By Jamal Saboune, Mehdi Arezoomand, Luc Martel, Robert Laganiere (auth.), Giuseppe Maino, Gian Luca Foresti (eds.)

The two-volume set LNCS 6978 + LNCS 6979 constitutes the lawsuits of the sixteenth overseas convention on snapshot research and Processing, ICIAP 2011, held in Ravenna, Italy, in September 2011.
The overall of 121 papers awarded was once conscientiously reviewed and chosen from one hundred seventy five submissions. The papers are divided into 10 oral periods, comprising forty four papers, and 3 publish periods, comprising seventy seven papers. They care for the next issues: snapshot research and illustration; picture segmentation; development research and category; forensics, safeguard and rfile research; video research and processing; biometry; form research; low-level colour snapshot processing and its functions; scientific imaging; picture research and trend reputation; picture and video research and processing and its applications.

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Extra info for Image Analysis and Processing – ICIAP 2011: 16th International Conference, Ravenna, Italy, September 14-16, 2011, Proceedings, Part II

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Computer Methods and Programs in Biomedicine 27(1), 1–8 (1994) 2. : Scale space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis, 629–639 (March 1990) 3. : Image selective smoothing and edge detection by nonlinear diffusion. Journal of Numerical Analysis 29, 845–866 (1992) 4. : Digital picture processing - an introduction. Springer, Heidelberg (1985) 5. : Bilateral filtering for gray and color images. In: Proceedings of the Sixth International Conference on Computer Vision, pp.

This paper presents an initial study which uses graphs to represent the actor’s shape and graph embedding to then convert the graph into a suitable feature vector. In this way, we can benefit from the wide range of statistical classifiers while retaining the strong representational power of graphs. The paper shows that, although the proposed method does not yet achieve accuracy comparable to that of the best existing approaches, the embedded graphs are capable of describing the deformable human shape and its evolution along the time.

Among different methods, the probabilistic graph edit distance (P-GED) proposed by Neuhaus and Bunke [10], [11] was chosen to automatically find the cost function from a labeled sample set of graphs. To this aim, the authors represented the structural similarity of two graphs by a learned probability p(g1 , g2 ) and defined the dissimilarity measure as: d(g1 , g2 ) = − log p(g1 , g2 ) (2) The main advantage of this model is that it learns the costs of edit operations automatically and is able to cope with large sets of graphs with huge distortion between samples of the same class [10], [11].

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