In computer vision, edge and object contour detection is essential for higher-level vision
tasks, such as shape matching, visual salience, image segmentation, and object recognition …

What distinguishes good explanations in neuroscience from bad? Carl F. Craver constructs
and defends standards for evaluating neuroscientific explanations that are grounded in a …

Advanced computer vision mechanisms have been inspired by neuroscientific findings.
However, with the focus on improving benchmark achievements, technical solutions have …

The distinct visual sensations of shape and texture have been studied separately in cortex;
therefore, it remains unknown whether separate neuronal populations encode each of these …

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 …

Deep learning currently rules edge detection. However, the impressive progress heavily
relies on high-quality manually annotated labels which require a significant amount of labor …

Lateral occipital cortical areas are involved in the perception of objects, but it is not clear
how these areas interact with first tier visual areas. Using synthetic images portraying a …

Evidence suggests that the brain uses an operational set of canonical computations like
normalization, input filtering, and response gain enhancement via reentrant feedback. Here …

A neural model called dARTEX is proposed of how laminar interactions in the visual cortex
may learn and recognize object texture and form boundaries. The model unifies five …

Discontinuities in feature maps serve as important cues for the location of object boundaries.
Here we used multi-input nonlinear analysis methods and EEG source imaging to assess …