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Psychophysical support for a two-dimensional view interpolation theory of object recognition.
The results suggest that the human visual system is better described as recognizing these objects by two-dimensional view interpolation than by alignment or other methods that rely on object-centered three-dimensional models.
A network that learns to recognize three-dimensional objects
A scheme is developed, based on the theory of approximation of multivariate functions, that learns from a small set of perspective views a function mapping any viewpoint to a standard view, and a network equivalent to this scheme will 'recognize' the object on which it was trained from any viewpoint.
Representation and recognition in vision
In Representation and Recognition in Vision, Shimon Edelman bases a comprehensive approach to visual representation on the notion of correspondence between proximal (internal) and distal similarities in objects and develops a theory of representation that is related to Shepard's notion of second-order isomorphism between representations and their targets.
Unsupervised learning of natural languages
This unsupervised algorithm is capable of learning complex syntax, generating grammatical novel sentences, and proving useful in other fields that call for structure discovery from raw data, such as bioinformatics.
A sequence of object‐processing stages revealed by fMRI in the human occipital lobe
Functional magnetic resonance imaging was used in combined functional selectivity and retinotopic mapping tests to reveal object‐related visual areas in the human occpital lobe and suggest the existence of object‐fragment representation in LO.
Fast perceptual learning in visual hyperacuity.
This hypothesis is given support by the demonstration that it is possible to synthesize, from a small number of examples of a given task, a simple network that attains the required performance level.