{"title":"Petri-net based line extractor for binary images","authors":"D. H. Nabors, H. Ranganath","doi":"10.1109/SECON.1992.202379","DOIUrl":null,"url":null,"abstract":"A line extractor network for binary images is presented utilizing the basic principles of the Petri-net architecture as the framework. A brief introduction to the Petri-net formalism describes the terminology, structure, and execution control required for general network definition. The authors discuss the line extractor network in terms of basic Petri-net notation and identify the required extensions to the formalism contained within the proposed network. Simulation results for several input line patterns provide an indication of the network performance to identify lines of various lengths, widths, and orientations contained within a binary image. Remarks include a comparison with other line detection methods and hardware requirements for networks implementation.<<ETX>>","PeriodicalId":230446,"journal":{"name":"Proceedings IEEE Southeastcon '92","volume":"149 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1992-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings IEEE Southeastcon '92","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SECON.1992.202379","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
A line extractor network for binary images is presented utilizing the basic principles of the Petri-net architecture as the framework. A brief introduction to the Petri-net formalism describes the terminology, structure, and execution control required for general network definition. The authors discuss the line extractor network in terms of basic Petri-net notation and identify the required extensions to the formalism contained within the proposed network. Simulation results for several input line patterns provide an indication of the network performance to identify lines of various lengths, widths, and orientations contained within a binary image. Remarks include a comparison with other line detection methods and hardware requirements for networks implementation.<>
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