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41#
發(fā)表于 2025-3-28 14:35:03 | 只看該作者
Studies in Behavioural Adaptationce characteristics. Graphs are well adapted to represent such information. Sequential methods for knowledge-based recognition of structures require to define in which order the structures have to be recognized. We propose to address this problem of order definition by developing algorithms that auto
42#
發(fā)表于 2025-3-28 19:27:37 | 只看該作者
43#
發(fā)表于 2025-3-29 02:13:44 | 只看該作者
44#
發(fā)表于 2025-3-29 03:54:46 | 只看該作者
45#
發(fā)表于 2025-3-29 07:50:45 | 只看該作者
https://doi.org/10.3920/978-90-8686-712-7the spatially varying nature of most images, which are much better modeled as piecewise smooth. This paper advocates the concept of local influence neighbourhoods (LINs). The influence neighbourhood of a pixel is defined as the set of neighbouring pixels which have a causal influence on it. LINs can
46#
發(fā)表于 2025-3-29 14:16:52 | 只看該作者
Modelling of Complex Signals in Nerves pixel lattice using a weighted undirected graph. The edge weights of the graph are determined by the Gaussian weighted distances between local neighbouring windows. We then compute the associated Laplacian matrix (the degree matrix minus the adjacency matrix). Anisotropic diffusion across this weig
47#
發(fā)表于 2025-3-29 18:36:21 | 只看該作者
48#
發(fā)表于 2025-3-29 20:51:41 | 只看該作者
49#
發(fā)表于 2025-3-30 01:35:48 | 只看該作者
Matching of Tree Structures for Registration of Medical Images placing landmarks in the images. In this paper we propose a method which is able to find reasonable landmarks automatically. To achieve this, nodes of the vessel systems, which have been extracted from the images by a segmentation algorithm, will be assigned by the so-called association graph metho
50#
發(fā)表于 2025-3-30 04:41:47 | 只看該作者
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