Download Discrete Geometry for Computer Imagery: 12th International by Valentin Gies, Thierry M. Bernard (auth.), Eric Andres, PDF

By Valentin Gies, Thierry M. Bernard (auth.), Eric Andres, Guillaume Damiand, Pascal Lienhardt (eds.)

ISBN-10: 3540255133

ISBN-13: 9783540255130

ISBN-10: 3540319654

ISBN-13: 9783540319658

This publication constitutes the refereed court cases of the twelfth overseas convention on Discrete Geometry for desktop Imagery, DGCI 2005, held in Poitiers, France in April 2005.

The 36 revised complete papers awarded including an invited paper have been rigorously reviewed and chosen from fifty three submissions. The papers are prepared in topical sections on purposes, discrete hierarchical geometry, discrete tomography, discrete topology, item homes, reconstruction and popularity, doubtful geometry, and visualization.

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Extra info for Discrete Geometry for Computer Imagery: 12th International Conference, DGCI 2005, Poitiers, France, April 13-15, 2005. Proceedings

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Arqu`es, and S. Michelin. Thinning grayscale well-composed images. Pattern Recognition Letters, 25:581–590, 2004. [12] F. Meyer. Topographic distance and watershed lines. Signal Processing, (38):113– 125, 1994. [13] A. Montanvert, P. Meer, and A. Rosenfeld. Hierarchical image analysis using irregular tessellations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(4):307–316, APRIL 1991. [14] L. Najman and M. Couprie. Watershed algorithms and contrast preservation. In Discrete geometry for computer imagery, volume 2886, pages 62–71.

8. S. T. Pfister, S. I. Roumeliotis, and J. W. Burdick. Weighted line fitting algorithms for mobile robot map building and efficient data representation. In ICRA, 2003. 9. T. R¨ ofer. Using histogram correlation to create consistent laser scan maps. In Proceedings of the IEEE International Conference on Robotics Systems (IROS2002), 2002. 10. S. Thrun. Robotic mapping: A survey. In G. Lakemeyer and B. Nebel, editors, Exploring Artificial Intelligence in the New Millenium. Morgan Kaufmann, 2002. 11. D.

D. (1983). fr Abstract. Watershed is one of the most popular tool defined by mathematical morphology. The algorithms which implement the watershed transform generally produce an over segmentation which includes the right image’s boundaries. Based on this last assumption, the segmentation problem turns out to be equivalent to a proper valuation of the saliency of each contour. Using such a measure, hierarchical watershed algorithms use the edge’s saliency conjointly with statistical tests to decimate the initial partition.

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Discrete Geometry for Computer Imagery: 12th International Conference, DGCI 2005, Poitiers, France, April 13-15, 2005. Proceedings by Valentin Gies, Thierry M. Bernard (auth.), Eric Andres, Guillaume Damiand, Pascal Lienhardt (eds.)


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