By Chrysi Papalazarou, Peter M. J. Rongen, Peter H. N. de With (auth.), Jacques Blanc-Talon, Wilfried Philips, Dan Popescu, Paul Scheunders (eds.)
This booklet constitutes the refereed court cases of the eleventh foreign convention on complicated innovations for clever imaginative and prescient structures, ACIVS 2009, held in Bordeaux, France in September/October 2009.
The forty three revised complete papers and 25 posters awarded have been rigorously reviewed and chosen from a hundred and fifteen submissions. The papers are prepared in topical sections on technovision, basic mathematical options, photograph processing, coding and filtering, photograph and video research, desktop imaginative and prescient, monitoring, colour, multispectral and special-purpose imaging, scientific imaging, and biometrics.
Read or Download Advanced Concepts for Intelligent Vision Systems: 11th International Conference, ACIVS 2009, Bordeaux, France, September 28–October 2, 2009. Proceedings PDF
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Additional info for Advanced Concepts for Intelligent Vision Systems: 11th International Conference, ACIVS 2009, Bordeaux, France, September 28–October 2, 2009. Proceedings
Mellakh et al. LDA based on intensity image[IBISC-3]. The face projection space was built using 156 face images from 52 subjects of the IV2 developpment part. All computed axes are used to reduce the dimensionality of the input space. For all the classes in the sample space,, two kinds of measures are deﬁned, the within-class scatter matrix: c Nj (xji − μj )(xji − μj )T Sw = (1) j=1 i=1 with xji the ith sample of class j, μj the mean of class j, c the number of classes and Nj the number of all training samples of class j.
625–638. Springer, Heidelberg (2003) 9. : View-based and modular eigenspaces for face recognition. In: CVPR, pp. 84–91 (1994) 10. ): Using discriminant eigenfeatures for image retrieval. IEEE Transactions on PAMI 18(8), 831–836 (1996) 11. : Capitalize on dimensionality increasing techniques for improving face recognition grand challenge performance. IEEE Transactions PAMI 28(5), 725– 737 (2006) 12. : Face veriﬁcation using gabor wavelets and adaboost. In: ICPR, pp. I:404–I:407 (2006) 13. : Guide to Biometric Reference Systems and Performance Evaluation.
All frame sizes are 720x576. 2 Performance Evaluation Metrics In order to evaluate the selected techniques, we have selected the precision ( P ) and recall ( R ) measures for foreground ( P 1, R1 ) and background ( P 0, R 0 ) detection: P 0 = TN / (TN + FN ) , R 0 = TN / (TN + FP ) (1) P 1 = TP / (TP + FP ) , R1 = TP / (TP + FN ) , where TP indicates the number of foreground pixels correctly detected, TN the number of background ones correctly detected, FP the number of foreground pixels wrongly detected as background and FN the number of background ones wrongly detected as foreground.