Read e-book online Advanced Concepts for Intelligent Vision Systems: 17th PDF

By Jacques Blanc-Talon, Cosimo Distante, Wilfried Philips, Dan Popescu, Paul Scheunders

This booklet constitutes the refereed complaints of the seventeenth foreign convention on complex options for clever imaginative and prescient structures, ACIVS 2016, held in Lecce, Italy, in October 2016.
The sixty four revised complete papers awarded during this quantity have been conscientiously chosen from 137 submissions. They take care of classical low-level photograph processing ideas; photo and video compression; 3D; defense and forensics; and evaluate methodologies.

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Read Online or Download Advanced Concepts for Intelligent Vision Systems: 17th International Conference, ACIVS 2016, Lecce, Italy, October 24-27, 2016, Proceedings PDF

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Extra info for Advanced Concepts for Intelligent Vision Systems: 17th International Conference, ACIVS 2016, Lecce, Italy, October 24-27, 2016, Proceedings

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1, but without contour smoothing in the cartesian coordinate system. For the remainder of this paper this approach is referred to as intensity-based segmentation. In doing this we have allowed comparison with a purely data-driven approach which had no shape regularisation, and to a completely different segmentation approach. Evaluation was performed on a 2D slice-by-slice basis in polar coordinates. The point-to-mesh distance (PMD), Fig. 3. Example segmentation results. From left to right; 1st column: Image slices.

For fair comparison our results were only compared to other methods working on the same dataset. To our knowledge, Essa et al. [3] are the only others to do this, and so we compared our results to their minimum s-excess graph segmentation. This involved formulating a graph to segment both inner and outer walls simultaneously in polar coordinates, and a hidden Markov model was used to track the vessel walls between the columns. Secondly, a simple intensity-based approach was implemented using Haar-like filtering.

Schneider et al. model in the sense of Brinkmann et al. [3] is not promising for compression. In our future work, we concentrate on models that combine gradient data and grey value data in order to unify the advantages of both. References 1. : How to choose interpolation data in images. SIAM J. Appl. Math. 70(1), 333–352 (2009) 2. : Image inpainting. In: Proceedings of the SIGGRAPH 2000, New Orleans, LI, pp. 417–424, July 2000 3. : Regularization with sparse vector fields: from image compression to TV-type reconstruction.

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