Correlative velocity estimation: visual motion analysis, independent of object form, in arrays of velocity-tuned bilocal detectors

Glünder, Helmut

doi:10.1364/josaa.7.000255

Cited by 15 publications

(7 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The sensitivity to large-field motion patterns resembling optic flow in the dorsal portion of MST (MSTd) supports the view that MST is involved in self-motion perception (Saito et al, 1986;Tanaka et al, 1986Ungerleider and Desimone, 1986;Komatsu and Wurtz, 1988;Duff y andWurtz, 1991a,b, 1995;Orban et al, 1992;Lagae et al, 1994;Bradley et al, 1996;Lappe et al, 1996). Neurons that respond preferentially to expansion could convey information about forward translation (Saito et al, 1986;Tanaka et al, 1986Perrone, 1987Perrone, , 1990Glünder, 1990;Hatsopoulos and Warren, 1991), and this principle can be generalized to combined translation and rotation (Perrone, 1992;Perrone and Stone, 1994). However, because many MST neurons show a form of "position invariance," i.e., they prefer a specific type of motion (e.g., counterclockwise rotation) regardless of where in their receptive field that motion is presented (Duffy and Wurtz, 1991b;Orban et al, 1992;Graziano et al, 1994;Lagae et al, 1994), MST seemed ill-suited to support navigation.…”

mentioning

confidence: 56%

“…The concept of optic-flow templates has been around for some time in the fields of insect vision (e.g., Horridge, 1991;Krapp and Hengstenberg, 1996) and primate self-motion estimation Tanaka et al, 1986Perrone, 1987Perrone, , 1990Glünder, 1990;Hatsopoulos and Warren, 1991). However, for primates, it has been less well accepted because of weaknesses in the early designs.…”

Section: Alternate Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Emulating the Visual Receptive-Field Properties of MST Neurons with a Template Model of Heading Estimation

Perrone

Stone

1998

J. Neurosci.

View full text Add to dashboard Cite

We have proposed previously a computational neural-network model by which the complex patterns of retinal image motion generated during locomotion (optic flow) can be processed by specialized detectors acting as templates for specific instances of self-motion. The detectors in this template model respond to global optic flow by sampling image motion over a large portion of the visual field through networks of local motion sensors with properties similar to those of neurons found in the middle temporal (MT) area of primate extrastriate visual cortex. These detectors, arranged within cortical-like maps, were designed to extract self-translation (heading) and self-rotation, as well as the scene layout (relative distances) ahead of a moving observer. We then postulated that heading from optic flow is directly encoded by individual neurons acting as heading detectors within the medial superior temporal (MST) area. Others have questioned whether individual MST neurons can perform this function because some of their receptive-field properties seem inconsistent with this role. To resolve this issue, we systematically compared MST responses with those of detectors from two different configurations of the model under matched stimulus conditions. We found that the characteristic physiological properties of MST neurons can be explained by the template model. We conclude that MST neurons are well suited to support self-motion estimation via a direct encoding of heading and that the template model provides an explicit set of testable hypotheses that can guide future exploration of MST and adjacent areas within the superior temporal sulcus.

show abstract

mentioning

confidence: 56%

Section: Alternate Modelsmentioning

confidence: 99%

Emulating the Visual Receptive-Field Properties of MST Neurons with a Template Model of Heading Estimation

Perrone

Stone

1998

J. Neurosci.

View full text Add to dashboard Cite

show abstract

“…But some aggregation of motion detectors is required anyway to account e.g. for higher-order motion (Lu & Sperling, 1996) or independence of form (Glünder, 1990).…”

Section: Discussionmentioning

confidence: 99%

The Rotating Snake Illusion is a straightforward consequence of non-linearity in arrays of standard motion detectors

Bach

Atala-Gérard

2020

Preprint

View full text Add to dashboard Cite

The Rotating Snakes illusion is a motion illusion based on repeating, asymmetric luminance patterns. Recently, we found certain grey-value conditions where a weak, illusory motion occurs in the opposite direction. There exist four models for explaining the illusion, one of which (Backus and Oruç 2005) also explains the unexpected perceived opposite direction. We here present a simple new model, without free parameters, based on an array of standard correlation-type motion detectors with a subsequent non-linearity (e.g., saturation) before summing the detector outputs. The model predicts (1) the pattern-appearance motion illusion for steady fixation, (2) an illusion under the real-world situation of saccades across the pattern occurring (pattern shift), (3) a relative maximum of illusory motion for the same grey values where it is found psychophysically, and (4) the inverse illusion for certain luminance values. We submit that its sparseness of assumptions justifies adding a fifth model to explain this illusion.

show abstract

“…Heeger 1987;Zanker and Braddick, in preparation), which also resolve other ambiguities, such as those caused by variations in contrast. In this context, two independent studies have used half-detectors of the correlation type to estimate speed (GluÈ nder 1990;Snippe and Koenderink 1994). GluÈ nder (1990) presents a model for estimating 2D velocity.…”

Section: Discussionmentioning

confidence: 99%

Speed tuning in elementary motion detectors of the correlation type

Zanker

Srinivasan

Egelhaaf

1999

Biological Cybernetics

View full text Add to dashboard Cite

Abstract. A prominent model of visual motion detection is the so-called correlation or Reichardt detector. Whereas this model can account for many properties of motion vision, from humans to insects (review, Borst and Egelhaaf 1989), it has been commonly assumed that this scheme of motion detection is not well suited to the measurement of image velocity. This is because the commonly used version of the model, which incorporates two unidirectional motion detectors with opposite preferred directions, produces a response which varies not only with the velocity of the image, but also with its spatial structure and contrast. On the other hand, information on image velocity can be crucial in various contexts, and a number of recent behavioural experiments suggest that insects do extract velocity for navigational purposes (review, Srinivasan et al. 1996). Here we show that other versions of the correlation model, which consists of a single unidirectional motion detector or incorporates two oppositely directed detectors with unequal sensitivities, produce responses which vary with image speed and display tuning curves that are substantially independent of the spatial structure of the image. This surprising feature suggests simple strategies of reducing ambiguities in the estimation of speed by using components of neural hardware that are already known to exist in the visual system. IntroductionThe simplest way in which a visual system can determine how fast and in what direction an object travels would be to determine how long the object needs to cover the distance between two given points. Indeed,`featuretracking' mechanisms have been proposed along these lines to explain motion vision (Braddick 1980;Ullman 1983) and are sometimes used in machine vision (Murray and Buxton 1990). The disadvantage of such schemes, however, is that they need to identify the object, or features within it, before carrying out the tracking. In another class of models, this problem is avoided by using information on local spatiotemporal changes of intensity to measure velocity. In one subclass of these`intensity-based' models, the so-called gradient models, image speed is determined by computing the ratio of the local temporal and spatial gradients of intensity (Fennema and Thompson 1975;Limb and Murphy 1975). Gradient models have the property that they measure the speed of an image independently of its spatial structure. Modi®cations of this scheme have been proposed for measuring image velocity in two dimensions and for overcoming problems associated with low and sparsely distributed contrasts (Hildreth and Koch 1987;Johnston et al. 1992;Srinivasan 1990).Another subclass of the intensity-based models is the so-called Reichardt or correlation detector, which extracts a motion signal from the spatiotemporal correlations that are present in the moving image (Reichardt 1957). In this model, which has been very successful in describing motion sensitivity in animal vision from insects to primates (review, Borst and Egelhaaf 1989), the signal fr...

show abstract

Correlative velocity estimation: visual motion analysis, independent of object form, in arrays of velocity-tuned bilocal detectors

Cited by 15 publications

References 27 publications

Emulating the Visual Receptive-Field Properties of MST Neurons with a Template Model of Heading Estimation

Emulating the Visual Receptive-Field Properties of MST Neurons with a Template Model of Heading Estimation

The Rotating Snake Illusion is a straightforward consequence of non-linearity in arrays of standard motion detectors

Speed tuning in elementary motion detectors of the correlation type

Contact Info

Product

Resources

About