Knowledge-Based Intelligent Information and Engineering by Cesar F. Caiafa, Emanuele Salerno, Araceli N. Proto (auth.),

By Cesar F. Caiafa, Emanuele Salerno, Araceli N. Proto (auth.), Bruno Apolloni, Robert J. Howlett, Lakhmi Jain (eds.)

This e-book is a part of a three-volume set that constitutes the refereed lawsuits of the eleventh foreign convention on Knowledge-Based clever details and Engineering structures, KES 2007, held in Vietri sul Mare, Italy, September 2007. The 409 revised papers offered have been rigorously reviewed and chosen from approximately 1,203 submissions. The papers current a wealth of unique examine effects from the sector of clever info processing within the broadest sense.

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Additional resources for Knowledge-Based Intelligent Information and Engineering Systems: 11th International Conference, KES 2007, XVII Italian Workshop on Neural Networks, Vietri sul Mare, Italy, September 12-14, 2007, Proceedings, Part III

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Four supplied classes (grit, vegetation, bricks, roads, from left to right) are shown in the top row of Fig. 3. This classification has been Blind Source Separation Applied to Spectral Unmixing 7 Fig. 3. Top, left to right: grit, vegetation, brick, road classes associated to our test 102band hyperspectral image. Middle: four fractional endmember abundances extracted by MaxNG with L2 -distance nongaussianity measure. Bottom: four fractional endmember abundances extracted by MaxNG with squared-skewness nongaussianity measure.

3) The functions gj , i = 1, 2, 3, are known in literature as the Shulman-Herve stabilizers, and their task is preserving edges during the reconstruction [13]. Unsupervised Blind Separation and Debluring of Mixtures of Sources 27 The regularization parameters λ2j , j = 1, 2, 3, reflect the confidence that we have in the data whereas qj , j = 1, 2, 3, have the meaning of threshold for creating a discontinuity on their respective sources sj . In the blind problem, both the sources and the mixing matrix are unknown.

We assume that the linear operators Hi and the variance of the noise are known. Let us consider first the non–blind problem, in which the mixing matrix A is known. Let C be the set of pairs of adjacent pixels in a vertical or in an horizontal way. A clique c is an element of C. We denote the finite difference operator of the generic vector y in the clique c by Dc y. A regularized solution is defined as the minimum of the following energy function: ⎛ ⎞ 2 3 EA (s) = m i − Hi ⎝ i=1 where gj (t) = 3 aij sj ⎠ 3 + j=1 ⎧ 2 ⎨t , ⎩ j=1 if |t| < qj 2q|t| − qj2 if |t| ≥ qj , λ2j gj (Dc (sj )), (2) c∈C j = 1, 2, 3.

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