Depth information in natural environments derived from optic flow by insect motion detection system: a model analysis

Abstract

Knowing the depth structure of the environment is crucial for moving animals in many behavioral contexts, such as collision avoidance, targeting objects, or spatial navigation. An important source of depth information is motion parallax. This powerful cue is generated on the eyes during translatory self-motion with the retinal images of nearby objects moving faster than those of distant ones. To investigate how the visual motion pathway represents motion-based depth information we analyzed its responses to image sequences recorded in natural cluttered environments with a wide range of depth structures. The analysis was done on the basis of an experimentally validated model of the visual motion pathway of insects, with its core elements being correlation-type elementary motion detectors (EMDs). It is the key result of our analysis that the absolute EMD responses, i.e., the motion energy profile, represent the contrast-weighted nearness of environmental structures during translatory self-motion at a roughly constant velocity. In other words, the output of the EMD array highlights contours of nearby objects. This conclusion is largely independent of the scale over which EMDs are spatially pooled and was corroborated by scrutinizing the motion energy profile after eliminating the depth structure from the natural image sequences. Hence, the well-established dependence of correlation-type EMDs on both velocity and textural properties of motion stimuli appears to be advantageous for representing behaviorally relevant information about the environment in a computationally parsimonious way.

A Bio-Inspired Model for Visual Collision Avoidance on a Hexapod Walking Robot

“…In the model used here (see [18]), optic flow estimation is based on two retinotopic arrays of either horizontally or vertically aligned EMDs. Individual EMDs are implemented by a multiplication of the delayed signal of a receptive input unit with the undelayed signal of a neighbouring unit.…”

“…The array of the absolute values of these local motion vectors μ r resembles a map of contrast-weighted relative nearness to objects in the environment [18], providing information about the contours of nearby objects (Fig. 2C).…”

“…Hence, nearness information can not be extracted unambiguously from EMD responses [5]. Rather, the responses of EMDs to pure translational optic flow have been concluded to resemble a representation of the relative contrast-weighted nearness to objects in the environment, or, in other words, of the contours of nearby objects [18].…”

Visual motion-sensitive neurons in the bumblebee brain convey information about landmarks during a navigational task

“…This distinguishing flight characteristic has been suggested to facilitate the processing of depth information from motion parallax cues (Egelhaaf et al, 2012; Schwegmann et al, 2014) by minimizing the time of flight during which rotations occur by performing extremely fast body and head rotations (“saccades”). Within the longer segments of flight, the intersaccades, the gaze is kept straight.…”

Head orientation of walking blowflies is controlled by visual and mechanical cues

“…The frame of reference can be defined by a variety of sensory cues. The gravity vector, pointing downwards, offers a clear and constant reference, but visual structures in natural surroundings can also be used to align the visual system, as they contain a preponderance of horizontal and vertical edges (Baddeley and Hancock, 1991;Coppola et al, 1998;Hancock et al, 1992;Keil and Cristóbal, 2000;Schwegmann et al, 2014;Switkes et al, 1978), even if the horizon is not visible (Hansen and Essock, 2004). Moreover, the overall asymmetry in brightness between the upper and the lower parts of the environment can be used for alignment of the visual system, given that the sky is usually brighter than the ground.…”