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.

旨在为下一代的视觉信息处理系统开发新的设计原理，我们将研究集中在生物大脑视觉系统中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，并为视觉模式识别设计高级系统。我们已经同时进行了各种研究，并获得了以下结果。1。可以识别复杂背景的面孔的神经网络模型。它可以将注意力集中在公认的脸上。2。可以正确识别部分遮挡模式的神经网络模型3。新版本的新版本，用于识别现实世界中的手写数字。我们以前开发的新知识是一种模式识别系统，其体系结构是从哺乳动物视觉系统中提出的。当我们使用3000个字符进行训练时，识别率取决于训练集的大小，其不同。4。新的学习规则，通过这些细胞具有自我组织的细胞。使用该学习规则，网络中类似于初级视觉皮层中的简单和复杂细胞类似的细胞。5。可以记住和回忆空间图的神经网络模型。该模型模仿了一个人在二维空间中四处走动时记住并回顾空间地图的情况。该模型记住了零碎的地图，但可以通过连续的回忆过程无缝地检索覆盖宽区域的图像。6。立体声算法从眼内未配对点提取深度提示。