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Motion of an extended boundary can be measured locally by neurons only orthogonal to its orientation (aperture problem) while this ambiguity is resolved for localized image features, such as corners or nonocclusion junctions. The integration of local motion signals sampled along the outline of a moving form reveals the object velocity. We propose a new(More)
Texture information is an elementary feature utilized by the human visual system to automatically, or pre-attentively, segment the visual scene. The neural substrate underlying human texture processing as well as the basic computational mechanisms remains largely unknown up to now. We propose a neural model of texture processing which integrates the data(More)
A majority of cortical areas are connected via feedforward and feedback fiber projections. In feedforward pathways we mainly observe stages of feature detection and integration. The computational role of the descending pathways at different stages of processing remains mainly unknown. Based on empirical findings we suggest that the top-down feedback(More)
The neural mechanisms underlying motion segregation and integration still remain unclear to a large extent. Local motion estimates often are ambiguous in the lack of form features, such as corners or junctions. Furthermore, even in the presence of such features, local motion estimates may be wrong if they were generated near occlusions or from transparent(More)
Our visual system segments images into objects and background. Figure-ground segregation relies on the detection of feature discontinuities that signal boundaries between the figures and the background and on a complementary region-filling process that groups together image regions with similar features. The neuronal mechanisms for these processes are not(More)
The human visual system uses texture information to segment visual scenes into figure and ground. We developed a computational model of human texture processing [Thielscher A, Neumann H (2003) Neural mechanisms of cortico-cortical interaction in texture boundary detection: a modeling approach. Neuroscience 122:921-939] which allows us to examine the(More)
In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier's archiving and manuscript policies are encouraged to visit: Abstract Surfaces of environmental objects are the key to understanding the visual(More)
In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier's archiving and manuscript policies are encouraged to visit: Visual motion field Robust estimators Optic flow Random sample consensus m-Functions(More)
In the primary visual pathway, information is represented in two distinct, complementary domains, namely " on " and " off " cells. In this work we examine how on and off cells may interact to form the input to simple cell subfields. On the basis of physiological evidence, we propose a mechanism of dominating opponent inhibition, where a simple cell subfield(More)