Definiens Ag

Peter Eschenbacher

Gerd K Binnig

Martin Baatz

Guenter Schmidt

Maria Athelogou

G&uuml;nter Schmidt

Konstantinos Bobolas

Renate Binnig

Andreas Melchinger

Richard Voss

A computer-implemented network structure includes a semantic network machine comprised of nodes containing informational contents and links containing relational contents. Relational contents describe the relationship between linked nodes. Some of the nodes are semantic Janus units. Based on time-variable states of each semantic Janus unit, the semantic Janus units perform operations on nodes and links. The operations are focused on selected portions of the semantic network machine such that each semantic Janus unit need not deal with all possible informational and relational contents within the semantic network machine. The computational resources of the computer network are thereby efficiently managed, and artificial intelligence tasks such as inferential retrieval are performed quicker. The amount of data that is processed is substantially reduced by focusing on bundled information. The semantic network is used for pattern recognition, for example, to recognize blood vessels in a medical image or streets on a digital satellite image.

Situation dependent operation of a semantic network machine

The present invention relates to a method for segmentation of a digital picture consisting of a multiplicity of single picture elements comprising determining if one of one and several features relating to contiguous picture objects comprising picture elements and picture segments are conforming or not conforming based on a specific homogeneity criterion by means of referencing a predetermined tolerance for each feature as a termination criterion, within which feature values relating to the contiguous picture objects in question may differ; if one of one feature and several features relating to the contiguous picture objects are determined to be conforming then merging the conforming picture objects; and repeating the resulting segmentation until the resulting segmentation converges in a stable or approximately stable condition in which no further contiguous picture objects are determined to be conforming.

Method of iterative segmentation of a digital picture

A method for fractal-darwinian object generation consists of the steps of: preparing a fractal object library including predetermined objects and associated rules of property and context, forming objects and comparing the formed objects with the objects in the fractal object library. By using the property rules, a local classification likelihood is allocated to each formed object. Thereupon, by using the context rules for each object, a respective fractal classification likelihood is formed. For optimisation of the fractal classification likelihood, alteration rules are applied to the objects. The above method is carried out iteratively, whereby a process of gradual optimisation takes place.

Fractal production of darwinian objects

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Neunkirchen, Germany

Peter Eschenbacher