Abstract: We present an object recognition system based on the Dynamic Link Architecture, which is an extension to classical Artificial Neural Networks. The Dynamic Link Architecture exploits correlations in the fine-scale temporal structure of cellular signals in order to group neurons dynamically into higher-order entities. These entities represent a very rich structure and can code for high level objects. In order to demonstrate the capabilities of the Dynamic Link Architecture we implemented a program that can recognize human faces and other objects from video images. Memorized objects are represented by sparse graphs, whose vertices are labeled by a multi-resolution 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. Our implementation on a transputer network successfully achieves recognition of human faces and office objects from grey 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.

Keywords: computer vision, distortion invariance, dynamic link architecture, elastic graph matching, object recognition, neural network, wavelet