38,253 research outputs found
Invariance of visual operations at the level of receptive fields
Receptive field profiles registered by cell recordings have shown that
mammalian vision has developed receptive fields tuned to different sizes and
orientations in the image domain as well as to different image velocities in
space-time. This article presents a theoretical model by which families of
idealized receptive field profiles can be derived mathematically from a small
set of basic assumptions that correspond to structural properties of the
environment. The article also presents a theory for how basic invariance
properties to variations in scale, viewing direction and relative motion can be
obtained from the output of such receptive fields, using complementary
selection mechanisms that operate over the output of families of receptive
fields tuned to different parameters. Thereby, the theory shows how basic
invariance properties of a visual system can be obtained already at the level
of receptive fields, and we can explain the different shapes of receptive field
profiles found in biological vision from a requirement that the visual system
should be invariant to the natural types of image transformations that occur in
its environment.Comment: 40 pages, 17 figure
Motion Invariance in Visual Environments
The puzzle of computer vision might find new challenging solutions when we
realize that most successful methods are working at image level, which is
remarkably more difficult than processing directly visual streams, just as
happens in nature. In this paper, we claim that their processing naturally
leads to formulate the motion invariance principle, which enables the
construction of a new theory of visual learning based on convolutional
features. The theory addresses a number of intriguing questions that arise in
natural vision, and offers a well-posed computational scheme for the discovery
of convolutional filters over the retina. They are driven by the Euler-Lagrange
differential equations derived from the principle of least cognitive action,
that parallels laws of mechanics. Unlike traditional convolutional networks,
which need massive supervision, the proposed theory offers a truly new scenario
in which feature learning takes place by unsupervised processing of video
signals. An experimental report of the theory is presented where we show that
features extracted under motion invariance yield an improvement that can be
assessed by measuring information-based indexes.Comment: arXiv admin note: substantial text overlap with arXiv:1801.0711
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