Movement-induced motion signal distributions in outdoor scenes

Abstract

The movement of an observer generates a characteristic field of velocity vectors on the retina (). Because such optic flow-fields are useful for navigation, many theoretical, psychophysical and physiological studies have addressed the question how egomotion parameters such as direction of heading can be estimated from optic flow. Little is known, however, about the structure of optic flow under natural conditions. To address this issue, we recorded sequences of panoramic images along accurately defined paths in a variety of outdoor locations and used these sequences as input to a two-dimensional array of correlation-based motion detectors (2DMD). We find that (a) motion signal distributions are sparse and noisy with respect to local motion directions; (b) motion signal distributions contain patches (motion streaks) which are systematically oriented along the principal flow-field directions; (c) motion signal distributions show a distinct, dorso-ventral topography, reflecting the distance anisotropy of terrestrial environments; (d) the spatiotemporal tuning of the local motion detector we used has little influence on the structure of motion signal distributions, at least for the range of conditions we tested; and (e) environmental motion is locally noisy throughout the visual field, with little spatial or temporal correlation; it can therefore be removed by temporal averaging and is largely over-ridden by image motion caused by observer movement. Our results suggest that spatial or temporal integration is important to retrieve reliable information on the local direction and size of motion vectors, because the structure of optic flow is clearly detectable in the temporal average of motion signal distributions. Egomotion parameters can be reliably retrieved from such averaged distributions under a range of environmental conditions. These observations raise a number of questions about the role of specific environmental and computational constraints in the processing of natural optic flow.

keywords:

• Vision
• motion
• optic flow
• motion detector model
• motion integration
• natural scenes