Local statistics of retinal optic flow for self-motion through natural sceneries

Calow, Dirk; Lappe, Markus

doi:10.1080/09548980701642277

Cited by 39 publications

(42 citation statements)

References 35 publications

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“…The most similar work to ours also uses the Brown range image database to generate realistic synthetic flow fields (Calow et al, 2004). The authors use a gaze tracker to record how people view the range images and then simulate their motion into the scene with varying fixation points.…”

Section: Previous Workmentioning

confidence: 98%

“…Optical flow has been generated from range images by Calow et al (2004), but they focus on a synthetic model of human gaze and ego-motion. Unlike (Calow et al, 2004), we focus on optical flow fields as they arise in machine vision applications as opposed to human vision. 2 The range image database we use captures information about surfaces and surface boundaries in natural scenes, but it is completely static.…”

Section: Obtaining Training Datamentioning

confidence: 99%

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On the Spatial Statistics of Optical Flow

Roth

Black

2007

Int J Comput Vision

161

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We present an analysis of the spatial and temporal statistics of "natural" optical flow fields and a novel flow algorithm that exploits their spatial statistics. Training flow fields are constructed using range images of natural scenes and 3D camera motions recovered from hand-held and car-mounted video sequences. A detailed analysis of optical flow statistics in natural scenes is presented and machine learning methods are developed to learn a Markov random field model of optical flow. The prior probability of a flow field is formulated as a Field-of-Experts model that captures the spatial statistics in overlapping patches and is trained using contrastive divergence. This new optical flow prior is compared with previous robust priors and is incorporated into a recent, accurate algorithm for dense optical flow computation. Experiments with natural and synthetic sequences illustrate how the learned optical flow prior quantitatively improves flow accuracy and how it captures the rich spatial structure found in natural scene motion.

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Section: Previous Workmentioning

confidence: 98%

Section: Obtaining Training Datamentioning

confidence: 99%

On the Spatial Statistics of Optical Flow

Roth

Black

2007

Int J Comput Vision

161

View full text Add to dashboard Cite

show abstract

“…(10) gives us expressions for the retinal speed [21,22] and stereo disparity [23,24] as a function of depth. Suppose an observer is translating laterally at a velocity T x ms −1 and uses a head and /or eye movement to track a point at depth z 0 .…”

Section: A Theoretical Model: Frontoparallel Disksmentioning

confidence: 99%

“…The latter found that deeper objects within a volume tend to be darker, which may be related to the proximity-luminance bias in human vision. Natural scene range data have been used for generating models of image statistics such as the optical flow seen by a moving observer [21,22] and the retinal disparities for a binocular observer [23,24].…”

Section: Related Workmentioning

confidence: 99%

Visibility in three-dimensional cluttered scenes

Langer

Mannan

2012

J. Opt. Soc. Am. A

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Three-dimensional (3D) cluttered scenes consist of a large number of small surfaces distributed randomly in a 3D view volume. The canonical example is the foliage of a tree or bush. 3D cluttered scenes are challenging for vision tasks such as object recognition and depth perception because most surfaces or objects are only partly visible. This paper examines the probabilities of surface visibility in 3D cluttered scenes. We model how the probabilities of visible gaps, depth discontinuities, and binocular and half-occluded points depend on scene parameters such as the size and density of the surfaces that make up the clutter, as well as on depth and inverse depth. Inverse depth is of particular interest since both binocular disparity and motion parallax depend directly on it. The probability models are verified using data from synthetic 3D cluttered scenes, which are generated using computer graphics.

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“…The incremental enhancement of our models with changes in the relative influence of receptive field segments supports the view that MSTd is optimized for optic flow direction selectivity (Duffy and Wurtz 1995;Lappe et al 1996) independent of other motion parameters (Calow and Lappe 2007). This selectivity may be the result of the dynamic adjustment of weightings on the inputs to each neuron (Wang 1995), possibly through Hebbian shaping guided by heading direction feedback (Zhang et al 1993).…”

Section: Gain Modulation By Optic Flowmentioning

confidence: 67%

Receptive Field Dynamics Underlying MST Neuronal Optic Flow Selectivity

Page

Gaborski

et al. 2010

Journal of Neurophysiology

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Yu CP, Page WK, Gaborski R, Duffy CJ. Receptive field dynamics underlying MST neuronal optic flow selectivity. J Neurophysiol 103: 2794 -2807, 2010. First published March 24, 2010 doi:10.1152/jn.01085.2009. Optic flow informs moving observers about their heading direction. Neurons in monkey medial superior temporal (MST) cortex show heading selective responses to optic flow and planar direction selective responses to patches of local motion. We recorded MST neuronal responses to a 90 ϫ 90°optic flow display and to a 3 ϫ 3 array of local motion patches covering the same area. Our goal was to test the hypothesis that the optic flow responses reflect the sum of the local motion responses. The local motion responses of each neuron were modeled as mixtures of Gaussians, combining the effects of two Gaussian response functions derived using a genetic algorithm, and then used to predict that neuron's optic flow responses. Some neurons showed good correspondence between local motion models and optic flow responses, others showed substantial differences. We used the genetic algorithm to modulate the relative strength of each local motion segment's responses to accommodate interactions between segments that might modulate their relative efficacy during co-activation by global patterns of optic flow. These gain modulated models showed uniformly better fits to the optic flow responses, suggesting that coactivation of receptive field segments alters neuronal response properties. We tested this hypothesis by simultaneously presenting local motion stimuli at two different sites. These two-segment stimuli revealed that interactions between response segments have direction and location specific effects that can account for aspects of optic flow selectivity. We conclude that MST's optic flow selectivity reflects dynamic interactions between spatially distributed local planar motion response mechanisms.

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Local statistics of retinal optic flow for self-motion through natural sceneries

Cited by 39 publications

References 35 publications

On the Spatial Statistics of Optical Flow

On the Spatial Statistics of Optical Flow

Visibility in three-dimensional cluttered scenes

Receptive Field Dynamics Underlying MST Neuronal Optic Flow Selectivity

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