Engineering

Knowledge Science, Engineering and Management: 9th by Franz Lehner, Nora Fteimi

By Franz Lehner, Nora Fteimi

This ebook constitutes the refereed court cases of the ninth overseas convention on wisdom technological know-how, Engineering and administration, KSEM 2016, held in Passau, Germany, in October 2016.

The forty nine revised complete papers offered including 2 keynotes have been rigorously chosen and reviewed from 116 submissions. The papers are geared up in topical sections on Clustering and type; textual content Mining and Lexical research; content material and rfile research; firm wisdom; Formal Semantics and Fuzzy common sense; wisdom Engineering; wisdom Enrichment and Visualization; wisdom administration; wisdom Retrieval; wisdom structures and protection; Neural Networks and synthetic Intelligence; Ontologies; and suggestion Algorithms and platforms.

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Light field photography with a hand-held plenoptic camera. Computer Science Technical Report CSTR, vol. 2, no. 11, pp. 1–11 (2005) 12. : Saliency detection via cellular automata. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 110–119 (2015) 13. : Salient region detection and segmentation. K. ) ICVS 2008. LNCS, vol. 5008, pp. 66–75. Springer, Heidelberg (2008). 1007/978-3-540-79547-6 7 14. : Frequency-tuned salient region detection. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp.

Definition 1 (Redundant tuples). The tuples δx for ∀x ∈ X are redundant if H(δ) < τ , where τ ≥ 0 is a threshold defining the minimal information gain. Given the n-tuples tree, the pruning process begins by examining the leaf nodes according to Definition 1; here, the symbols corresponding to the leaf and its siblings are considered as ∀x ∈ X in the senses of Eq. (1). Once they are identified being redundant, the leaves are deleted and their parent node changes to the new leaf. The new leaves are then re-scanned to search for the redundant subsequences of shorter length.

3 Background Measure We observe that foreground and background regions in natural images and datasets are quite different. Foreground region tends to be in a small depth and in the front of the image, on the contrary, background regions tend to be in a high depth value and far from the image. It is intuitive cognition to the foreground and background and we considered the occluded region as a background point and occlude another region as a foreground point. To accurately measure background regions and improve computational efficiency, an input image is segmented into N small superpixels by the simple linear iterative clustering (SLIC) algorithm [35].

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