposes a new and innovative solution to this problem using Bayesian networks. In order to capture vague meanings of adjectives used by the speaker, psycholinguistic experiments are evaluated. Object recognition errors are taken into account by conditional probabilities estimated on test sets. The Bayesian network is dynamically built up from verbal object description and is evaluated by an inference technique combining bucket elimination and conditioning. Results show that speech and image data is interpreted more robustly in the combined case than in the case of isolated interpretations.