By Lazaros Nalpantidis, Volker Krüger, Jan-Olof Eklundh, Antonios Gasteratos
This e-book constitutes the refereed complaints of the tenth foreign convention on laptop imaginative and prescient structures, ICVS 2015, held in Copenhagen, Denmark, in July 2015. The forty eight papers provided have been rigorously reviewed and chosen from ninety two submissions. The paper are prepared 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 variation; robotic imaginative and prescient; and imaginative and prescient platforms applications.
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Additional resources for Computer Vision Systems: 10th International Conference, ICVS 2015, Copenhagen, Denmark, July 6-9, 2015, Proceedings
37 %). Moreover, when the cell activiy is thresholded the classiﬁcation accuracy is improved. 19 %. 78 % of correct classiﬁcations. 4 Conclusions The goal of this work was to implement a bio-inspired descriptor, mimicking some functionalities of the visual system. From biology, it is well known that the retina extracts details from images using a Diﬀerence of Gaussians (DoG) of diﬀerent sizes and encodes such diﬀerences with action potentials. We have presented a set of modiﬁcations to FREAK which are more biologically inspired.
70 In this table we can observe that eliminating the less discriminative pairs from the descriptor increases the performance. Best results are obtained when the size is reduced to 256 bytes. e. 64 bytes). In addition to this, in all our experiments the scale and orientation normalization is not used, since we are using a dense grid and not a keypoint detector as in the original idea. The confusion matrix from one run of the DescrC S ON OF F T h descriptor is shown in Fig. 3, where row names are true labels and column names are the predicted ones.
Intell. 32(5), 815–830 (2010) 11. : Dense interest points. In: The Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2010, pp. 2281–2288, San Francisco, CA, USA, 13–18 June 2010 (2010) 12. : VLFeat: an open and portable library of computer vision algorithms (2008). org/ 13. : Improving texture categorization with biologically inspired ﬁltering. Image Vis. Comput. 32, 424–436 (2013) 14. : CS-FREAK: an improved binary descriptor. , Huang, K. ) IGTA 2014. CCIS, vol. 437, pp.