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In the automatic analysis of a tennis game, it is important to detect some anomalous match events, such as "fault serve" and "ball out", as these events are crucial in understanding the progress of a game. Audio information can be used to detect these events, but it is unreliable, because of the acoustic mismatch between the training and the test data and interfering noise caused...
A mathematical morphology based filter structure called a sieve is used to process mouth image sequences of a talker's mouth and form visual speech features. The effects of varying the type of filter, the post-processing and hidden Markov model (HMM) parameters on recognition accuracy are investigated using two audio-visual speech databases.
In this paper we describe a framework to improve the detection of ball hit events in tennis games by combining audio and visual information. Detection of the presence and timing of these events is crucial for the understanding of the game. However, neither modality on its own gives satisfactory results: audio information is often corrupted by noise and also suffers from acoustic mismatch between the...
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