Distortion invariant object recognition in the dynamic linkarchitecture
Lades, M.; Vorbruggen, J.C.; Buhmann, J.; Lange, J.; von der Malsburg, C.; Wurtz, R.P.; Konen, W.
Computers, IEEE Transactions on
Volume 42, Issue 3, Mar 1993 Page(s):300 - 311
Digital Object Identifier 10.1109/12.210173
Summary:An object recognition system based on the dynamic link
architecture, an extension to classical artificial neural networks
(ANNs), is presented. The dynamic link architecture exploits
correlations in the fine-scale temporal structure of cellular signals to
group neurons dynamically into higher-order entities. These entities
represent a rich structure and can code for high-level objects. To
demonstrate the capabilities of the dynamic link architecture, a program
was implemented that can recognize human faces and other objects from
video images. Memorized objects are represented by sparse graphs, whose
vertices are labeled by a multiresolution description in terms of a
local power spectrum, and whose edges are labeled by geometrical
distance vectors. Object recognition can be formulated as elastic graph
matching, which is performed here by stochastic optimization of a
matching cost function. The implementation on a transputer network
achieved recognition of human faces and office objects from gray-level
camera images. The performance of the program is evaluated by a
statistical analysis of recognition results from a portrait gallery
comprising images of 87 persons
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