This paper proposes a fuzzy feature extraction method for man-made object detection under complex scene.
提出具有模糊测度的几何基元特征的人造目标检测方法。
Fuzzy feature extraction and block design feedforward networks are employed to recognize handwritten digits.
将模糊特征提取技术与区组设计前馈网络相结合用于手写体数字识别。
In this paper, the method of graphic recognition based on fuzzy feature extraction is studied, with the emphasis on the extraction method.
本文讨论了基于模糊特征提取的几何图形识别方法。
A fuzzy gradient feature extraction method based on gradient normalization applied in handwritten character recognition is proposed.
提出一种应用于手写字符识别的基于梯度归一化模糊梯度特征提取方法。
The simulation result is that the Fuzzy forward neural networks which is trained by this algorithm have good non-logic generalization and feature extraction ability, as well as fast learning speed.
模拟结果表明利用该算法训练的模糊层次神经网络具有较好的非逻辑归纳能力和特征抽取能力，并且学习速度也大大加快。
The main contribution of this dissertation includes four aspects. They are instantaneous parameters extraction, fuzzy feature selection, single classifier design and combined classifier design.
本文主要工作体现在瞬时参数的提取、模糊特征选择、单个分类器设计和组合分类器设计这四个方面。
The new method of road semi-automatic extraction was proposed, which was based on multiseeded-fuzzy connectedness combined with road feature in SPOT image.
结合模糊连接度理论和SPOT影像上道路的表现特性提出了主干道路半自动提取的方法。
The new method of road semi-automatic extraction was proposed, which was based on multiseeded-fuzzy connectedness combined with road feature in SPOT image.
结合模糊连接度理论和SPOT影像上道路的表现特性提出了主干道路半自动提取的方法。
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