Use of Top-Down Information for Visual Information Processing

当我们看一个物体时，我们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。我们把注意力集中在吸引我们兴趣的地方。我们从那里获取信息并有选择地处理它。我们经常尝试使用来自周围区域的信息来预测一个模式，并通过确认最初的预测是否正确来识别它。自上而下的信息对于这种信息的主动加工起着重要的作用。最近报道了各种关于高级脑功能的神经生理和心理实验结果，包括自上而下的加工。我们试图从信息处理的角度对这些结果进行系统的分析，并进行建模研究，以获得新一代信息处理器的设计原则。也就是说，我们首先为更高的大脑功能提出了一个新的模型，并对模型进行了改进，使其行为方式与生物大脑相似。同时，我们还做了一些实验，将模型实际应用于现实问题。通过这些研究，我们获得了以下成果：(1)能够识别和恢复部分遮挡模式的模型；(2)提高新认知器的识别率（鲁棒视觉模式识别模型）；(3)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——从视觉模式中提取对称轴的神经网络模型；(5)光流提取；(6)图地分离、轮廓整合和运动整合机制关系的心理实验和模型。(7) LPD刺激知觉、整体运动整合与透明运动的关系：心理实验与计算模型

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