Computation of pattern invariance in brain-like structures
Section snippets
The problem of shift invariance
Our visual system can effortlessly recognize familiar objects despite large changes in their retinal images. The image of a given object changes due to variations in the viewing conditions, for example, changes in the viewing direction, illumination, position and distance. The visual system can somehow compensate for these changes and treat different images as representing an unchanging object. Many of the images we see are novel either because they depict objects not seen before, or because
Shift invariance by the conjunction of fragments
We have seen above the limitations of both the full replication and the single representation approaches to the problem of shift-invariant recognition. The full replication model is straightforward, and it uses the brain's inherent parallelism and the existence of multiple units responding selectively to a variety of different shapes. At the same time, the proposal to have a separate mechanism at each location tuned to each recognizable image is implausible because of its extreme redundancy and
Computation of pattern invariance in brain-like structures
In this section we first summarize the main properties of the approach to shift invariance and its implications, and then discuss the application of a similar approach to other aspects of invariant pattern perception.
Summary
Invariant perception is an achievement of biological visual systems that is difficult to replicate in artificial systems. We have outlined an approach to the computation of pattern invariance that appears more suitable for brain-like structures than alternative approaches. In this approach, invariance for complex patterns is based on a large number of stored relationships between more elementary image fragments. Invariant perception therefore depends on a continuous process of learning from
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