15 February 2007 Optimal sensor design for estimating local velocity in natural environments
Author Affiliations +
Proceedings Volume 6492, Human Vision and Electronic Imaging XII; 649205 (2007); doi: 10.1117/12.707181
Event: Electronic Imaging 2007, 2007, San Jose, CA, United States
Motion coding in the brain undoubtedly reflects the statistics of retinal image motion occurring in the natural environment. To characterize these statistics it is useful to measure motion in artificial movies derived from simulated environments where the "ground truth" is known precisely. Here we consider the problem of coding retinal image motion when an observer moves through an environment. Simulated environments were created by combining the range statistics of natural scenes with the spatial statistics of natural images. Artificial movies were then created by moving along a known trajectory at a constant speed through the simulated environments. We find that across a range of environments the optimal integration area of local motion sensors increases logarithmically with the speed to which the sensor is tuned. This result makes predictions for cortical neurons involved in heading perception and may find use in robotics applications.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Tal Tversky, Wilson S. Geisler, "Optimal sensor design for estimating local velocity in natural environments", Proc. SPIE 6492, Human Vision and Electronic Imaging XII, 649205 (15 February 2007); doi: 10.1117/12.707181; https://doi.org/10.1117/12.707181


Environmental sensing

Motion estimation

Error analysis


Motion measurement

Motion models


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