Many attempts have been made at developing artificial vision systems otherwise known as pattern recognition systems for automatic target recognition (ATR). Currently artificial vision systems are limited to fixed viewing aspects and carefully controlled and lighting conditions. Real world artificial vision problems such as ATR exhibit a great degree of variability and are beyond the scope of conventional artificial vision systems. Incorporating aspects of biological visions systems may be the key to developing truly effective artificial vision systems for real world problems. One biologically influenced approach to ATR involves performing a decomposition of an image into its constituent features. It is believed by scientists who study the brain that there are "feature detector" neurons operating in the human vision system. It is further believed by these scientists that edge features are the most likely type of feature used by brains since most of the information in an image is contained within the edges in a manner more fully described in the book Vision authored by D. Marr and published by Freeman, San Francisco, 1982, with the applicable sections thereof being incorporated by reference. However, the recognition of edge features or combinations thereof does not solve the computer vision problem. It imposes a requirement to "bind" the recognized edges into a coherent pattern.
Observations of coherent oscillatory behavior in the brains of test animals have been reported in recent years (see C. M. Gray et al, "Oscillatory Responses in Cat Visual Cortex Exhibit Inter-Columnar Synchronization which Reflects Stimulus Properties," Nature, v. 338, p.334, 1989, which is herein incorporated by reference). Currently many brain scientists are of the view that complex nonlinear dynamics across many neurons implement feature binding (see W. J. Freeman, "The Physiology of Perception," Scientific American, February, 1991 and R. Eckhorn et al, "Feature Linking via Synchronization Among Distributed Assemblies: Simulations of Results from Cat Cortex," Neural Comput. 2, 293-307, 1990; both of which techniholds that coherence is the outward manifestation of feature binding caused by nonlinear dynamics, the teaching behind the present invention holds that coherence is the very process by how binding occurs. The teaching behind the present invention posits that local processing elements (implemented by small groups of neurons) transform the detections of simple relationships among small numbers of features into representative signals of which the coherence relation with respect to other representative signals may be detected. Unlike the present invention, the popular theory would not be implementable in conventional computer architecture. It would require a massive array of analog electronic circuits to implement. Making this work would involve overcoming formidable engineering challenges. Conversely, the teaching behind the present invention, as will be shown, is very amenable to conventional computer architecture.
A method of performing an edge feature decomposition, generating an invariant representation of edge features, detecting the invariant feature relations in an input image of an unknown object and binding the associated edge features by a noise coding process is described in the cross-referenced patent application Ser. No. 08/833,482 having Attorney Docket No. 77387. Also disclosed in U.S. patent application Ser. No. 08/920,289 having Attorney Docket No. 78226 is a hardware implementation to accommodate the edge feature decomposition. In the above referenced inventions, following the edge feature decomposition, the decomposed image is further transformed into digital quantities representing relations between the features which are compared against prestored digital quantities so that the transformed representation of the unknown image captured by an optical subsystem is matched against prestored quantities, thereby, identifying the invariant edge feature relations within the unknown object. The process of the above referenced inventions only matches feature relations of the input with those represented by the prestored digital quantities. To recognize the unknown object, it is necessary to establish the universal relationships among all the edge features associated with the recognized invariant digital quantities. The process of relating or binding the associated edge features into a complete pattern is in an underlying sense related to coherence measurement. In the aforementioned U.S. patent application Ser. No. 08/833,482 having Attorney Docket No. 77387, a process of generating pseudo-random sequences associated with specific edge features is described as a means of characterizing the coherence of the edge features. It would be beneficial if a dedicated processor could be provided optimized for the noise coding processing so as to reduce the computational time to a manageable level. As it will be shown, the nature of the noise coding lends itself well to implementation on pipeline type architecture. The present invention provides noise coding to be implemented on a system that utilizes practices and techniques of conventional computer architecture.