The goal of pre-attentive segmentation is to mark conspicuous image locations such as region boundaries, smooth contours and popout targets against backgrounds. This salience detection relies on not only feature similarity analysis but also local feature contrast. We identify these two measures with attraction and nondirectional repulsion, and unify the dual processes of association by attraction and segregation by repulsion in one grouping framework. We generalize normalized cuts to multi-way partitioning with these dual measures and show that the criterion can be viewed as a stochastic jump-diffusion process, where the probability of jump is determined by the relative strengths of attraction and repulsion. We demonstrate that this extended model can deal with salience detection under various situations as well as the asymmetry in visual search. Through these results, we provide a clear understanding of the role of negative weights in the graph partitioning framework. This opens up the possibilities of encoding negative correlations in constraint satisfaction problems, where solutions by simple and robust eigendecomposition become possible. 1.

An important approach in visual neuroscience considers how the function of the early visual system relates to the statistics of its natural input. Previous studies have shown how many basic properties of the primary visual cortex, such as the receptive fields of simple and complex cells and the spatial organization (topography) of the cells, can be understood as efficient coding of natural images. Here we extend the framework by considering how the responses of complex cells could be eciently coded by a higher-order neural layer. This leads to contour coding and end-stopped receptive fields. Interestingly, contour integration can in this framework be seen as a direct result of top-down noise reduction, suggesting such a role for cortico-cortical feedback connections in the visual cortex.

recurrent model of contour integration in primary

robust image processing

Distributed synchronization is known to occur at several scales in the brain, and has been suggested as playing a key functional role in perceptual grouping. State-of-the-art visual grouping algorithms, however, seem to give comparatively little attention to neural synchronization analogies. Based on the framework of concurrent synchronization of dynamic systems, simple networks of neural oscillators coupled with diffusive connections are proposed to solve visual grouping problems. Multi-layer algorithms and feedback mechanisms are also studied. The same algorithm is shown to achieve promising results on several classical visual grouping problems, including point clustering, contour integration and image segmentation. 1

Texture discontinuities are a fundamental cue by which the visual system segments objects from their background. The neural mechanisms supporting texture-based segmentation are therefore critical to visual perception and cognition. In the present experiment we employ an EEG source-imaging approach in order to study the time course of texture-based segmentation in the human brain. Visual Evoked Potentials were recorded to four types of stimuli in which periodic temporal modulation of a central 3u figure region could either support figure-ground segmentation, or have identical local texture modulations but not produce changes in global image segmentation. The image discontinuities were defined either by orientation or phase differences across image regions. Evoked responses to these four stimuli were analyzed both at the scalp and on the cortical surface in retinotopic and functional regions-of-interest (ROIs) defined separately using fMRI on a subject-by-subject basis. Texture segmentation (tsVEP: segmenting versus non-segmenting) and cue-specific (csVEP: orientation versus phase) responses exhibited distinctive patterns of activity. Alternations between uniform and segmented images produced highly asymmetric responses that were larger after transitions from the uniform to the segmented state. Texture modulations that signaled the appearance of a figure evoked a pattern of increased activity starting at,143 ms that was larger in V1 and LOC ROIs, relative to identical modulations that didn’t signal figure-ground segmentation. This segmentation-related activity occurred after an initial response phase that did not depend on the global segmentation