Advances in Pattern Recognition: Second Mexican Conference by Pavel Herrera-Domínguez, Leopoldo Altamirano-Robles (auth.),

By Pavel Herrera-Domínguez, Leopoldo Altamirano-Robles (auth.), José Francisco Martínez-Trinidad, Jesús Ariel Carrasco-Ochoa, Josef Kittler (eds.)

This booklet constitutes the completely refereed court cases of the second one Mexican convention on development attractiveness, MCPR 2010, held in Puebly, Mexico, in September 2010. The 39 revised papers have been rigorously reviewed and chosen from 89 submissions and are equipped in topical sections on machine imaginative and prescient and robotics, snapshot processing, neural networks and sign processing, trend popularity, facts mining, normal language and record processing.

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It is necessary to start from a previously obtained skeleton before running our processing algorithms. Any already well-known thinning algorithm can be used. Starting from that thinned image, we reduce the skeleton to points (expressed as pixel coordinates), and later we proceed to reconstruct the skeleton. Our algorithm consists of 4 significant steps, namely, finding out straight line segments, finding out key points, joining key points and joining resulting line segments. 1 Finding Out Straight Line Segments The image is decomposed into straight line segments l, made up of consecutive pixels aligned in the same direction, containing a minimum of 2 pixels to represent a line segment.

That comparison allows to determinate if the line segments are 8connected. In this case, a new line segment is created from their union. Finally, the line segment is stored in L . Fig. 4e shows the way the key points have been joined trough the algorithm at this step. 4 Joining Resulting Line Segments This is a polishing step where we detect line segments that can join and become one, from among the line segments generated in the previous step, and similarly, two line segments will join if both are 8-connected and have the same slope, resulting one line segment made up of the two most distant points, from among the four points that characterize both line segments.

In the preliminary experiments the basic emotion recognition achieves up to 70-90% that depends on complexity in selection of AUs for representation of particular emotion and subjectivity of its perception by each person. The designed knowledge-base framework is general enough to create the diverse instances of emotions, as well as it provides quite exact quantitative description of measured facial actions. This permits simple and formal definition of relationship between emotions, facial actions, and their descriptors.

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