A tutorial on learning with Bayesian networks
Learning in graphical models
Detecting Faces in Images: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparison of different implementations of MFCC
Journal of Computer Science and Technology
Learning Dynamic Bayesian Networks
Adaptive Processing of Sequences and Data Structures, International Summer School on Neural Networks, "E.R. Caianiello"-Tutorial Lectures
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Modeling Individual and Group Actions in Meetings: A Two-Layer HMM Framework
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 7 - Volume 07
A meeting browser evaluation test
CHI '05 Extended Abstracts on Human Factors in Computing Systems
The AMI meeting corpus: a pre-announcement
MLMI'05 Proceedings of the Second international conference on Machine Learning for Multimodal Interaction
Multimodal integration for meeting group action segmentation and recognition
MLMI'05 Proceedings of the Second international conference on Machine Learning for Multimodal Interaction
Browsing recorded meetings with ferret
MLMI'04 Proceedings of the First international conference on Machine Learning for Multimodal Interaction
Using audio, visual, and lexical features in a multi-modal virtual meeting director
MLMI'06 Proceedings of the Third international conference on Machine Learning for Multimodal Interaction
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In this work we present a multi-modal video editing system for meetings, which uses graphical models for the segmentation and classification of the video modes. The task of video editing is about selecting the camera, that represents the meeting in the best way out of various available cameras. Therefore a new training structure for graphical models was developed. This is necessary for the learning of boundaries combined with the video mode itself. All developed and known decoding structures can be easily connected for an EM-training to our training structure. The achieved results of the system are state of the art.