TL;DR: An object recognition system based on the dynamic link architecture, an extension to classical artificial neural networks (ANNs), is presented and the implementation on a transputer network achieved recognition of human faces and office objects from gray-level camera images.
Abstract: 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. >
TL;DR: A flexible neural mechanism for invariant recognition based on correlated neuronal activity and the self-organization of dynamic links is proposed which allows an unsupervised decision of whether a given input pattern matches with a stored model pattern.
TL;DR: Dynamic Link Matching as mentioned in this paper is a neural dynamics for translation invariant object recognition that is robust against distortion, which is used for human face recognition against a gallery of 112 neutral frontal view faces.
TL;DR: A neural system for the recognition of objects from realistic images, together with results of tests of face recognition from a large gallery, based on Dynamic Link Matching, which requires very little genetic or learned structure.
Abstract: We present a neural system for the recognition of objects from realistic images, together with results of tests of face recognition from a large gallery. The system is inherently invariant with respect to shift, and is robust against many other variations, most notably rotation in depth and deformation. The system is based on Dynamic Link Matching. It consists of an image domain and a model domain, which we tentatively identify with primary visual cortex and infero-temporal cortex. Both domains have the form of neural sheets of hypercolumns, which are composed of simple feature detectors (modeled as Gabor-based wavelets). Each object is represented in memory by a separate model sheet, that is, a two-dimensional array of features. The match of the image to the models is performed by network self-organization, in which rapid reversible synaptic plasticity of the connections (\dynamic links") between the two domains is controlled by signal correlations, which are shaped by xed inter-columnar connections and by the dynamic links themselves. The system requires very little genetic or learned structure, relying essentially on the rules of rapid synaptic plasticity and the a priori constraint of preservation of topography to nd matches. This constraint is encoded within the neural sheets with the help of lateral connections, which are excitatory over short range and inhibitory over long range.
TL;DR: In this paper, a system for the interpretation of camera images of scenes composed of several known objects with mutual occlusion is presented, where objects are internally represented by stored model graphs.
Abstract: We present a system for the interpretation of camera images of scenes composed of several known objects with mutual occlusion. The scenes are analyzed by the recognition of the objects present and by the determination of their occlusion relations. Objects are internally represented by stored model graphs. These are formed in a semi-automatic way by showing objects against a varying background. Objects are recognized by dynamic link matching. Our experiments show that our system is very successful in analyzing cluttered scenes. The system architecture goes beyond classical neural networks by making extensive use of flexible links between units, as proposed in the dynamic link architecture. The present implementation is, however, rather algorithmic in style and is to be regarded as a pilot study that is preparing the way for a detailed implementation of the architecture.