By Sebastiano Battiato
This booklet constitutes the completely refereed court cases of the sixteenth foreign convention on complicated suggestions for clever imaginative and prescient structures, ACIVS 2015, held Catania, Italy, in October 2015. The seventy six revised complete papers have been rigorously chosen from 129 submissions.
Read or Download Advanced Concepts for Intelligent Vision Systems: 16th International Conference, ACIVS 2015, Catania, Italy, October 26-29, 2015. Proceedings PDF
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Extra resources for Advanced Concepts for Intelligent Vision Systems: 16th International Conference, ACIVS 2015, Catania, Italy, October 26-29, 2015. Proceedings
LNCS, vol. 7944, pp. 11–21. Springer, Heidelberg (2013) 16. : Surface dependent representations for illumination insensitive image comparison. PAMI 29, 98–111 (2007) 17. : Image descriptors based on curvature histograms. , Koch, R. ) GCPR 2014. LNCS, vol. 8753, pp. 239–249. Springer, Heidelberg (2014) 18. : A new perceptual edge detector in color images. In: Advanced Concepts for Intelligent Vision Systems (2010) 19. : Texture removal by pixel classiﬁcation using a rotating ﬁlter. In: ICASS (2011) 20.
In: 2012 21st International Conference on Pattern Recognition (ICPR), Tsukuba, Japan, November 2012 10. : A dataset for quality assessment of camera captured document images. In: Proceedings of the 2013 Camera-Based Document Analysis and Recognition, CBDAR 2013, Washington, DC, USA, pp. 113–125 (2013) 11. : A no-reference perceptual blur metric. In: Proceedings of the 2002 International Conference on Image Processing, vol. 3, pp. III-57–III-60 (2002) 12. : Automated image quality assessment for camera-captured ocr.
Edges play the most important role in the ringing suppression problem because ringing eﬀect is the most noticeable near strong edges. We have also discovered that some blocks with edges are not represented well enough in the testing phase due to absence of edges with approximately the same direction in the training set. In order to improve the performance for these blocks we extend the dictionaries by their rotated versions after the learning process. Denote the Rota as the operator that rotates all atoms in the dictionary by a◦ , then the extended dictionaries are built as follows (here Dsbase , Drbase are the dictionaries obtained in the dictionary learning phase): Ds = Dsbase ; Rot90 (Dsbase ); Rot180 (Dsbase ); Rot270 (Dsbase ) Dr = Drbase ; Rot90 (Drbase ); Rot180 (Drbase ); Rot270 (Drbase ) We use only these three rotations (90◦ , 180◦ , 270◦ ) because they can be applied to the square images without introducing interpolation errors.