December 2022
Volume 22, Issue 14
Open Access
Vision Sciences Society Annual Meeting Abstract  |   December 2022
Psychophysical measurement of perceived motion flow in naturalistic scenes
Author Affiliations & Notes
  • Yung-Hao Yang
    Cognitive Informatics Lab, Department. of Intelligence Science and Technology, Graduate School of Informatics, Kyoto University, Japan
  • Taiki Fukiage
    Human Information Science Laboratory, NTT Communication Science Laboratories, Nippon Telegraph and Telephone Corporation, Japan
  • Shin’ya Nishida
    Cognitive Informatics Lab, Department. of Intelligence Science and Technology, Graduate School of Informatics, Kyoto University, Japan
    Human Information Science Laboratory, NTT Communication Science Laboratories, Nippon Telegraph and Telephone Corporation, Japan
  • Footnotes
    Acknowledgements  This work has been supported by JSPS Kakenhi JP 20H00603.
Journal of Vision December 2022, Vol.22, 3861. doi:https://doi.org/10.1167/jov.22.14.3861
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      Yung-Hao Yang, Taiki Fukiage, Shin’ya Nishida; Psychophysical measurement of perceived motion flow in naturalistic scenes. Journal of Vision 2022;22(14):3861. https://doi.org/10.1167/jov.22.14.3861.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

The computational sub-goal of early visual motion processing is to estimate the spatial map of local image motion vectors (optical flow). Due to technical difficulty, little attempts have been made to psychophysically measure the optical flow experienced by human observers. Using a novel measurement method, the present study psychophysically estimated the perceived optical flow of naturalistic scenes. The target stimulus was a brief movie presented in a circular aperture. In the middle of the movie presentation, a small dot was flashed at the aperture center. The observer had to report the image motion vector at the location/timing of the flash by matching the speed and direction of the following matching stimulus (pink-noise field). The target and matching stimuli were alternatively presented several times until the observer was satisfied (method of adjustment). A preliminary experiment using a random-dot kinematogram as the target stimulus assured the reliability of our method (R2 =.88). We then applied this new method to more naturalistic stimuli, i.e., five short movie clips selected from the slow flow version of MPI Sintel Flow Dataset (an open-source 3D animated short film with grand-truth optical flow data). The perceived motion vector was measured at 36 locations (6 x 6 grid) for each clip. The directional bias was reduced by flipping each clip vertically, horizontally, or both. The perceived vector agreed to some extent with the local ground-truth vector at the flash point (R2=0.66), as well as with the ground-truth vector spatially averaged around the flash point by the best-fit Gaussian operator (R2=0.67). Nevertheless, at several locations (most of them were close to the object boundary), the perceived vector was significantly deviated from the local or averaged ground-truth vector. We have developed a promising method to reveal the characteristics of the human visual motion perception for naturalistic complex scenes.

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