By Ed. by Jairo GutiÃ©rrez.
The expanding enterprise use of instant and cellular applied sciences on quite a few units has sped up the necessity for a greater figuring out of the applied sciences concerned. company information Communications and Networking: A examine viewpoint addresses the major concerns for companies using info communications and the expanding significance of networking applied sciences in enterprise. enterprise info Communications and Networking: A study viewpoint covers a chain of technical advances within the box whereas highlighting their respective contributions to enterprise or organizational ambitions, and facilities at the problems with network-based functions, mobility, instant networks, and community defense.
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Additional info for Business Data Communications and Networking: A Research Perspective
In order to extract all skin like regions, the region growing process has to be repeated for all unvisited pixels in an image. All the skin like regions are then filtered on a minimum surface criterion. Indeed, a skin like region is considered as skin region only when its area represents more than λ % of the original image. The images of the last column in Figure 5 illustrate the result of such a process. As we can see, small skin-like regions are filtered after the region growing based segmentation process.
42 Hammami, Chahir, & Chen Figure10. Gaussian curves of skin color pixel percentage (left for normal Web sites and right for pornographic ones) 1,2 1 0,8 0,6 0,4 0,2 0 0 5 10 15 20 25 30 35 40 45 Random Error Rate Method According to the random error rate method, MYL learning dataset was divided into two subsets: the first one for learning, composed roughly of 70% samples from the MYL learning dataset, the other one for testing composed of the remaining samples.
Color images (left) and their corresponding skin binary mask (right) 30 hours of various video sources (Karpova, Tsishkou, & Chen, 2003). These 1,110 skincolor images cover five races, two sexes, exterior/interior, and day/night conditions. They were manually segmented for skin binary mask, as illustrated in Figure 4, discriminating skin pixels from the non skin ones. Data Preparation and Data Mining Based Learning Let the set of pixels Ω be extracted and pre-processed automatically from training images and corresponding binary masks.