Distortion invariant object recognition in the dynamic link architecture
| Autor: | C. von der Malsburg, J. Lange, Joachim M. Buhmann, Jan C. Vorbrüggen, Martin Lades, Rolf P. Würtz, Wolfgang Konen |
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| Rok vydání: | 1993 |
| Předmět: |
Artificial neural network
Matching (graph theory) business.industry Computer science 3D single-object recognition ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Cognitive neuroscience of visual object recognition Image processing Facial recognition system Theoretical Computer Science Computational Theory and Mathematics Hardware and Architecture Computer Science::Computer Vision and Pattern Recognition Pattern recognition (psychology) Dynamic link matching Computer vision Artificial intelligence business Software |
| Zdroj: | IEEE Transactions on Computers. 42:300-311 |
| ISSN: | 0018-9340 |
| DOI: | 10.1109/12.210173 |
| Popis: | 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. > |
| Databáze: | OpenAIRE |
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