Fast Hough transform: A hierarchical approach
Computer Vision, Graphics, and Image Processing
Temporal reasoning based on semi-intervals
Artificial Intelligence
A survey on temporal reasoning in artificial intelligence
AI Communications
Automatically extracting highlights for TV Baseball programs
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Rule-based video classification system for basketball video indexing
MULTIMEDIA '00 Proceedings of the 2000 ACM workshops on Multimedia
Maintaining knowledge about temporal intervals
Communications of the ACM
Temporal representation and reasoning in artificial intelligence: Issues and approaches
Annals of Mathematics and Artificial Intelligence
An integrated baseball digest system using maximum entropy method
Proceedings of the tenth ACM international conference on Multimedia
A unified framework for semantic shot classification in sports videos
Proceedings of the tenth ACM international conference on Multimedia
Content-based video indexing of TV broadcast news using hidden Markov models
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 06
Temporal representation and reasoning in artificial intelligence: A review
Mathematical and Computer Modelling: An International Journal
Exploring the structure of media stream interactions for multimedia browsing
AMR'05 Proceedings of the Third international conference on Adaptive Multimedia Retrieval: user, context, and feedback
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Relations among temporal intervals can be used to detect semantic events in audio visual documents. The aim of our work is to study all the relations that can be observed between different segmentations of a same document. These segmentations are automatically provided by a set of tools. Each tool determines temporal units according to specific low or mid-level features. All this work is achieved without any prior information about the document type (sport, news ...), its structure, or the type of the semantic events we are looking for. Considering binary temporal relations between each couple of segmentations, a parametric representation is proposed. Using this representation, observations are made about temporal relation frequencies. When using relevant segmentations, some semantic events can be inferred from these observations. If they are not relevant enough, or if we are looking for semantic events of a higher level, conjunctions between two temporal relations can turn to be more efficient. In order to illustrate how observations can be made in the parametric representation, an example is given using Allen's relations. Finally, we present some first results of an experimental phase made on TV news programs.