Research on Visual Pattern Recognition with Hierarchical Neural Networks

层次神经网络视觉模式识别研究

基本信息

批准号：
02402035
负责人：
FUKUSHIMA Kunihiko
金额：
$ 21.12万
依托单位：
Osaka University
依托单位国家：
日本
项目类别：
Grant-in-Aid for General Scientific Research (A)
财政年份：
1990
资助国家：
日本
起止时间：
1990 至 1993
项目状态：
已结题

项目摘要

Aiming to develop new design principles for visual pattern recognition systems of the next generation, we have performed various researches in parallel and have improved the necognitron and the selective attention model, which were proposed previously by the author.1. Neocognitron We have succeeded in increasing robustness of the neocognitron in pattern recognition by the introduction of new network architectures and the development of improved learning methods. Performance of the improved neocognitrons has been tested for handwritten alphanumeric character recognition.(1) Thickness invariant line extraction has been realized by a new line-extraction method that uses edge information.(2) The introduction of bend-extracting cells has greatly improved the learning and recognizing ability.(3) Non-uniform blurring within a receptive field has been realized by dualization of C-cell layrs of the neocognitron, and the ability to recognize deformed patterns has been increase.(4) The shapes of receptive fields can be adaptively adjusted by creating non-uniform sensitivity within a receptive field. The ability to discriminate similar patterns can also be increased.(5) A new method of learning by error correction has been developed. The same high robustness in pattern recognition can be obtained with much smaller effort than the conventional supervised learning.(6) Large ability of generalization can be obtained by unsupervised learning with winner-take-all process, if different threshold values are used for feature-extraction in the learning and the recognition phases.2. Selective Attention A cursive English word recognition system has been developed. The system consists of the selective attention model, to which search controller and automatic attention switching mechanisms have been added. The principles of the selective attention model has been shown to be useful also for handwritten Kanji recognition and face recognition.

为了开发下一代视觉模式识别系统的新设计原理，我们并行进行了各种研究，并改进了作者先前提出的认知机和选择性注意模型。1．通过引入新的网络架构和开发改进的学习方法，我们成功地提高了新认知机在模式识别方面的鲁棒性。改进的新认知器的性能已在手写字母数字字符识别中进行了测试。（1）通过使用边缘信息的新线提取方法实现了厚度不变的线提取。（2）弯曲提取单元的引入极大地提高了线提取的精度。 (3)通过新认知机C细胞层的二元化实现了感受野内的非均匀模糊，并具有识别变形模式的能力。 (4)通过在感受野内创建非均匀灵敏度，可以自适应地调整感受野的形状。还可以提高区分相似模式的能力。(5)开发了一种新的纠错学习方法。与传统的监督学习相比，可以用更少的努力获得同样高的模式识别鲁棒性。（6）如果对特征使用不同的阈值，则采用赢家通吃过程的无监督学习可以获得很大的泛化能力。学习和识别阶段的提取；2．选择性注意草书英语单词识别系统已经开发出来。该系统由选择性注意模型组成，其中添加了搜索控制器和自动注意切换机制。选择性注意模型的原理已被证明对于手写汉字识别和人脸识别也很有用。