Download Computer Vision/Computer Graphics CollaborationTechniques: by Davide Moschini, Andrea Fusiello (auth.), André Gagalowicz, PDF

By Davide Moschini, Andrea Fusiello (auth.), André Gagalowicz, Wilfried Philips (eds.)

This ebook constitutes the refereed complaints of the 4th overseas convention on laptop Vision/Computer snap shots Collaboration options, MIRAGE 2009, held in Rocquencourt, France, in could 2009. The forty-one revised complete papers offered have been conscientiously reviewed and chosen from a complete of eighty three submissions. The papers hide quite a lot of themes with specialize in desktop Vision/Computer pix collaboration ideas related to photo analysis/synthesis ways specially referring to theoretical, computational, experimental or commercial elements of model-based photo research and image-based version synthesis.

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Extra info for Computer Vision/Computer Graphics CollaborationTechniques: 4th International Conference, MIRAGE 2009, Rocquencourt, France, May 4-6, 2009. Proceedings

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Specular surface inspection using structured highlight and Gaussian images. IEEE Transactions on Pattern Analysis And Machine Intelligence 10(1), 208–218 (1990) 15. : 3D surface estimation and model construction from specular motion in image sequences. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(5), 513–520 (1997) 16. : 3D Acquisition of Mirroring Objects using Striped Patterns. Graphical Models 67, 233–259 (2005) 17. : Image-Based Lighting. IEEE Computer Graphics and Applications 22(2), 26–34 (2002) 18.

5 Conclusions In this paper, we introduced a simple and fast 3D mesh denoising method using the concept of multivariate kernel density estimation. The main idea behind our proposed approach is to use a regularized bandwidth matrix of the kernel density in order to avoid over-smoothing and to fully preserve the geometric structure of the 3D mesh data, while effectively removing undesirable noise. The experimental results showed that our proposed method outperforms existing mesh denoising techniques.

6 λ= 0. 9 λ= 0. 8 λ= 0. 7 λ= 0. 6 λ= 0. 5 0 λ= 0. 4 0 (b) Fig. 9. Visual error vs. 3 Choice of the Regularization Parameter The regularization parameter should be tuned to be small enough to capture the intrinsic shape of a 3D object and large enough not to recapture noise. This parameter may be estimated experimentally using the visual error as shown in Fig. 9(a) and Fig. 9(b), which display the plots of the visual error vs. the regularization parameter for different iteration numbers of the proposed approach.

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