|
Published Articles >> Table of Contents >> Abstract
32nd Applied Imagery Pattern Recognition Workshop (AIPR'03)
p. 205
A Survey of Recent Developments in Theoretical Neuroscience and Machine Vision
Jeffrey B. Colombe, The MITRE Corporation, McLean VA
Full Article Text:
  
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/AIPR.2003.1284273
Send link to a friend
| Abstract |
|
Efforts to explain human and animal vision, and to
automate visual function in machines, have found it
difficult to account for the view-invariant perception of
universals such as environmental objects or processes,
and the explicit perception of featural parts and wholes in
visual scenes. A handful of unsupservised learning
methods, many of which relate directly to independent
components analysis (ICA), have been used to make
predictive perceptual models of the spatial and temporal
statistical structure in natural visual scenes, and to
develop principled explanations for several important
properties of the architecture and dynamics of
mammalian visual cortex. Emerging principles include a
new understanding of invariances and part-whole
compositions in terms of the hierarchical analysis of
covariation in feature subspaces, reminiscent of the
processing across layers and areas of visual cortex, and
the analysis of view manifolds, which relate to the
topologically ordered feature maps in cortex.
|
 Additional Information
|
Citation:
Jeffrey B. Colombe,
"A Survey of Recent Developments in Theoretical Neuroscience and Machine Vision,"
aipr,
p. 205,
32nd Applied Imagery Pattern Recognition Workshop (AIPR'03),
2003
|
|
