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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. 3, pp. 300311, March, 1993.  
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@article{ 10.1109/12.210173, author = {M. Lades and J.C. Vorbruggen and J. Buhmann and J. Lange and C. von der Malsburg and R.P. Wurtz and W. Konen}, title = {Distortion Invariant Object Recognition in the Dynamic Link Architecture}, journal ={IEEE Transactions on Computers}, volume = {42}, number = {3}, issn = {00189340}, year = {1993}, pages = {300311}, doi = {http://doi.ieeecomputersociety.org/10.1109/12.210173}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
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TY  JOUR JO  IEEE Transactions on Computers TI  Distortion Invariant Object Recognition in the Dynamic Link Architecture IS  3 SN  00189340 SP300 EP311 EPD  300311 A1  M. Lades, A1  J.C. Vorbruggen, A1  J. Buhmann, A1  J. Lange, A1  C. von der Malsburg, A1  R.P. Wurtz, A1  W. Konen, PY  1993 KW  dynamic link architecture; object recognition system; artificial neural networks; finescale 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; graylevel camera images; face recognition; selforganising feature maps; transputer systems. VL  42 JA  IEEE Transactions on Computers ER   
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 finescale temporal structure of cellular signals to group neurons dynamically into higherorder entities. These entities represent a rich structure and can code for highlevel 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 graylevel 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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