By Franz Lehner, Nora Fteimi

This ebook constitutes the refereed lawsuits of the ninth overseas convention on wisdom technology, Engineering and administration, KSEM 2016, held in Passau, Germany, in October 2016.

The forty nine revised complete papers awarded including 2 keynotes have been conscientiously chosen and reviewed from 116 submissions. The papers are equipped in topical sections on Clustering and category; textual content Mining and Lexical research; content material and rfile research; company wisdom; Formal Semantics and Fuzzy common sense; wisdom Engineering; wisdom Enrichment and Visualization; wisdom administration; wisdom Retrieval; wisdom structures and safety; Neural Networks and synthetic Intelligence; Ontologies; and suggestion Algorithms and structures.

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Extra resources for Knowledge Science, Engineering and Management: 9th International Conference, KSEM 2016, Passau, Germany, October 5-7, 2016, Proceedings

Sample text

3) and prune the n-tuples tree based on Definition 1; (3) Collect the variable-length tuples from the pruned tree, and represent each sequence s ∈ S by the vector V s according to Eq. (2) and Definition 2; (4) Choose K vectors for the initial cluster centers using Eqs. 1) Generate c1 , c2 , . . 2) Recompute the center for each cluster ck by averaging the vectors belonging to ck , where k = 1, 2, . . , K. ; end Algorithm 1. Outline of the KM-NVLT algorithm. The time complexity for generating the variable-length tuples is O(n × |X| × L), where L is the total lengths of the sequences in S.

Bottom-up approaches usually exploit low-level cues such as feature, colors and spatial distances to construct saliency maps. Itti [17] propose a saliency model based on the neural network which integrates three feature. Harel [18] propose a graph-based saliency measure. In [19–21], a saliency models based on Bayesian has been proposed which 30 H. Sheng et al. is exploiting low and mid level cues. Sun et al. [22] improve the Xie’s model by introducing boundary and soft segmentation. Perrazzi [23] propose a contrastbased saliency filter and calculate saliency detection by uniqueness and spatial distribution of the patch on the image.

Comparison of our saliency maps with state-of-the-art methods 6 Conclusion We propose a bottom-up method to detect salient regions in light field images through cellular automata method, which incorporates occlusion relationship cues and background and foreground priors. The occlusion relationship is calculated on the raw images and is showed that it is simple and effective. Based on the EPI images, we can obtain the occlusion relationship and use this cues to calculate the background and foreground.

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