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Parsing of random graphs for scene analysis

100%

Skomorowski M.

Machine Graphics and Vision

1998

tom Vol. 7, No. 1/2

313-323

Further results of research into parsing of random graphs for recognition of distored scenes ([9, 11]) are presented. An efficient top-down parallel parsing algorithm for analysis of distored scenes is proposed. The proposed approach involves parsing of graph grammars. To take into acount all variations of a distored scene under study, a probabilistic description of the scene is needed. The random graph approach ([9, 11]) is proposed here for such a description.

Image labelling by random graph parsing for syntactic scene description

100%

Skomorowski M.

Foundations of Computing and Decision Sciences

1998

tom Vol. 23, No. 3

161-178

A new approach to scene labelling is proposed. The proposed approach involves parsing for graph grammars. To take into account all variations of an ambiguous (distorted) scene under study, a probabilistic description of the scene is needed. Random graphs are proposed here for such a description. An efficient, O(n2), parsing algorithm for random graphs is proposed here as a tool for scene labelling. An example is provided.

A system for monitoring and analysis of vehicular traffic

63%

Markiewicz M. , Skomorowski M.

Archives of Transport System Telematics

2008

tom Vol. 1, iss. 1

39-41

The main purpose of the system described in this paper is optimization of traffic in the city by collecting and analyzing data related to traffic flow. Devices used in the process of collecting traffic information are installed in vehicles and send the information about current vehicle’s position and momentary speed. Based on that it is possible to find the best routes for any two points in the city.

Parsing of random graph languages for automated inspection in statisfical-based quality assurance systems

63%

Flasiński M. , Skomorowski M.

Machine Graphics and Vision

1998

tom Vol. 7, No. 3

565-623

Basic requirements for an automated visual inspection in intelligent SPC-oriented quality assurance systems are discussed. Extensions of feature IE-graphs representing solids in CAD ([62]) to a stochastic model of manufacturing processes are proposed. An efficient random graph language analysis based on parsable ETPL(k) graph grammars([55)] is presented as a tool for intelligent reasoning in high layer modules of automated inspection systems. The first applications of the model are shown.