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Titlebook: Computational Modeling of Objects Presented in Images. Fundamentals, Methods, and Applications; 6th International Co Reneta P. Barneva,Vale

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樓主: broach
41#
發(fā)表于 2025-3-28 17:22:33 | 只看該作者
List of symbols and abbreviations,nt strategies for choosing a starting image, and thus we develop variants of the SA method for strip constrained binary tomography. We evaluate the different approaches on images with varying densities of object pixels.
42#
發(fā)表于 2025-3-28 20:33:26 | 只看該作者
List of symbols and abbreviations,a restricted depth data set, depending on the tasks complexity. While the sample size is small, we can conclude that pre-trained DL descriptors are the most descriptive, but not by a statistically significant margin and therefore part-based descriptors are still a viable option for small, but difficult 3D data sets.
43#
發(fā)表于 2025-3-28 22:58:15 | 只看該作者
List of symbols and abbreviations,est the theoretic setup on simulated data by reconstructing phantom images from simulated projections and compare the results to reconstructions from classical X-ray projections. We show that using decomposed projections can lead to better results from 20 times less number of projections than the classical X-Ray tomography.
44#
發(fā)表于 2025-3-29 04:43:26 | 只看該作者
45#
發(fā)表于 2025-3-29 09:17:44 | 只看該作者
46#
發(fā)表于 2025-3-29 15:20:11 | 只看該作者
47#
發(fā)表于 2025-3-29 16:17:44 | 只看該作者
List of symbols and abbreviations,measures namely, sensitivity or true positive rate (TPR), specificity or false negative rate (SPC) and recognition accuracy (ACC). Our experimental outcome for the present setup is two-fold: (i) CC view performs better then MLO for mammogram mass classification, (ii) hard limiter is the best ELM kernel for this problem.
48#
發(fā)表于 2025-3-29 21:44:58 | 只看該作者
49#
發(fā)表于 2025-3-30 01:05:02 | 只看該作者
50#
發(fā)表于 2025-3-30 04:30:59 | 只看該作者
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