mechanisms

The activities of neurons in primary visual cortex have been shown to be significantly influenced by stimuli outside their classical receptive fields. We propose that these contextual influences serve pre-attentive visual segmentation by causing relatively higher neural responses to important or conspicuous image locations, making them more salient for perceptual pop-out. These locations include boundaries between regions, smooth contours, and pop-out targets against backgrounds. The mark of these locations is the breakdown of spatial homogeneity in the input, for instance, at the border between two texture regions of equal mean luminance. This breakdown causes changes in contextual influences, often resulting in higher responses at the border than at surrounding locations. This proposal is implemented in a biologically based model of V1 in which contextual influences are mediated by intra-cortical horizontal connections. The behavior of the model is demonstrated using examples of text...

Locally parallel dense patterns- sometimes called texture flows- define a perceptually coherent structure of particular significance to perceptual organization. We argue that with applications ranging from image segmentation and edge classification to shading analysis and shape interpretation, texture flows deserve attention equal to edge segment grouping and curve completion. This paper develops the notion of texture flow from a geometrical point of view to argue that local measurements of such structures must incorporate two curvatures. We show how basic theoretical considerations lead to a unique model for the local behavior of the flow and to a notion of texture flow “good continuation”. This, in turn, translates to a specification of consistency constraints between nearby flow measurements which we use for the computation of globally (piecewise) coherent struc-ture through the contextual framework of relaxation labeling. We demonstrate the results on synthetic and natural images.

Abatraet-Rapid texture segregation is examined using filtered noise textures. The stimuli consist of a foreground region of filtered noise with one dominant texture orientation against a background region with a different dominant orientation. Shape discrimination of the foreground region is measured as a function of the difference in orientation between the two regions (AO), the distance over which the dominant orientation rotates from the background to the foreground value (AX), and the dominant spatial frequency of the textures (f). Pe~o~an ~ declines with smaller A@, larger Ax, and lowerf. These effects are partially independent of viewing distance, which implies that it is the refuiiue or object spatial frequency, not retinof spatial frequency, which determines performance in this task. We present a model consisting of channels tuned for orientation and spatial frequency which compute local oriented energy, followed by (texture) edge detection and a cross-correlator which performs the shape discrimination. Monte Carlo simulations of this model are in accord with the degradation in performance with increased Ax and decreased AtI Texture Texture gradient Spatial filtering

Abstract—Previous theories of early vision have assumed that visual search is based on simple two-dimensional aspects of an image, such as the orientation of edges and lines. It is shown here that search can also be based on three-dimensional orientation of objects in the corresponding scene, provided that these objects are simple convex blocks. Direct comparison shows that image-based and scene-based orientation are similar in their ability to facilitate search. These findings support the hypothesis that scene-based properties are represented at preattentive levels in early vision. Visual search is a powerful tool for investigating the representations and processes at the earliest stages of human vision. In this task, observers try to determine as rapidly as possible whether a given target item is present or absent in a display. If the time to detect the target is relatively independent of the number of other items present, the display is considered to contain a distinctive visual feature. Features found in this way (e.g. orientation, color, motion) are taken to be the primitive elements of the visual systems. The most comprehensive theories of visual search (Beck, 1982; Julesz, 1984; Treisman, 1986) hypothesize the existence of two visual subsystems. A preattentive system detects features in parallel across the visual field. Spatial relations between features are not registered at this stage. These can only be determined by an attentive system that inspects serially each collection of features in the image. Recent findings, however, have argued for more sophisticated preattentive processes. For example, numerous reports show features to

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We present a computational model for human texture perception which assigns functional principles to the Gestalt laws of similarity and proximity. Motivated by early vision mechanisms, in a first step local texture features are extracted by utilizing multi-scale filtering and non-linear spatial pooling. In the second stage, features are grouped according to the spatial feature binding model of the Competitive Layer Model (CLM) (Wersing, Steil, & Ritter, 2001). The CLM uses cooperative and competitive interactions in a recurrent network, where binding is expressed by the layer-wise coactivation of feature-representing neurons. feature space with proximity being taken into account by a spatial component. To choose the stimulus dimensions which allow the most salient similarity-based texture segmentation, the feature similarity metrics is reduced to the directions of maximum variance. We show that our combined texture feature extraction and binding model performs segmentation in strong conformity with human perception. The examples range from classical microtextures and Brodatz textures to other classical Gestalt stimuli, which offer a new perspective on the role of texture for more abstract similarity grouping.

Abstract. Recent human vision research [1] suggests modelling preattentive texture segmentation by taking a set of feature samples from a local region on each side of a hypothesized edge, and then performing standard statistical tests to determine if the two samples differ significantly in their mean or variance. If the difference is significant at a specified level of confidence, a human observer will tend to pre-attentively see a texture edge at that location. I present an algorithm based upon these results, with a well specified decision stage and intuitive, easily fit parameters. Previous models of pre-attentive texture segmentation have poorly specified decision stages, more unknown free parameters, and in some cases incorrectly model human performance. The algorithm uses heuristics for guessing the orientation of a texture edge at a given location, thus improving computational efficiency by performing the statistical tests at only one orientation for each spatial location. 1 Pre-attentive Texture Segmentation