Polygonal object recognition

Ilan Schreiber*, Moshe Ben-Bassat

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

A technique for recognizing a 2-D unoccluded polygonal object by combining the alignment method with efficient string matching algorithms is presented. The approach is based on a single anchor point: the gravitation center of the contour of the object (GCC). The GCC is stable and insensitive to digitization errors, and it can always be found and calculated very efficiently. Additionally, it is universal and does not depend on the specific library of known objects. In this approach, most of the work is done in the preprocessing stage, when all of the library objects are transformed into a GCC canonical representation form. For the recognition stage, the canonical representation of the unknown object concatenated to itself is considered as a 'text' and the canonical representation of the known library objects as a set of 'words.' The recognition problem is then reduced to finding all of the occurrences of the 'words' in the 'text,' for which an efficient O(n) algorithm is introduced, where n is the order of the polygon being recognized. This approach was evaluated with several sets of objects from different fields, and very satisfactory results were obtained.

Original languageEnglish
Title of host publicationProceedings - International Conference on Pattern Recognition
PublisherPubl by IEEE
Pages852-859
Number of pages8
ISBN (Print)0818620625
StatePublished - 1990
EventProceedings of the 10th International Conference on Pattern Recognition - Atlantic City, NJ, USA
Duration: 16 Jun 199021 Jun 1990

Publication series

NameProceedings - International Conference on Pattern Recognition
Volume1

Conference

ConferenceProceedings of the 10th International Conference on Pattern Recognition
CityAtlantic City, NJ, USA
Period16/06/9021/06/90

Fingerprint

Dive into the research topics of 'Polygonal object recognition'. Together they form a unique fingerprint.

Cite this