Use of Top-Down Information for Visual Information Processing

使用自上而下的信息进行视觉信息处理

基本信息

批准号：
14380169
负责人：
FUKUSHIMA Kunihiko
金额：
$ 8.7万
依托单位：
Tokyo University of Technology
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (B)
财政年份：
2002
资助国家：
日本
起止时间：
2002 至 2005
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-14380169/
关键词：
Visual Information Processing Neural network model Pattern recognition Occluded pattern Incremental learning Symmetry axis Optic flow Figure-ground separation 動き抽出 optic flow MT,MST野回転,拡大・縮小図地分離輪郭統合ぼかし対称軸 MT, MST野トッ

项目摘要

When we are looking at an object, we do not passively accept whole information within our visual field, but actively gather necessary information only. We focus our attention to the places that attract our interest. We capture information from there and process it selectively. We often try to predict a pattern using information from surrounding areas, and recognize it by confirming whether the initial prediction was correct. Top-down information plays an important role for such active processing of information.Varieties of neurophysiological and psychological experimental results on higher brain functions, including top-down processing, have recently been reported. We tried to analyze these results systematically from the stand-point of information processing, and made modeling research to obtain new design principles for information processors of a new generation. Namely, we first propose a new model for a higher brain function, and improved the model so as to behave in a similar way as the biological brain. At the same time, we also made several experiments for practical implementation of the models to real-world problems.As a result of these researches, we have obtained the following results :(1)A model capable of recognizing and restoring partly occluded patterns(2)Improving the recognition rate of the neocognitron (a model for robust visual pattern recognition)(3)A new method for incremental learning appropriate for multi-layered neural network(4)Use of blur for robust image processing --- a neural network model that extracts axes of symmetry from visual patterns(5)Extraction of optic flow : A model of neural network for MT and MST cells(6)Psychological experiments and models revealing relations among the mechanisms of figure-ground separation, contour integration, and motion integration.(7)Relations among the perception for LPD stimuli, global motion integration, and transparent motion : psychological experiments and a computational model

当我们观察一个物体时，我们并不是被动地接受视野内的全部信息，而是主动地只收集必要的信息。我们将注意力集中在那些吸引我们兴趣的地方。我们从那里捕获信息并有选择地处理它。我们经常尝试使用周围区域的信息来预测模式，并通过确认初始预测是否正确来识别它。自上而下的信息对于这种主动的信息处理起着重要作用。最近报道了各种关于高级大脑功能的神经生理学和心理学实验结果，包括自上而下的处理。我们试图从信息处理的角度对这些结果进行系统分析，并进行建模研究，以获得新一代信息处理器的新设计原理。也就是说，我们首先提出了一个更高的大脑功能的新模型，并改进了该模型，使其以与生物大脑相似的方式运行。同时，我们还进行了一些实验，将模型实际应用于现实世界的问题。通过这些研究，我们获得了以下结果：（1）能够识别和恢复部分遮挡模式的模型（ 2）提高neocognitron（一种鲁棒视觉模式识别模型）的识别率（3）一种适合多层神经网络的增量学习新方法（4）利用模糊进行鲁棒图像处理——神经网络提取轴的模型视觉模式的对称性(5)光流提取：MT和MST细胞的神经网络模型(6)揭示图地分离、轮廓整合和运动整合机制之间关系的心理学实验和模型。(7) LPD 刺激感知、全局运动积分和透明运动之间的关系：心理实验和计算模型