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Titlebook: Computer Vision Systems; 6th International Co Antonios Gasteratos,Markus Vincze,John K. Tsotsos Conference proceedings 2008 Springer-Verlag

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樓主: cherub
11#
發(fā)表于 2025-3-23 13:35:23 | 只看該作者
12#
發(fā)表于 2025-3-23 17:39:32 | 只看該作者
Feature Extraction and Classification by Genetic Programmingre extraction and classification stages. The strategy taken for the classifier is to evolve a set of partial solutions, each of which works for a single class. It is found that this approach is significantly faster than conventional genetic programming, and frequently results in a better classifier.
13#
發(fā)表于 2025-3-23 18:05:34 | 只看該作者
14#
發(fā)表于 2025-3-24 02:10:41 | 只看該作者
Attention Modulation Using Short- and Long-Term Knowledgenowledge about target features is often used to bias the bottom-up pathway. In this paper we propose a system which does not only make use of knowledge about the target features, but also uses already acquired knowledge about objects in the current scene to speed up the visual search. Main ingredien
15#
發(fā)表于 2025-3-24 02:27:00 | 只看該作者
16#
發(fā)表于 2025-3-24 09:28:34 | 只看該作者
,Baby Boomers and the Death Café, uniform motions. For each cluster a motion model is fitted and it is used to create a multiple hypothesis prediction for the following frame. Experiments have been performed on standard and outdoor datasets in order to show the validity of the proposed technique.
17#
發(fā)表于 2025-3-24 11:58:47 | 只看該作者
18#
發(fā)表于 2025-3-24 18:44:09 | 只看該作者
Region of Interest Generation in Dynamic Environments Using Local Entropy Fields uniform motions. For each cluster a motion model is fitted and it is used to create a multiple hypothesis prediction for the following frame. Experiments have been performed on standard and outdoor datasets in order to show the validity of the proposed technique.
19#
發(fā)表于 2025-3-24 20:15:19 | 只看該作者
20#
發(fā)表于 2025-3-24 23:37:50 | 只看該作者
https://doi.org/10.1007/978-3-031-53177-4g action and a cognitive long-term motion prediction. Trajectory recognition rates around?90% are achieved, requiring only a small number of training sequences. The proposed prediction approach yields significantly more reliable results than a Kalman Filter based reference approach.
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