Dynamic Processing of Visual Patterns

视觉模式的动态处理

基本信息

批准号：
09308010
负责人：
FUKUSHIMA Kunihiko
金额：
$ 9.98万
依托单位：
The University of Electro-Communications (1999-2000)Osaka University (1997-1998)
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (A).
财政年份：
1997
资助国家：
日本
起止时间：
1997 至 2000
项目状态：
已结题

项目摘要

Aiming to develop new design principles for visual information processing systems of the next generation, we have concentrated our research on the active and dynamic processes in the visual system of the biological brain. We used modeling approach to uncover the mechanism of the brain and to design advanced systems for visual pattern recognition. We have performed various researches in parallel and have obtained the following results.1. Neural network model that can recognize faces from complex backgraound. It can focus attention to and segment facial components (eyes and mouth) from the recognized face.2. Neural network model that can recognize partly occluded patterns correctly.3. Neocognitron of a new version for recognizing handwritten digits in the real world. The neocognitron, which we have developed previously, is a pattern recognition system whose architecture has been suggested from the mammalian visual system. The recognition rate, which varies depending on the size of training set, was over 98% when we used 3000 characters for the training.4. New learning rule by which cells with shift-invariant receptive fields are self-organized. With this learning rule, cells similar to simple and complex cells in the primary visual cortex are generated in a network.5. Neural network model that can memorize and recall spatial maps. The model emulates a situation where a person memorizes and recalls spatial maps when he moves around in a two-dimensional space. The model memorizes fragmentary maps, but can retrieve an image covering a wide area seamlessly by a continuous chain process of recalling.6. Stereo algorithm that extracts a depth cue from interocularly unpaired points.

为了开发下一代视觉信息处理系统的新设计原理，我们将研究重点放在生物大脑视觉系统的主动和动态过程上。我们使用建模方法来揭示大脑的机制并设计先进的视觉模式识别系统。我们并行进行了多项研究，取得了以下成果： 1．可以从复杂背景中识别人脸的神经网络模型。它可以从识别的人脸中集中注意力并分割面部成分（眼睛和嘴巴）。2．能够正确识别部分遮挡模式的神经网络模型。 3．新版本 Neocognitron 用于识别现实世界中的手写数字。我们之前开发的新认知机是一种模式识别系统，其架构是从哺乳动物视觉系统中得到启发的。识别率随训练集的大小而变化，当我们使用3000个字符进行训练时，识别率超过98%。 4．新的学习规则，具有平移不变感受野的细胞是自组织的。通过这种学习规则，在网络中生成类似于初级视觉皮层中的简单和复杂细胞的细胞。5．可以记忆和回忆空间地图的神经网络模型。该模型模拟了一个人在二维空间中移动时记忆和回忆空间图的情况。该模型只能记住零碎的地图，但可以通过连续的连锁回忆过程无缝地检索覆盖广阔区域的图像。 6．从眼间未配对点提取深度提示的立体算法。