By Benoit Huet, Alan F. Smeaton, Ketan Mayer-Patel, Yannis Avrithis
This ebook constitutes the refereed complaints of the fifteenth foreign Multimedia Modeling convention, MMM 2009, held in Sophia-Antipolis, France, in January 2009. The 26 revised complete papers and 20 revised poster papers offered including 2 invited talks have been conscientiously reviewed and chosen from one hundred thirty five submissions. The papers are prepared in topical sections on automatic annotation, coding and streaming, video semantics and relevance, audio, attractiveness, category and retrieval, in addition to question and summarization.
Read or Download Advances in Multimedia Modeling: 15th International Multimedia Modeling Conference, MMM 2009, Sophia-Antipolis, France, January 7-9, 2009. Proceedings. ... Applications, incl. Internet Web, and HCI) PDF
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Extra info for Advances in Multimedia Modeling: 15th International Multimedia Modeling Conference, MMM 2009, Sophia-Antipolis, France, January 7-9, 2009. Proceedings. ... Applications, incl. Internet Web, and HCI)
At B, if the trajectory is identiﬁed to be the player “Ito” by the recognition process, for example, the distribution is updated to increase his probability and diminish the others’ probabilities. As time elapses without further evidence of identity, the distribution ﬂattens (from B through C in Fig. 2). This behavior of oblivion is also implemented in the system. When the player come closer to another player, during the instants C upto D, the distribution is updated by mixing that of interfering player.
E. O’Connor C. O Fig. 1. The SenseCam: a prototype wearable camera. The bottom two rows show some typical SenseCam images. applications, such as the monitoring of people with age-dementia , a wearable visual monitor is a useful diagnostic tool, and knowledge of the wearer’s location could facilitate more eﬃcient browsing of a large repository of captured images or video. In this paper, we address the problem of estimating a person’s trajectory through a space, using only their SenseCam images.
We believe that knowledge of the user’s location will be a valuable additional aid to eﬃcient organisation of SenseCam images. 1 Feature Comparison Data Capture In order to perform a straight-forward comparison between the SIFT and SURF descriptors, SenseCam images of static scenes are captured by ﬁxing the camera in place and taking one image every 5-10 minutes. The interest-point descriptors found in each image of a single static scene should have unique corresponding points in other images of the scene.