基于神经网络模型的边界检测

[Proceedings] Third International Conference on Tools for Artificial Intelligence - TAI 91 Pub Date : 1991-11-10 DOI:10.1109/TAI.1991.167102

D. C. D. Hung, Ke Chen

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引用次数: 0

摘要

针对鲁棒边界检测问题，提出了一种新的前馈神经网络模型。在结构上，它是基于一个具有对称神经网络特征的圆形掩模。通过对加权中间模式的分析，发现在边界方向相似的情况下，有一个优势模式反复出现。因此，分段线性化的边缘可以通过这种近似检测到。实验结果表明，这种新结构可以通过改变掩模大小来体验缩放效应。

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Boundary detection based on neural networks model

A new model of feedforward neural networks is proposed for solving the problem of robust boundary detection. Structurally, it is based on a circular mask which is characterized as a symmetrical neural network. By analyzing the weighted intermediate pattern, a dominant pattern is found to appear repeatedly for similar boundary orientation. Hence, a piecewise linearized edge could be detected by this approximation. Experimental results show that this new architecture can be applied to experience scaling effect by changing the mask size.<>

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[Proceedings] Third International Conference on Tools for Artificial Intelligence - TAI 91

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