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Fundamental structures in the design of machine vision systems

Johnson, Jeffrey and Simon, Jean-Claude (2001). Fundamental structures in the design of machine vision systems. Mathematical Geology, 33(3) pp. 331–352.

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The chicken-and-egg problem of machine vision is that (a) abstracting objects requires images to be segmented, (b) segmentation requires greyscale statistics, (c) greyscale statistics are defined by segmented regions, and this leads back to (a). New image structures called gradient runs and gradient polygons are presented. These lead to new types of three-dimensional histograms. The histogram of a whole image mixes histograms from different regions within the image. These regions and their histograms may need separating. Global histograms can be used for preliminary segmentations. These then generate new histograms of the statistics for more coherent locally structured regions. These ideas have used to binarise documents for automated document reading systems, and they may be developed in the design of machine vision systems for other areas of application.

Item Type: Journal Article
ISSN: 0882-8121
Keywords: machine vision; abstracting objects; segmentation; greyscale statistics; histograms; gradient runs; gradient polygons, multilevel systems, hypernetworks
Academic Unit/Department: Mathematics, Computing and Technology > Engineering & Innovation
Mathematics, Computing and Technology
Item ID: 5084
Depositing User: Jeffrey Johnson
Date Deposited: 24 Jul 2006
Last Modified: 14 Jan 2016 16:12
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