Takeichi Hiroshige, Suzuki Wataru, Yamashita Wakayo, Hiyama Atsushi
Open Systems Information Science Special Team, Predictive Medicine Special Project (PMSP), RIKEN Center for Integrative Medical Sciences (IMS), RIKEN, Yokohama, Kanagawa, Japan.
Computational Engineering Applications Unit, Head Office for Information Systems and Cybersecurity (ISC), RIKEN, Wako, Saitama, Japan.
Front Psychol. 2025 Aug 26;16:1586648. doi: 10.3389/fpsyg.2025.1586648. eCollection 2025.
A subset of the true optical flow can be extracted by constructing a vector field that represents image gradients and then tracking vectors in this vector field. This pseudo-flow (p-flow) subset effectively visualizes nonrigid motion and leads to the perception of nonrigid structure from motion. In this study, we investigate whether the human sensory system can extract information about the physical properties of inanimate fluid, especially viscosity, from the p-flow.
Computer-generated movies of flowing liquid were constructed using the p-flow algorithm and the Lucas-Kanade method. The movies featured liquids of different viscosities in the form of point-light displays. The viscosity of the fluid in various subsets of these movies was then estimated by 312 participants.
The error, i.e., difference between expected and actual ratings showed smaller variability across repeated trials and the mean response time was significantly shorter when using the p-flow than with the conventional Lucas-Kanade method.
Our results suggest that the p-flow enables a more reliable viscosity rating, which could be related to the local constraint used in the algorithm.
通过构建一个表示图像梯度的矢量场，然后在该矢量场中跟踪矢量，可以提取真实光流的一个子集。这个伪流（p-flow）子集有效地可视化了非刚性运动，并从运动中产生了对非刚性结构的感知。在本研究中，我们调查人类感官系统是否能够从p流中提取有关无生命流体物理特性的信息，特别是粘度。
使用p流算法和卢卡斯-卡纳德方法构建了流动液体的计算机生成电影。这些电影以点光显示的形式呈现不同粘度的液体。然后，312名参与者估计了这些电影各个子集中流体的粘度。
误差，即预期评分与实际评分之间的差异，在重复试验中显示出较小的变异性，并且使用p流时的平均响应时间明显短于传统的卢卡斯-卡纳德方法。
我们的结果表明，p流能够实现更可靠的粘度评分，这可能与算法中使用的局部约束有关。
