By Chrysi Papalazarou, Peter M. J. Rongen, Peter H. N. de With (auth.), Jacques Blanc-Talon, Wilfried Philips, Dan Popescu, Paul Scheunders (eds.)
This e-book constitutes the refereed complaints of the eleventh foreign convention on complicated strategies for clever imaginative and prescient platforms, ACIVS 2009, held in Bordeaux, France in September/October 2009.
The forty three revised complete papers and 25 posters offered have been conscientiously reviewed and chosen from one hundred fifteen submissions. The papers are geared up in topical sections on technovision, primary mathematical concepts, photograph processing, coding and filtering, picture and video research, laptop imaginative and prescient, monitoring, colour, multispectral and special-purpose imaging, clinical 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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Extra info for Advanced Concepts for Intelligent Vision Systems: 11th International Conference, ACIVS 2009, Bordeaux, France, September 28–October 2, 2009. Proceedings
Section 3 discusses the evaluation methodology to compare the selected techniques describing the dataset, the quality metrics, and the selection of technique parameters. Section 4 shows the evaluation results. Finally, section 5 presents the conclusion of this work. 2 Selected Techniques In this section, we briefly describe the selected BGS techniques, classified according to the model representation, just in order to identify their most relevant parameters and implementation details that might diverge from the referenced work.
The paper is organized as follows: the next section presents the ﬁre regions in diﬀerent color spaces; section 3 describes the segmentation techniques used for 14 S. Rudz et al. ﬁre scenes; the evaluation method for segmentation quality measure is detailed in section 4; ﬁnally, experiments and evaluation results are presented in section 5. 2 Fire in Color Spaces As mentioned in previous section, the ﬁre segmentation methods use diﬀerent color spaces, but it is diﬃcult to know which one is better.
The purpose of this approach is to ﬁnd the projection axes that maximize the variance of the input space. xN ], and solving the eigen problem CΦ = ΦΛ leads to the computation of an orthogonal basis Φ that describes the face space in the diﬀerent variance directions Λ. A reduced projection space is obtained by keeping the axes with the maximum of variances (highest eigenvalues), and the projected vector on the reduced space is considered as the new representation. Biosecure Reference System[BioSecure].