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Titlebook: Discrete Geometry for Computer Imagery; 21st IAPR Internatio Michel Couprie,Jean Cousty,Nabil Mustafa Conference proceedings 2019 Springer

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https://doi.org/10.1007/978-3-030-10448-1nequality. In this paper, continuing the research, we provide a sufficient condition for weight sequences to provide metric distance. Further, we present an algorithm to compute the distance transform based on these distances. Optimization results are also shown for the approximation of the Euclidea
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Computer Architecture in the 1990sompose with itself across multiple contractions. The paper concludes with experiments demonstrating the second operator’s utility on manifolds and a brief discussion of future directions for research.
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Vladimir Voevodin,Sergey Sobolev in these intervals. We use the BSDS dataset to assess our observation scale selection methods. The experiments show that some of these strategies lead to better segmentation results than the ones obtained with the original HGB method.
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Ilya V. Afanasyev,Dmitry I. Lichmanovelineation methods using the segmentation by a deep neural network as baseline, and shows the potential of a new paradigm, namely ., to learn object information from the target image only during delineation.
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