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ABSTRACT: It is well known that the detection thresholds for stationary auditory and visual signals are lower if the signals are presented bimodally rather than unimodally, provided the signals coincide in time and space. Recent work on auditory-visual motion detection suggests that the facilitation seen for stationary signals is not seen for motion signals. We investigate the conditions under which motion perception also benefits from the integration of auditory and visual signals. We show that the integration of cross-modal local motion signals that are matched in position and speed is consistent with thresholds predicted by a neural summation model. If the signals are presented in different hemi-fields, move in different directions, or both, then behavioural thresholds are predicted by a probability-summation model. We conclude that cross-modal signals have to be co-localised and co-incident for effective motion integration. We also argue that facilitation is only seen if the signals contain all localisation cues that would be produced by physical objects.
Experimental Brain Research 11/2005; 166(3-4):538-47. · 2.39 Impact Factor
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Spatial Cognition III, Routes and Navigation, Human Memory and Learning, Spatial Representation and Spatial Learning; 01/2003
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08/2001;
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Visual Form 2001, 4th International Workshop on Visual Form, IWVF-4, Capri, Italy, May 28-30, 2001, Proceedings; 01/2001
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ABSTRACT: The second-order statistics of natural images can be well
characterized by a “self-similar” 1/F<sup>2</sup> power
spectrum and the bandpass decomposition in biological vision systems is
characterized by a self-similar, wavelet-like structuring of the
“frequency channels”. It has thus often been suggested that
there might exist a systematic interrelationship between these two
properties, but a complete formal derivation of this relation has not
yet been provided. Using rate-distortion arguments and a complexity
measure, we first show that a self-similar bandpass decomposition can
achieve a desired level of distortion with a less complex system
structure than required for a decomposition in bands of equal linear
bandwidth. A closer analysis reveals that the true optimum decomposition
is approximately self-similar but shows a systematic decrease of the
log-bandwidths with increasing center frequency of the subbands. Since
this effect has also been observed in neurophysiological experiments, we
conclude that the typical properties of visual neurons may in fact
result from an optimized exploitation of the statistical redundancies of
the natural environment
1998 Conference on Computer Vision and Pattern Recognition (CVPR '98), June 23-25, 1998, Santa Barbara, CA, USA; 01/1998
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CVGIP: Graphical Model and Image Processing. 01/1993; 55:428-446.
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ABSTRACT: Intrinsic signal dimensionality, a property closely related to Gaussian curvature, is shown to be an important conceptual tool in multi-dimensional image processing for both biological and engineering sciences. Intrinsic dimensionality can reveal the relationship between recent theoretical developments in the definition of optic flow and the basic neurophysiological concept of 'end-stopping' of visual cortical cells. It is further shown how the concept may help to avoid certain problems typically arising from the common belief that an explicit computation of a flow field has to be the essential first step in the processing of spatio- temporal image sequences. Signals which cause difficulties in the computation of optic flow, mainly the discontinuities of the motion vector field, are shown to be detectable directly in the spatio-temporal input by evaluation of its three-dimensional curvature. The relevance of the suggested concept is supported by the fact that fast and efficient detection of such signals is of vital importance for ambulant observers in both the biological and the technical domain.© (1991) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
08/1991;
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ABSTRACT: Empirical evidence from both psychology and physiology stresses the importance of inherently
two-dimensional signals and corresponding operations in vision. Examples of this are the existence of
"bug-detectors" , hypercomplex and dot-responsive cells, the occurence of contour illusions, and interactions of
patterns with clearly separated orientations. These phenomena can not be described, and have been largely
ignored, by common theories of size and orientation selective channels. The reason for this is shown to be
located at the heart of the theory of linear systems: their one-dimensional eigenfunctions and the "or"-like
character of the superposition principle. Consequently, a nonlinear theory is needed. We present a first
approach towards a general framework for the description of 2D-signals and 2D-cells in biological vision.© (1990) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
09/1990;
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ABSTRACT: In this paper we analyze the properties of a repeated isotropic center-surround inhibition which includes simple nonlinearities like half-wave rectification and saturation. Our simulation results show that such operations, here implemented as iterated non-linear differences and ratios of Gaussians (INDOG and INROG), lead to endstopping. The benefits of the approach are twofold. Firstly, the INDOG can be used to design simple endstopped operators, e.g., corner detectors. Secondly, the results can explain how endstopping might arise in a neural network with purely isotropic characteristics. The iteration can be implemented as cas-cades by feeding the output of one NDOG to a next stage of NDOG. Alternatively, the INDOG mechanism can be activated in a feedback loop. In the latter case, the resulting spatio-temporal response properties are not separable and the response becomes spatially endstopped if the input is transient. Finally, we show that ON-and OFF-type INDOG outputs can be integrated spa-tially to result in quasi-topological image features like open versus closed and the number of components.
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ABSTRACT: The standard model of visual processing is based on the selective properties of linear spatial filters which are tuned to different orientations and radial frequencies. This standard model is well suited for the description of a wide range of phenomena in vision but it is not clear, whether the whole range of basic properties of early vision is entirely within the models explanatory scope. Here we suggest that there exists a basic selective processing property in early vision which is definitely outside the explanatory scope of the standard model: the selectivity for intrinsically 2D signals. This property has already been observed in the classical experiments of Hubel and Wiesel, and has more recently been found in more complex form in the extra-classical receptive field properties of various visual neurons. We show here that this selectivity cannot be described within the framework of linear spatial filtering because of reasons which lie at the heart of the theory o f linear systems: the restriction of such systems to OR- combinations of their intrinsically 1D eigenfunctions. We present a general nonlinear framework for the modeling of i2D-selective systems which is based on AND-like combinations of frequency components, and which is closely related to the Wiener-Volterra representation of nonlinear systems. To our knowledge, i2D-selectivity is the only non- standard property for which such a theoretical framework yet exists. The framework enables the combination of the nonlinear i2D-selectivity with other basic selectivities of visual neurons, for examples with simple and complex-like properties, and makes it thus possible, to construct models for the variety of neurophysiological observations on the i2D-selective processing in visual neurons. As an insight of general interest for the recent discussion on second-order properties in early vision, the framework reveals the existence of extended equivalence classes in which nonlinear schemes can have very dissimilar structural properties, and lead nevertheless to identical input-output relations. Finally, there is a close relation between i2D-selectivity and the higher-order statistical redundancies in natural images.
Proceedings of SPIE;
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ABSTRACT: The frequency domain plays a key role in the description of signals and systems. In the classical approaches, the individual frequency components are treated as independent: In linear systems, the superposition principle restricts the filtering to an OR-like processing of independent complex exponentials. Likewise, the classical second-order statistic (the powerspectrum) measures only the occurrence of each individual frequency component, independent of whether it occurs in a systematic combination with other components or not. This basic limitation can be overcome by the extension of the classical approaches to nonlinear systems and higher-order statistics, which makes it possible to selectively address AND-like combinations of frequency components. We measure which AND combinations are statistically most relevant in natural images, and investigate how this statistical structure can be exploited by nonlinear Volterra filters.
2nd Intl. Symp. Image and Signal Processing and Analysis;
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ABSTRACT: In this paper we develop a 4-dimensional representation for patterns based on image decompositions via orientation- and size-specific filters. By retaining image positional information, this encoding scheme reduces pattern rotations, translations, and scale changes to shifts in the filter outputs. The appropriate correlation processes for matching are discussed and the recognition system is illustrated by a number of examples.
Computer Vision, Graphics, and Image Processing.
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ABSTRACT: The statistical structure of natural images is analysed by two complementary approaches: by the measurement of higher-order spectra and by our method of 'wavelet statistics', which explores interesting projections of the multivariate probability density function (pdf).
We show that the probability mass is concentrated in low-dimensional subspaces which represent intrinsically one-dimensional image features, and that the multivariate pdf thus resembles a 'hyper star'. The polyspectra show a corresponding concentration of polyspectral energy for aligned frequency components. This basic statistical structure provides the explanation for the generation of a sparse representation by oriented filters: the kurtosis is maximised if the filter decomposition is adapted to the shape of the tri-spectrum, which is closely related to the fact that only oriented filters can cause the projection of a maximum part of the probability mass to the zero level.
Since the hyper star is not separable in Cartesian axes, linear filters cannot yield independent components of natural images, and there remain substantial statistical dependences across orientation, phase, scale, and position. We show how these dependences are exploited by basic cortical nonlinearities, like the phase-invariance of complex cells, cortical gain control, and end-stopping. The visual cortex thus seems to pursue a higher-order whitening strategy.
European Conference on Visual Perception (ECVP);
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Open Systems and Dynamics. 5(1998):25-39.
