
pmid: 14766138
This paper reviews some of the recent neurophysiological studies that explore the variety of visual computations in the early visual cortex in relation to geometric inference, i.e. the inference of contours, surfaces and shapes. It attempts to draw connections between ideas from computational vision and findings from awake primate electrophysiology. In the classical feed-forward, modular view of visual processing, the early visual areas (LGN, V1 and V2) are modules that serve to extract local features, while higher extrastriate areas are responsible for shape inference and invariant object recognition. However, recent findings in primate early visual systems reveal that the computations in the early visual cortex are rather complex and dynamic, as well as interactive and plastic, subject to influence from global context, higher order perceptual inference, task requirement and behavioral experience. The evidence argues that the early visual cortex does not merely participate in the first stage of visual processing, but is involved in many levels of visual computation.
Time Factors, Pattern Recognition, Visual, Animals, Humans, Photic Stimulation, Visual Cortex
Time Factors, Pattern Recognition, Visual, Animals, Humans, Photic Stimulation, Visual Cortex
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 60 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
