A self-learning visual pattern explorer and recognizer using a higher order neural network

G. Linhart, G. Dorffner
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引用次数: 6

Abstract

A proposal by M. B. Reid et al. (1989) to improve the efficiency of higher-order neural networks was built into a pattern recognition system that autonomously learns to categorize and recognize patterns independently of their position in an input image. It does this by combining higher-order with first-order networks and the mechanisms known from ART. Its recognition is based on a 16*16 pixel input which contains a section of the image found by a separate centering mechanism. With this system position invariant recognition can be implemented efficiently, while combining all the advantages of the subsystems.<>
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使用高阶神经网络的自学习视觉模式浏览器和识别器
M. B. Reid等人(1989)提出了一个提高高阶神经网络效率的建议,该建议被构建到一个模式识别系统中,该系统可以自主学习分类和识别模式,而不依赖于模式在输入图像中的位置。它通过结合高阶网络和一阶网络以及ART中已知的机制来做到这一点。它的识别是基于一个16*16像素的输入,其中包含由一个单独的定心机制找到的图像的一部分。该系统结合了各子系统的优点,可以有效地实现位置不变识别。
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