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A neural network model of the ideal observer and the statistical efficiency analysis

理想观察者的神经网络模型及统计效率分析

基本信息

批准号：
11610070
负责人：
ISHIGUCHI Akira
金额：
$ 2.3万
依托单位：
Ochanomizu University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
1999
资助国家：
日本
起止时间：
1999 至 2000
项目状态：
已结题

项目摘要

The purpose of this study was as follows : First, we calculate a statistical efficiency which reflects relative discriminability of the human visual system to that of an ideal observer in order to investigate some properties in lower to higher processes of the visual system. Secondly, we construct a neural network model of the ideal observer by learning a statistically optimal responses in visual tasks. Furthermore, we artificially disorder the network with use of an algorithm and make it to simulate the human performance.We obtained the following results :(1) We calculated the statistical efficiency in the task of detecting symmetry patterns on the three dimensional (3D) corrugate surface with depth noise. With using these results, we could build a model about a surface construction and pattern detection in it.(2) We studied a problem about an integration of motion parallax with binocular disparity in the 3D slant perception with use of measurement of the statistical efficiency. We made clear the sampling differences between the motion parallax and binocular disparity information.(3) We studied a spatial and temporal summation of orientations of lines with use of measurements of the statical efficiency, and clarified the mechanism of the temporal sampling superiority over the spatial sampling.(4) We constructed an learning algorithm which was a basis of the neural network model of the ideal observer.(5) With use of the above algorithm, we built a double-steps neural network model of the ideal observer in the structure-from-motion task.(6) We made an algorithm which causes some disorder in the network and simulates a human performance.

本研究的目的如下：首先，我们计算了一个反映人类视觉系统相对于理想观察者的相对可区分性的统计效率，以研究视觉系统从低级到高级过程的一些性质。其次，通过学习视觉任务中的统计最优反应，构建了理想观察者的神经网络模型。得到了以下结果：(1)计算了在有深度噪声的三维波纹表面上检测对称图案的统计效率。利用这些结果，我们可以建立一个关于表面结构和其中的图案检测的模型。(2)利用统计效率的度量，研究了3D斜视感知中运动视差和双目视差的整合问题。明确了运动视差和双目视差信息之间的采样差异。(3)利用静态效率的测量方法研究了直线方向的时空总和，阐明了时间采样优于空间采样的机理。(4)构建了一种学习算法，作为理想观察者神经网络模型的基础。(5)利用上述算法，我们建立了理想观察者在运动结构任务中的两步神经网络模型。(6)提出了一种导致网络混乱的算法，并模拟了人类的行为。