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Titlebook: Computational Science – ICCS 2019; 19th International C Jo?o M. F. Rodrigues,Pedro J. S. Cardoso,Peter M.A Conference proceedings 2019 Spri

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發(fā)表于 2025-3-23 11:54:53 | 只看該作者
A New Shape Descriptor and Segmentation Algorithm for Automated Classifying of Multiple-morphologicaar boundary of algae bodies and noisy background, since an image segmentation is the most important preprocessing step in object classification and recognition. The previously proposed approach was able to classify twelve genera of microalgae successfully; however, when we extended it to work with n
12#
發(fā)表于 2025-3-23 14:30:28 | 只看該作者
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發(fā)表于 2025-3-23 20:03:28 | 只看該作者
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發(fā)表于 2025-3-24 00:31:19 | 只看該作者
Nonlinear Dimensionality Reduction in Texture Classification: Is Manifold Learning Better Than PCA?e image descriptors, namely Gray-Level Co-occurrence Matrix features, Haralick features, Histogram of Oriented Gradients features and Local Binary Patterns are combined to characterize and discriminate textures. For patches extracted from several texture images, a concatenation of the image descript
15#
發(fā)表于 2025-3-24 04:30:54 | 只看該作者
16#
發(fā)表于 2025-3-24 08:53:19 | 只看該作者
Path-Finding with a Full-Vectorized GPU Implementation of Evolutionary Algorithms in an Online Crowdreal-time and it can handle dynamic obstacles in maps of arbitrary size. The experiments show the proposed approach outperforms other traditional path-finding algorithms (e.g. A*). The conclusions present further improvement possibilities to the proposed approach like the application of multi-object
17#
發(fā)表于 2025-3-24 12:35:16 | 只看該作者
Path-Finding with a Full-Vectorized GPU Implementation of Evolutionary Algorithms in an Online Crowdreal-time and it can handle dynamic obstacles in maps of arbitrary size. The experiments show the proposed approach outperforms other traditional path-finding algorithms (e.g. A*). The conclusions present further improvement possibilities to the proposed approach like the application of multi-objective algorithms to represent full crowd models.
18#
發(fā)表于 2025-3-24 18:27:34 | 只看該作者
19#
發(fā)表于 2025-3-24 21:17:02 | 只看該作者
978-3-030-22749-4Springer Nature Switzerland AG 2019
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發(fā)表于 2025-3-25 01:08:36 | 只看該作者
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