Recognition of partly occluded patterns: a neural network model
Recognition of partly occluded patterns: a neural network model
复制标题
部分遮挡模式的识别：神经网络模型
DOI：
10.1007/s004220000210
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发表时间：
2001
影响因子：
1.9
通讯作者：
K. Fukushima
中科院分区：
文献类型：
--
作者：
K. Fukushima
摘要：人类通常能够阅读被污染的墨水污渍部分遮挡的字母或单词。 但是，如果污渍被完全擦去，信件的闭塞区域变成白色，我们通常会在阅读信件时遇到困难。在这篇文章中，我提出了一个假设，解释了为什么一个图案被可见物体遮挡时比被不可见的不透明物体遮挡时更容易识别。基于这一假设构造了一个神经网络模型，视觉系统从输入的模式中提取各种视觉特征，然后尝试进行识别，如果遮挡对象不可见，视觉系统将很难区分哪些特征与原始模式相关，哪些特征是由遮挡新产生的。然而，如果被遮挡的物体是可见的，视觉系统可以很容易地区分相关和不相关的功能，并正确识别被遮挡的模式。其感受野覆盖遮挡对象的特征提取细胞的活性在分层网络的早期阶段被抑制。由于由遮挡产生的不相关的特征被消除，因此该模型可以正确地识别被遮挡的模式，只要遮挡不是太大，甚至可以防止人类识别。
Abstract. Human beings are often able to read a letter or word partly occluded by contaminating ink stains. However, if the stains are completely erased and the occluded areas of the letter are changed to white, we usually have difficulty in reading the letter. In this article I propose a hypothesis explaining why a pattern is easier to recognize when it is occluded by visible objects than by invisible opaque objects. A neural network model is constructed based on this hypothesis.The visual system extracts various visual features from the input pattern and then attempts to recognize it. If the occluding objects are not visible, the visual system will have difficulty in distinguishing which features are relevant to the original pattern and which are newly generated by the occlusion. If the occluding objects are visible, however, the visual system can easily discriminate between relevant and irrelevant features and recognize the occluded pattern correctly.The proposed model is an extended version of the neocognitron model. The activity of the feature-extracting cells whose receptive fields cover the occluding objects is suppressed in an early stage of the hierarchical network. Since the irrelevant features generated by the occlusion are thus eliminated, the model can recognize occluded patterns correctly, provided the occlusion is not so large as to prevent recognition even by human beings.