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Titlebook: Computer Recognition Systems 3; Marek Kurzynski,Michal Wozniak Book 2009 Springer-Verlag Berlin Heidelberg 2009 artificial intelligence.co

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發(fā)表于 2025-3-28 16:38:59 | 只看該作者
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發(fā)表于 2025-3-28 19:19:05 | 只看該作者
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發(fā)表于 2025-3-29 01:38:41 | 只看該作者
Vehicles Recognition Using Fuzzy Descriptors of Image Segmentsion of vehicles recorded in image data. The description takes into account selected geometrical properties and shape coefficients determined for segments of reference image (vehicle model). The proposed method was implemented using reasoning system with fuzzy rules. A vehicles recognition algorithm
44#
發(fā)表于 2025-3-29 05:00:14 | 只看該作者
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發(fā)表于 2025-3-29 10:06:19 | 只看該作者
The Comparison of Normal Bayes and SVM Classifiers in the Context of Face Shape Recognitionation approaches have been used: the Normal Bayes Classifier, the Type 1 Linear Support Vector Machine (LSVM) and the type 2 LSVM with a soft margin. The influence of the shape extraction algorithm parameters on the classification efficiency has been investigated. The experiments were conducted on a
46#
發(fā)表于 2025-3-29 11:55:04 | 只看該作者
Detection of Interest Points on 3D Data: Extending the Harris Operatored by the need of general, robust characterization of a complexity of the mesh fragment, to be used for mesh segmentation and description methods. We analyze the reasoning behind traditional Harris operator for 2D images [4] and propose several possible extensions to 3D data. We investigate their pe
47#
發(fā)表于 2025-3-29 18:37:47 | 只看該作者
48#
發(fā)表于 2025-3-29 21:35:14 | 只看該作者
A Comparison Framework for Spectrogram Track Detection Algorithmsks in spectrograms. There is no standard test database which is carefully tailored to test different aspects of an algorithm. This naturally hinders the ability to perform comparisons between a developing algorithm and those which exist in the literature. The method presented in this paper will allo
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發(fā)表于 2025-3-30 00:11:59 | 只看該作者
50#
發(fā)表于 2025-3-30 04:42:12 | 只看該作者
Morphological Analysis of Binary Scene in APR Integrated Environmenthe aim of the method is to find object on the scene and then to describe theirs basic features like edges, neighbors and surface [2]. The algorithm construction gives benefits in terms speed as well as to computation costs, at the same time being capable of presenting number of attributes values for
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