By Luc De Raedt (auth.), Gerson Zaverucha, Augusto Loureiro da Costa (eds.)
This e-book constitutes the refereed lawsuits of the nineteenth Brazilian Symposium on synthetic Intelligence, SBIA 2008, held in Salvador, Brazil, in October 2008.
The 27 revised complete papers offered including three invited lectures and three tutorials have been rigorously reviewed and chosen from 142 submissions. The papers are geared up in topical sections on laptop imaginative and prescient and development attractiveness, allotted AI: independent brokers, multi-agent structures and online game wisdom illustration and reasoning, computing device studying and knowledge mining, traditional language processing, and robotics.
Read or Download Advances in Artificial Intelligence - SBIA 2008: 19th Brazilian Symposium on Artificial Intelligence Savador, Brazil, October 26-30, 2008. Proceedings PDF
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Extra resources for Advances in Artificial Intelligence - SBIA 2008: 19th Brazilian Symposium on Artificial Intelligence Savador, Brazil, October 26-30, 2008. Proceedings
In this context, texture is one of the most important visual characteristics when deﬁning similarity among images. Texture is deﬁned as the repetition of a certain atomic pattern (or texton) residing in a region. For a low-level image analysis, texture features play a very important role in distinguishing textured regions from one another based on the measurement of optical homogeneity of surfaces. In the 1970’s, Haralick et al.  proposed the ﬁrst systematic analysis of texture by using a co-occurrence matrix, which describes the distribution of co-occurring pixel values at a given oﬀset.
An overview of the speech driven facial animation system is presented in section 2. The AV-HMM is introduced in section 3, where an HMMI algorithm for the general case of considering full covariance matrices for the audio-visual observations is also derived. In section 4, the proposed algorithm for feature extraction is described. The MPEG-4 compliant facial animation technique is presented in section 5. Experimental results and some concluding remarks are included in sections 6 and 7, respectively.
3(d), show the AMSE and the ACC as a function of the number of states and the number of mixtures for an AV-HMM with diagonal covariance matrix. In this case, equation (8) applies for the estimation of the visual observations ovt . As can be observed, to obtain a similar accuracy a more complex model (larger number of states or mixtures) is required. 76.
Advances in Artificial Intelligence - SBIA 2008: 19th Brazilian Symposium on Artificial Intelligence Savador, Brazil, October 26-30, 2008. Proceedings by Luc De Raedt (auth.), Gerson Zaverucha, Augusto Loureiro da Costa (eds.)