Computer Vision Systems: 10th International Conference, ICVS by Lazaros Nalpantidis, Volker Krüger, Jan-Olof Eklundh,

By Lazaros Nalpantidis, Volker Krüger, Jan-Olof Eklundh, Antonios Gasteratos

This booklet constitutes the refereed court cases of the tenth overseas convention on desktop imaginative and prescient platforms, ICVS 2015, held in Copenhagen, Denmark, in July 2015. The forty eight papers awarded have been rigorously reviewed and chosen from ninety two submissions. The paper are equipped in topical sections on organic and cognitive imaginative and prescient; hardware-implemented and real-time imaginative and prescient structures; high-level imaginative and prescient; studying and version; robotic imaginative and prescient; and imaginative and prescient platforms applications.

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Extra info for Computer Vision Systems: 10th International Conference, ICVS 2015, Copenhagen, Denmark, July 6-9, 2015, Proceedings

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16 C. Hilario Gomez et al. related to our contribution are presented. Next, each of our proposed descriptors are explained in detail. Finally, the bag-of-words approach used for the scene categorization task is introduced. 1 Retinal Ganglion Cells Configuration In FREAK [1], the sampling grid shown in Fig. 1 is proposed. Each circle corresponds to a receptive field of a ganglion cell and its size represents the standard deviation of the Gaussian kernels applied to the underlying sampling point. However, the Gaussian smoothing applied to each receptive field is approximated calculating the mean intensity.

The final termination predicate is defined as 38 T. Werner et al. Ext(ht , vi ) > min k, Int(ht ) + k t+1 , (3) which compares the external dissimilarity to the next vertex with current maximal external dissimilarity. As in [4], parameter k regulates how much extra internal variability is accepted within the candidates; the less segments the candidate has, the more influence k has. If the predicate holds, the vertex vi is rejected and the growing process stops. Otherwise, the vertex is accepted and added to the current hypothesis.

CS-FREAK: an improved binary descriptor. , Huang, K. ) IGTA 2014. CCIS, vol. 437, pp. 129–136. Springer, Heidelberg (2014) 15. : Efficient action recognition with MoFREAK. In: Proceedings of the 2013 International Conference on Computer and Robot Vision, pp. 319–325. IEEE Computer Society (2013) 16. : Model and large-scale simulator of a biological retina with contrast gain control. D. thesis, University of Nice Sophia-Antipolis (2008) 17. : Centrist: a visual descriptor for scene categorization.

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