Abstract

Various analytical or diagnostic techniques (e.g., evaluation of 2-D electrophoretograms, discrimination, and counting of objects) deal with pictures containing many similar patterns or objects and a few which are dissimilar. In this paper an efficient method is described which analyzes such pictures. It proceeds in three stages: removal of film background, separation into regions enclosing a single object or pattern, and classification of these regions into groups containing objects that are similar. The classification is based on moment analysis of the separated regions. The limitations due to noise (film granularity and fluctuations in object shape) on the selection of moment orders are discussed. Using selected integral functions (moments) and a corresponding choice of variables for the representation of 2-D space, adapted to the particular characteristics of the patterns to be classified, leads to a fast method for discriminating patterns that are difficult to distinguish by visual inspection because of their low texture content. The intended application of the method is automated data extraction from 2-D electrophoretograms. The performance of the procedure is shown in two examples.

© 1984 Optical Society of America

Full Article  |  PDF Article
OSA Recommended Articles
Polarization patterns of thick clouds: overcast skies have distribution of the angle of polarization similar to that of clear skies

Ramón Hegedüs, Susanne Åkesson, and Gábor Horváth
J. Opt. Soc. Am. A 24(8) 2347-2356 (2007)

Real-time 2D parallel windowed Fourier transform for fringe pattern analysis using Graphics Processing Unit

Wenjing Gao, Nguyen Thi Thanh Huyen, Ho Sy Loi, and Qian Kemao
Opt. Express 17(25) 23147-23152 (2009)

Self-Optimizing, Self-Learning System in Pictorial Pattern Recognition

P. H. Bartels and J. Bellamy
Appl. Opt. 9(11) 2453-2458 (1970)

References

You do not have subscription access to this journal. Citation lists with outbound citation links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Figures (11)

You do not have subscription access to this journal. Figure files are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Equations (19)

You do not have subscription access to this journal. Equations are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Metrics

You do not have subscription access to this journal. Article level metrics are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription