Simulation of neural contour mechanisms: representing anomalous contours

Heitger, F.; Heydt, Rüdiger von der; Peterhans, E.; Rosenthaler, L.; Kübler, Olaf

doi:10.1016/s0262-8856(97)00083-8

Cited by 102 publications

(104 citation statements)

References 19 publications

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“…It is a challenge to produce the correct illusory contours in the pacman figure as well as the Ehrenstein figure with the same model, because one calls for interpolation, whereas the other prohibits it (see Figure 1A). To reconcile these conflicting demands was a main consideration in the model by Heitger, von der Heydt, Peterhans, Rosenthaler, and Kübler (1998), results of which are shown in Figure 1B. Also from the neurophysiological perspective I have reservations with the claim that illusory contours and border ownership must be treated together and that the former cannot be explained without the latter, which is the main point of the review.…”

Section: Neurophysiological Constraints On Models Of Illusory Contoursmentioning

confidence: 99%

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The “side” matters: How configurality is reflected in completion

Kogo

Wagemans

2013

Cognitive Neuroscience

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Section: Neurophysiological Constraints On Models Of Illusory Contoursmentioning

confidence: 99%

“…A, Illusory contours can appear as interpolations between edges of the inducing elements (pacman figure, left), or can be orthogonal to the inducing elements (Ehrenstein figure, right). B, Contours produced by the model of Heitger et al 1998. Modified from Heitger et al 1998.…”

Section: Neurophysiological Constraints On Models Of Illusory Contoursmentioning

confidence: 99%

The “side” matters: How configurality is reflected in completion

Kogo

Wagemans

2013

Cognitive Neuroscience

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“…Gabor multiresolutions have been successfully used for image analysis and applications where exact reconstruction is not required, such as texture analysis (Clausi and Jernigan, 2000;Ro et al, 2001), texture synthesis (Portilla et al, 1996), edge/contour extraction (Heitger et al, 1998;Kovesi, 2003;Grigorescu et al, 2003), or object recognition (Pötzsch et al, 1996;Krüger, 2001). And, even without exact reconstruction they have been shown useful for image restoration applications (Cristóbal and Navarro, 1994;Kovesi, 1999;Mingolla et al, 1999;Christiansen, 2002).…”

Section: Fischer Et Almentioning

confidence: 99%

Self-Invertible 2D Log-Gabor Wavelets

et al. 2007

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Abstract. Orthogonal and biorthogonal wavelets became very popular image processing tools but exhibit major drawbacks, namely a poor resolution in orientation and the lack of translation invariance due to aliasing between subbands. Alternative multiresolution transforms which specifically solve these drawbacks have been proposed. These transforms are generally overcomplete and consequently offer large degrees of freedom in their design. At the same time their optimization gets a challenging task. We propose here the construction of log-Gabor wavelet transforms which allow exact reconstruction and strengthen the excellent mathematical properties of the Gabor filters. Two major improvements on the previous Gabor wavelet schemes are proposed: first the highest frequency bands are covered by narrowly localized oriented filters. Secondly, the set of filters cover uniformly the Fourier domain including the highest and lowest frequencies and thus exact reconstruction is achieved using the same filters in both the direct and the inverse transforms (which means that the transform is self-invertible). The present transform not only achieves important mathematical properties, it also follows as much as possible the knowledge on the receptive field properties of the simple cells of the Primary Visual Cortex (V1) and on the statistics of natural images. Compared to the state of the art, the log-Gabor wavelets show excellent ability to segregate the image information (e.g. the contrast edges) from spatially incoherent Gaussian noise by hard thresholding, and then to represent image features through a reduced set of large magnitude coefficients. Such characteristics make the transform a promising tool for processing natural images.

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“…The elements central for the de nition of the grouping model of Heitger et al (1998) are the following:…”

Section: Relaxation Labeling For Contour Ndingmentioning

confidence: 99%

Computational Neural Models of Spatial Integration in Perceptual Grouping

Neumann

Mingolla

2001

From Fragments to Objects - Segmentation and Grouping in Vision

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Recent developments in the neural computational modeling of perceptual grouping are described with reference to a newly proposed taxonomy to formalize mechanisms of spatial integration. This notational framework and nomenclature is introduced in order to clarify key properties common to all or most models, while permitting unique attributes of each approach to be independently examined. The strength of spatial integration in the models that are considered is always some function of the distances and relative alignments in perceptual space of the centers of units representing orientational features or energy in a visual scene. We discuss the signi cance of variations of the constituents of an activation function for spatial integration, and also consider the larger modeling framework in which this function is applied in each approach. We also discuss the relationship of feedforward and feedback m e c hanisms and the issues of self-organization as core principles underlying the establishment of spatial integration mechanisms. The relationship of the grouping models to models of other visual competencies is considered with respect to prospects for future research.

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Simulation of neural contour mechanisms: representing anomalous contours

Cited by 102 publications

References 19 publications

The “side” matters: How configurality is reflected in completion

The “side” matters: How configurality is reflected in completion

Self-Invertible 2D Log-Gabor Wavelets

Computational Neural Models of Spatial Integration in Perceptual Grouping

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