A computational model of the analysis of some first-order and second-order motion patterns by simple and complex cells

Johnston, Alan; McOwan, Peter W.; Buxton, Hilary

doi:10.1098/rspb.1992.0162

Cited by 130 publications

(29 citation statements)

References 32 publications

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“…(a) Contrast modulation of static binary noise. The envelope is moving rightwards (two cycles per image and two cycles per sequence: one sequence 128 frames 1 s, 128 pixels 18 ( Johnston & Cli¡ord 1995b) rightwards and so should appear as green. In each case there are areas showing a clear rightward motion signal.…”

Section: Computing Direction In Texture-defined and Flicker-defined Mmentioning

confidence: 99%

“…(d ) Some of the spatio-temporal di¡erentiating ¢lters and their pattern of connectivity as described in the Johnston & Cli¡ord (1995b) gradient model. The sum of the products of the ¢lter outputs on the numerator are divided by those shown on the denominator.…”

Section: Computing Direction In Texture-defined and Flicker-defined Mmentioning

confidence: 99%

“…Figure 1d shows some of the spatio-temporal ¢lters used in the model and how they are combined. The mathematical details of the full implementation of the model are described elsewhere (Johnston et al 1992Johnston & Cli¡ord 1995a). Velocity computation is based on the spatio-temporal gradient scheme in which the temporal derivative of image brightness is divided by the spatial derivative of image brightness.…”

Section: Introductionmentioning

confidence: 99%

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Induced motion at texture–defined motion boundaries

Johnston

Benton

McOwan

1999

Proc. R. Soc. Lond. B

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When a static textured background is covered and uncovered by a moving bar of the same mean luminance we can clearly see the motion of the bar. Texture-de¢ned motion provides an example of a naturally occurring second-order motion. Second-order motion sequences defeat standard spatio-temporal energy models of motion perception. It has been proposed that second-order stimuli are analysed by separate systems, operating in parallel with luminance-de¢ned motion processing, which incorporate identi¢able pre-processing stages that make second-order patterns visible to standard techniques. However, the proposal of multiple paths to motion analysis remains controversial. Here we describe the behaviour of a model that recovers both luminance-de¢ned and an important class of texture-de¢ned motion. The model also accounts for the induced motion that is seen in some texture-de¢ned motion sequences. We measured the perceived direction and speed of both the contrast envelope and induced motion in the case of a contrast modulation of static noise textures. Signi¢cantly, the model predicts the perceived speed of the induced motion seen at second-order texture boundaries. The induced motion investigated here appears distinct from classical induced e¡ects resulting from motion contrast or the movement of a reference frame.

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Section: Computing Direction In Texture-defined and Flicker-defined Mmentioning

confidence: 99%

Section: Computing Direction In Texture-defined and Flicker-defined Mmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Induced motion at texture–defined motion boundaries

Johnston

Benton

McOwan

1999

Proc. R. Soc. Lond. B

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show abstract

Section: Introductionmentioning

confidence: 99%

“…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 from one input unit A is delayed or temporally low-pass ®ltered and multiplied with that from a neighbouring input B (see Fig.…”

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confidence: 99%

Speed tuning in elementary motion detectors of the correlation type

Zanker

Srinivasan

Egelhaaf

1999

Biological Cybernetics

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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...

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Model-based methods for generating equiluminant texture targets in imaging systems

Papathomas

1996

Int. J. Imaging Syst. Technol.

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Recently developed techniques for obtaining texture targets whose space‐average luminance is equal to a standard uniform luminance are reviewed. Such targets can be used in psychophysical experiments to obtain parameters for improving image compression algorithms based on the characteristics of the human visual system. The techniques reviewed in this article have been developed for studying apparent motion, but can be extended to cover applications in textural grouping and stereoscopic imaging systems. The point of departure of the techniques reviewed here is that properties of first‐order motion (elicited by targets that are discriminated from the background by virtue of their mean luminance difference) are distinct from those of second‐order motion (elicited by contrast‐defined targets that, on average, are equiluminant to the background). A model that accounts for most of the psychophysical experiments is one consisting of two parallel pathways for processing first‐ and second‐order motion. We present the rationale of each technique under this model, examine the expected and actual performances, and conclude with the advantages and disadvantages of each method. © 1996 John Wiley & Sons, Inc.

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A computational model of the analysis of some first-order and second-order motion patterns by simple and complex cells

Cited by 130 publications

References 32 publications

Induced motion at texture–defined motion boundaries

Induced motion at texture–defined motion boundaries

Speed tuning in elementary motion detectors of the correlation type

Model-based methods for generating equiluminant texture targets in imaging systems

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