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Articles 1 - 4 of 4
Full-Text Articles in Engineering
Image Segmentation Using Fuzzy-Spatial Taxon Cut, Lauren Barghout
Image Segmentation Using Fuzzy-Spatial Taxon Cut, Lauren Barghout
MODVIS Workshop
Images convey multiple meanings that depend on the context in which the viewer perceptually organizes the scene. This presents a problem for automated image segmentation, because it adds uncertainty to the process of selecting which objects to include or not include within a segment. I’ll discuss the implementation of a fuzzy-logic-natural-vision-processing engine that solves this problem by assuming the scene architecture prior to processing. The scene architecture, a standardized natural-scene-perception-taxonomy comprised of a hierarchy of nested spatial-taxons. Spatial-taxons are regions (pixel-sets) that are figure-like, in that they are perceived as having a contour, are either `thing-like', or a `group of …
Two Correspondence Problems Easier Than One, Aaron Michaux, Zygmunt Pizlo
Two Correspondence Problems Easier Than One, Aaron Michaux, Zygmunt Pizlo
MODVIS Workshop
Computer vision research rarely makes use of symmetry in stereo reconstruction despite its established importance in perceptual psychology. Such stereo reconstructions produce visually satisfying figures with precisely located points and lines, even when input images have low or moderate resolution. However, because few invariants exist, there are no known general approaches to solving symmetry correspondence on real images. The problem is significantly easier when combined with the binocular correspondence problem, because each correspondence problem provides strong non-overlapping constraints on the solution space. We demonstrate a system that leverages these constraints to produce accurate stereo models from pairs of binocular images …
Adaptive Motion Pooling And Diffusion For Optical Flow, Naga Venkata Kartheek Medathati, Pierre Kornprobst, Guillaume Masson, Manuela Chessa, Fabio Solari
Adaptive Motion Pooling And Diffusion For Optical Flow, Naga Venkata Kartheek Medathati, Pierre Kornprobst, Guillaume Masson, Manuela Chessa, Fabio Solari
MODVIS Workshop
We study the impact of local context of an image (contrast and 2D structure) on spatial motion integration by MT neurons. To do so, we revisited the seminal work by Heeger and Simoncelli (HS) [4] using spatio-temporal filters to estimate optical flow from V1-MT feedforward interactions. However, the HS model has difficulties to deal with several problems encountered in real scenes (e.g., blank wall problem and motion discontinuities). Here, we propose to extend the HS model with adaptive processing by focussing on the role of local context indicative of the local velocity estimates reliability. We set a network structure representative …
A Space-Variant Model For Motion Interpretation Across The Visual Field, Guido Maiello, Manuela Chessa, Peter J. Bex, Fabio Solari
A Space-Variant Model For Motion Interpretation Across The Visual Field, Guido Maiello, Manuela Chessa, Peter J. Bex, Fabio Solari
MODVIS Workshop
We implement a neural model for the estimation of the focus of radial motion (FRM) at different retinal locations and we assess the model by comparing its results with respect to the precision with which human observers can estimate the FRM in naturalistic, moving dead leaves stimuli. The proposed neural model describes the deep hierarchy of the first stages of the dorsal visual pathway [Solari et al., 2014]. Such a model is space-variant, since it takes into account the retino-cortical transformation of the primate visual system through log-polar mapping that produces a cortical representation of the visual signal to the …