By Benoit Huet, Alan F. Smeaton, Ketan Mayer-Patel, Yannis Avrithis
This publication constitutes the refereed court cases 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 a hundred thirty five submissions. The papers are geared up in topical sections on computerized annotation, coding and streaming, video semantics and relevance, audio, attractiveness, type and retrieval, in addition to question and summarization.
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Extra resources 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)
Let x ˆ(s|sai ) be the optimal estimate at one of children sai of node s and vsai , the sub-tree rooted at sai , and P (s|sai ) for the corresponding error covariance. Ks x ˆ(s|s−) = P (s|s−) P −1 (s|sai )ˆ x(s|sai ) (15) i=1 P −1 (s|s−) = Px−1 (s) + Ks [P −1 (s|sai ) − Px−1 (s)] (16) i=1 Error covariance matrix P (s|sai ) indicates the distribution of attention weight on salient features at the given resolution. This matrix is kept for the later coarse-to-ﬁne smoothing. To avoid noise incurred by signal interpolation , we regard every layer in the MAR tree as an individual Markov process and limit the scope of recursive smoothing.
Of course, a number of technological solutions are available for localisation, such as GPS , GSM  and RF-based localisation using base-station signal strength . It is not always desirable or even possible to use these technologies. For example, GPS localisation is unreliable indoors; GSM or RF-based localisation may not support localisation to a ﬁne granularity, required for some applications. The SenseCam could, for example, replace the traditional audioguide used in many museums .
Table 1. Static scene comparison of SIFT and SURF. Rows show ﬁgures for Stability and Average Precision. Columns show SIFT and 4 SURF variants. the SIFT descriptor is a 128-dimensional vector. The number beside each SURF descriptor indicates the size of the descriptor and USURF is a non-rotationally-invariant version of SURF. Since all SURF variants use the same detector, their stability scores are equal. USURF64 performs best in these tests overall. 9970 Table 2. Severe lighting changes: Comparison of SIFT and SURF for feature matching.
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) by Benoit Huet, Alan F. Smeaton, Ketan Mayer-Patel, Yannis Avrithis