Fukushima Kunihiko
Kansai University, Takatsuki, Osaka 569-1095, Japan.
Neural Netw. 2008 Jun;21(5):774-85. doi: 10.1016/j.neunet.2007.12.049. Epub 2008 Jan 12.
In an earlier paper [Fukushima, K., & Tohyama, K. (2005). Analysis of optic flow: a neural network model. In ICONIP 2005, International Conference on Neural Information Processing (pp. 312-317); Tohyama, K., & Fukushima, K. (2005). Neural network model for extracting 98 optic flow. Neural Networks, 18(5-6), 549-556], we proposed a neural network extracting optic flow based on vector field hypothesis. We started discussion there, however, from the stage where local velocities of visual objects have already been extracted. This paper proposes a new mechanism of extracting local velocity from retinal images, and adds it to the previous network. The network has a hierarchical multilayered architecture. X- and Y-cells of the retina extract spatial and temporal contrast of brightness. The network contains several types of V1 cells, namely, S- C- and V-cells. S- and C-cells extract orientated edges. V-cells extract local velocities, based on the signals from Y- and S-cells. MT cells extract relative velocities between adjoining small visual fields. MST cells add the responses of MT cells to extract a specific optic flow, such as rotation and expansion/contraction. The difference in type of optic flow extracted by MST cells can be created simply by the difference in relative locations of inhibitory to excitatory areas in the receptive fields of MT cells.
在之前的一篇论文中[福岛健一和远山和男（2005年）。光流分析：一种神经网络模型。发表于《ICONIP 2005，神经信息处理国际会议》（第312 - 317页）；远山和男和福岛健一（2005年）。用于提取光流的神经网络模型。《神经网络》，18(5 - 6)，549 - 556]，我们提出了一种基于矢量场假设提取光流的神经网络。然而，我们从视觉对象的局部速度已经被提取的阶段开始讨论。本文提出了一种从视网膜图像中提取局部速度的新机制，并将其添加到先前的网络中。该网络具有分层的多层架构。视网膜的X细胞和Y细胞提取亮度的空间和时间对比度。该网络包含几种类型的V1细胞，即S细胞、C细胞和V细胞。S细胞和C细胞提取有方向的边缘。V细胞基于来自Y细胞和S细胞的信号提取局部速度。MT细胞提取相邻小视野之间的相对速度。MST细胞将MT细胞的反应相加，以提取特定的光流，如旋转和扩张/收缩。MST细胞提取的光流类型差异可以简单地通过MT细胞感受野中抑制区与兴奋区相对位置的差异来产生。
