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<p>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.</p>
dynamic link architecture; object recognition system; artificial neural networks; fine-scale temporal structure; cellular signals; human faces; video images; sparse graphs; multiresolution description; local power spectrum; geometrical distance vectors; stochastic optimization; matching cost function; transputer network; gray-level camera images; face recognition; self-organising feature maps; transputer systems.
M. Lades, J.C. Vorbruggen, J. Buhmann, J. Lange, C. von der Malsburg, R.P. Wurtz, W. Konen, "Distortion Invariant Object Recognition in the Dynamic Link Architecture", IEEE Transactions on Computers, vol. 42, no. , pp. 300-311, March 1993, doi:10.1109/12.210173
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